United States Department of Agriculture datasets | Open Net Zero by Icebreaker One

Best Practices in the SNAP Employment and Training Program

This study is the second in a series of reviews of effective employment and training ET program components and practices. The study included a review of research focusing on SNAP ET and other public workforce programs published from 2016 to 2020. Particular attention was given to recent changes to the SNAP ET program, new referral and retention strategies, and promising work-based learning interventions, like apprenticeships.

1

2 years ago

Barriers that Constrain the Adequacy of Supplemental Nutrition Assistance Program (SNAP) Allotments

This study identifies the barriers that SNAP participants face when trying to achieve a healthy diet through a nationally representative survey of SNAP participants. The study identifies the individual, household, and environmental barriers faced by SNAP participants that prevent them from having access to a healthy diet throughout the month; describes the interaction between these barriers; describes the nature of the barriers and the coping strategies used; and identifies any associations with household food insecurity.

1

2 years ago

USFS Cartographic 2011 Tree Canopy Cover Puerto Rico Virgin Islands (Map Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Hawaii TCC 2011 cartographic dataset is comprised of a single layer. The pixel values range from 0 to 99 percent. The background is represented by the value 255. The dataset has data gaps due to consistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 110.

3

2 years ago

USFS Cartographic 2011 Tree Canopy Cover Coastal AK (Map Service)

The National Land Cover Database 2011 (NLCD2011) percent tree canopy cover (TCC 2011) layer was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management, NASA, and the U.S. Army Corps of Engineers. One of the primary goals of the project was to generate a current, consistent, and seamless national land cover, percent tree canopy cover, and percent impervious cover at medium spatial resolution. TCC 2011 is the NLCD tree canopy cover dataset at medium spatial resolution (30 m). It was produced by the USDA Forest Service Remote Sensing Applications Center (RSAC). The TCC 2011 dataset has two layers: percent tree canopy cover and standard error. For the tree canopy cover layer, the pixel values range from 0 to 100 percent. For the standard error layer, the pixel values range from 0 to 45 percent. The standard error represents the model uncertainty associated with the corresponding pixel in the tree canopy cover layer. The tree canopy cover layer was produced using a Random Forests' regression algorithm and the standard error layer was calculated from the variance of the canopy cover estimates from the random forest regression trees.

3

2 years ago

USFS Analytical 2016 Tree Canopy Cover Standard Error Puerto Rico Virgin Islands (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

USFS Analytical 2016 Tree Canopy Cover Standard Error Coastal AK (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe Coastal Alaska TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and line up. The USFS's GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/and download the FS-Cartographic version of the 2011 and/or 2016 datasets for their cartographic applications.

3

2 years ago

Terrestrial Condition Assessment (TCA) Feral Pig Density (Map Service)

Data are derived from generalized linear models and model selection techniques using 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents. Models were used to determine the strength of association among a diverse set of biotic and abiotic factors associated with wild pig population dynamics. The models and associated factors were used to predict the potential population density of wild pigs at the 1 km resolution. Predictions were then compared with available population estimates for wild pigs on their native range in North America indicating the predicted densities are within observed values. See Lewis et al (2017) and Lewis et al (2019) for more information.Lewis, Jesse S., Matthew L. Farnsworth, Chris L. Burdett, David M. Theobald, Miranda Gray, and Ryan S. Miller. 'Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal.' Scientific reports7 (2017): 44152.Lewis, Jesse S., Joseph L. Corn, John J. Mayer, Thomas R. Jordan, Matthew L. Farnsworth, Christopher L. Burdett, Kurt C. VerCauteren, Steven J. Sweeney, and Ryan S. Miller. 'Historical, current, and potential population size estimates of invasive wild pigs (Sus scrofa) in the United States.' Biological Invasions21, no. 7 (2019): 2373-2384.

3

2 years ago

USFS Analytical 2016 Tree Canopy Cover Puerto Rico Virgin Islands (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Basal Area Percent Change (Map Service)

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ('initial assessments'). Late-season fires, however, may be deferred until the following spring or summer ('extended assessments'). National mosaics of each thematic product are prepared annually. Mosaics of extended assessments, if any, are provided separately from initial assessment mosaics. This map service includes annual raster mosaics of published BA-7 datasets for fires that burned during calendar years 2013 through 2020, excluding 2020 extended assessments. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php).

3

2 years ago

Percent change in annual precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metadata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Percent change in April 1 snow water equivalent (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Payments in Lieu of Taxes (PILT) and All Service Receipts (ASR) (Feature Layer)

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. This data is intended for read-only use. Payments In Lieu of Taxes (PILT) and All Service Receipts (ASR) are combined into a base layer that is used in Forest Service business functions, as well as by other entities such as states and counties. This layer depicts Forest Service lands that qualify for PILT and/or ASR. Payments in Lieu of Taxes are Federal payments to local governments that help offset losses in property taxes due to the existence of nontaxable Federal lands within their boundaries. All Service Receipts data provides acreage inputs to the FS All Service Receipts program that tracks receipt data by unit and computes revenue sharing payments to states and counties. Please note, the publication of this dataset in EDW replaces the file geodatabase on the Public Lands and Realty Management website. Metadata and Downloads.

9

2 years ago

PADUS FS Scenic and Conservation Easement Areas (Feature Layer)

An area depicting a type of special use authorization (usually granted for linear rights-of-way) that is utilized in those situations where a conveyance of a limited and transferable interest in NFS land is necessary or desirable to serve or facilitate authorized long-term uses, and that may be compensable according to its terms. This data is intended for read-only use. The PAD-US feature classes were developed by the Forest Service for submission to the Protected Areas Database of the United States (PAD-US). It is the official inventory of public parks and other protected open space. With more than 3 billion acres in 150,000 holdings, the spatial data in PAD-US represents public lands held in trust by thousands of national, State and regional/local governments, as well as non-profit conservation organizations. PAD-US is published by the U.S. Geological Survey Gap Analysis Program (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity, conservation, recreation, public health, climate change adaptation, and infrastructure investment.

7

2 years ago

PADUS FS National Designated Areas (Feature Layer)

An area depicting designated land boundaries, excluding boundaries designated by proclamation. This data is intended for read-only use. The PAD-US feature classes were developed by the Forest Service for submission to the Protected Areas Database of the United States (PAD-US). It is the official inventory of public parks and other protected open space. With more than 3 billion acres in 150,000 holdings, the spatial data in PAD-US represents public lands held in trust by thousands of national, State and regional/local governments, as well as non-profit conservation organizations. PAD-US is published by the U.S. Geological Survey Gap Analysis Program (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity, conservation, recreation, public health, climate change adaptation, and infrastructure investment. Metadata

7

2 years ago

Next Generation Fire Severity Mapping (Image Service)

The geospatial products described and distributed here depict the probability of high-severity fire, if a fire were to occur, for several ecoregions in the contiguous western US. The ecological effects of wildland fire � also termed the fire severity � are often highly heterogeneous in space and time. This heterogeneity is a result of spatial variability in factors such as fuel, topography, and climate (e.g. mean annual temperature). However, temporally variable factors such as daily weather and climatic extremes (e.g. an unusually warm year) also may play a key role. Scientists from the US Forest Service Rocky Mountain Research Station and the University of Montana conducted a study in which observed data were used to produce statistical models describing the probability of high severity fire as a function of fuel, topography, climate, and fire weather. Observed data from over 2000 fires (from 2002-2015) were used to build individual models for each of 19 ecoregions in the contiguous US (see Parks et al. 2018, Figure 1). High severity fire was measured using a fire severity metric termed the relativized burn ratio, which uses pre- and post-fire Landsat imagery to measure fire-induced ecological change. Fuel included pre-fire metrics of live fuel amount such as NDVI. Topography included factors such as slope and potential solar radiation. Climate summarized 30-year averages of factors such as mean summer temperature that spatially vary across the study area. Lastly, fire weather incorporated temporally variable factors such as daily and annual temperature. In turn, these statistical models were used to generate 'wall-to-wall' maps depicting the probability of high severity fire, if a fire were to occur, for 13 of the 19 ecoregions. Maps were not produced for ecoregions in which model quality was deemed inadequate. All maps use fuel data representing the year 2016 and therefore provide a fairly up-to-date assessment of the potential for high severity fire. For those ecoregions in which the relative influence of fire weather was fairly strong (n=6), two additional maps were produced, one depicting the probability of high severity fire under moderate weather and the other under extreme weather. An important consideration is that only pixels defined as forest were used to build the models; consequently maps exclude pixels considered non-forest.

3

2 years ago

National Wild and Scenic Rivers: Legal Status (Feature Layer)

The Land Status view of a Wild and Scenic River. Areas designated by Congress as part of the National Wild and Scenic River System, with related details including the date of the designation, status of the final boundary description, authority, and land status case and document information. Metadata

7

2 years ago

National Wild and Scenic Rivers (Feature Layer)

A depiction of areas designated as Wild and Scenic Rivers. Metadata

7

2 years ago

National Insect and Disease Risk Maps - 2012

FHAAST has completed the 2013 - 2027 National Insect and Disease Risk Map (2012 NIDRM); a nationwide strategic assessment and database of the potential hazard for tree mortality due to major forest insects and diseases. The goal of NIDRM is to summarize landscape-level patterns of potential insect and disease activity, consistent with the philosophy that science-based, transparent methods should be used to allocate pest-management resources across geographic regions and individual pest distributions. In other words: prioritize investment for areas where both hazard is significant and effective treatment can be efficiently implemented.

3

2 years ago

National Forest Lands with Nationally Designated Management or Use Limitations (Feature Layer)

An area depicting National Forest System land parcels that have management or use limits placed on them by legal authority. Examples are: National Recreation Area, National Monument, and National Game Refuge. Metadata

7

2 years ago

Mean Fraction of Annual Runoff from Forest Service Lands (Map Service)

Available water supply varies greatly across the United States depending on topography, climate, elevation and geology. Forested and mountainous locations, such as national forests, tend to receive more precipitation than adjacent non-forested or low-lying areas. However, contributions of national forest lands to regional streamflow volumes is largely unknown. Using outputs from the Variable Infiltration Capacity hydrologic model, we calculated mean annual and mean summer (July and August) streamflow metrics based on total flow and flow from national forest lands for each 1:100,000 scale National Hydrography Dataset stream reach in the contiguous United States. Specifically, this data publication contains twenty-one comma-delimited ASCII text files (for different drainage areas and processing units across the United States) containing 1915-2011 mean annual flow and mean summer flow.Data can be downloaded here: Geodatabase or ShapefileThese files also contain the mean annual and mean summer flows from National Forest System (NFS) lands as well as the portion of total mean annual and summer flow contributed by flow from NFS lands.These data provide insight into 1915-2011 hydrologic regimes and national forest contributions to total water yield. These non-spatial files were then merged and joined to the September 2012 snapshot of the National Hydrography Dataset (NHD), version 2.Note: 'Forest Service lands' are here defined as those lands within the Forest Service administrative boundaries; these include some inholdings and other non-USFS lands enclosed within these boundaries.

3

2 years ago

MTBS Wildfire Burned Area Boundaries

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period between 1984 and the current MTBS release. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector polygon of the location of all currently inventoried and mappable MTBS fires occurring between calendar year 1984 and the current MTBS release for the continental United States, Alaska, Hawaii and Puerto Rico. Map Service Feature Layer

3

2 years ago

Landscape Change Monitoring System Conterminous United States Year of Highest Probability of Gain (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Landscape Change Monitoring System Conterminous United States Most Recent Year of Slow Loss (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Landscape Change Monitoring System Conterminous United States Most Recent Year of Fast Loss (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.�These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.�

3

2 years ago

Landscape Change Monitoring System Conterminous United States Land Cover (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Historical winter temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical winter precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Great Basin Montane Watersheds - Furthest Head (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Drought and Moisture Surplus for the Conterminous United States, Annual Data 5-Year Windows (Image Service)

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option; the data can also be downloaded directly from the FSGeodata Clearinghouse. To summarize this dataset by U.S. Forest Service Lands, see the Drought Summary Tool. You can also explore cumulative drought and moisture changes from this StoryMap; additional drought products from the Office of Sustainability and Climate are available in our Climate Gallery and the OSC Drought page.The Moisture Deficit and Surplus map uses moisture difference z-score datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station. A z-score is a statistical method for assessing how different a value is from the mean (average). Mean moisture values were derived from historical data on precipitation and potential evapotranspiration, from 1900 to 2022. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits or surpluses. Thus, the dark red areas on this map indicate a five-year period with extremely dry conditions, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361

3

2 years ago

Drought and Moisture Surplus for the Conterminous United States, Annual Data 1-Year Windows (Image Service)

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option; the data can also be downloaded directly from the FSGeodata Clearinghouse. To summarize this dataset by U.S. Forest Service Lands, see the Drought Summary Tool. You can also explore cumulative drought and moisture changes from this StoryMap; additional drought products from the Office of Sustainability and Climate are available in our Climate Gallery and the OSC Drought page.The Moisture Deficit and Surplus map uses moisture difference z-score datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station. A z-score is a statistical method for assessing how different a value is from the mean (average). Mean moisture values were derived from historical data on precipitation and potential evapotranspiration, from 1900 to 2022. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits or surpluses. Thus, the dark red areas on this map indicate a one-year period with extremely dry conditions, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361

3

2 years ago

Characteristics of masticated particles in mixed-conifer forests of the western United States: Experimental burns and smoldering tests

This data publication contains the results from 45 experimental burns and 48 smoldering tests on masticated materials from mixed-conifer forests. These data were collected from 15 study locations from 2012 through 2016 as part of the MASTIDON project. The MASTIDON project was a four-year study to describe the phyical characteristics of masticated materials that were treated with four different cutting heads in xeric and mesic environments. The main focus of the project was to evaluate how leaving the particles on the ground for varying lengths of time affected the burnability of the particles. The project was funded by the Joint Fire Sciences Program (JFSP) and RMRS between 2013 and 2016. The masticated particles were created by four different machines, including a vertical rotating head, horizontal drum, chipper, and mower. They had been decomposing in situ in wet and dry areas of Idaho, and dry areas of Colorado, New Mexico, and South Dakota since their initial treatment and were between 0 and 10 years old. The materials were burned at the RMRS Missoula Fire Sciences lab, Missoula, MT. The experimental burns were conducted in a combustion facility on a large fuel bed 0.68 square meters in size. The smoldering tests were conducted on beds 497 square centimeters in size under a fume hood in the soils laboratory. This download includes (1) data on fire behavior within the experimental burns, including rate of spread, flame height, flame duration, consumption, heat flux, moisture content, and more; (2) temperature data, burn durations, duff moistures and thicknesses from the smoldering tests; (3) photos of the experimental burn beds and smoldering beds; and (4) files describing the MASTIDON project and its goals.

3

2 years ago

National School Lunch Program Direct Certification Improvement Study

This study describes current methods of direct certification used by State and local agencies and the challenges that State and local education agencies face in attaining high matching rates. The study presents the analysis of unmatched records in order to provide a better understanding of the categorically eligible children who are not matched in the direct certification process and identifies potential matching process improvements that might increase the number of matched children.

1

2 years ago

MyPyramid Food Raw Data

MyPyramid Food Data provides information on the total calories; calories from solid fats, added sugars, and alcohol (extras); MyPyramid food group and subgroup amounts; and saturated fat content of over 1,000 commonly eaten foods with corresponding commonly used portion amounts. This information is key to help consumers meet the recommendations of the Dietary Guidelines for Americans and manage their weight by understanding how many calories are consumed from "extras." CNPP has created an interactive tool from this data set available on the web at MyFood-a-pedia.gov. A mobile version is coming soon to provide consumers with assistance on-the-go.

1

2 years ago

Measuring the Effect of SNAP Participation on Food Security

SNAP is designed to reduce food insecurity – reduced food intake or disrupted eating patterns in a household due to lack of money or other resources – but data quantifying this effect is limited. The objectives of this study were to: Assess how food security and food expenditures vary with SNAP participation. Examine how relationships between SNAP and food security and between SNAP and food expenditures vary by household characteristics and circumstances. Estimating the effect of SNAP on food insecurity using household survey data is challenging because households that choose to participate in SNAP can differ in systematic ways from households that do not participate, making it hard to distinguish the impact of SNAP from these other factors. This study sought to control for the SNAP participation “selection bias” by comparing information collected from households within days of entering the program (new entrants) to information obtained after about 6 months of participation.

1

2 years ago

Information and Guidance on the Domestic Beef Market

This memorandum provides information and guidance to State agencies and School Food Authorities (SFAs) on the current status of the domestic beef market. Despite an increase in wholesale prices for ground beef, the USDA is continually encouraging schools to purchase and use beef in their menus as a good source of lean protein. The USDA is also offering guidance on ways that schools can ensure the resources needed to provide students with healthy, balanced meals.

1

2 years ago

Healthy Access Locator

Healthy Access Locator map can be used to view schools that have received a Healthier US Schools Challenge Award (HUSSC). To get started, click on Search Awards and enter your search criteria. When the information displays on the map, you can click a star for additional details or see a summary of your results below the map. You can also use the Data Layers feature to display different data layers on obesity and diabetes prevalence, SNAP Participation Rates, and SNAP Outreach Plans by states. (HUSSC Awards as of May 30, 2013).

1

2 years ago

Guidance for Management Plans and Budgets - A Child and Adult Care Food Program Handbook

This handbook specifically focuses on plans for institutions (independent centers and sponsoring organizations) to conduct organized and fiscally responsible operations of the CACFP management plans outline the institution’s policies and procedures for administering and monitoring its own operations and those of its sponsored facilities. Budgets outline the use of CACFP and other funds for meeting Program requirements. FNS recognizes that maintaining a high-quality CACFP requires a commitment to excellence on the part of institutions and caregivers. We applaud the efforts of the many dedicated persons who ensure that the participating children and adults are served wholesome, attractive, and nutritious meals in a sociable environment while meeting the requirements for federal assistance.

1

2 years ago

Food Distribution Program on Indian Reservations Program Fact Sheet

This fact sheet describes what FDPIR is, who is eligible for the program, and what foods are available through the program. The fact sheet also provides the number of participants, information about health and nutrition, and resources for supplemental information.

1

2 years ago

Find Summer Meals in Your Community

Nutritious free meals are available for children and teens 18 and younger at many locations throughout the nation throughout the summer while school is out of session. This mapping tool helps to find a site near you.

2

2 years ago

Farm to School Census

The Healthy, Hunger-Free Kids Act of 2010 (HHFKA) formally established a Farm to School Program within USDA to improve access to local foods in schools. In order to establish realistic goals with regard to increasing the availability of local foods in schools, in 2013, USDA conducted the first nationwide Farm to School Census (the Census). In 2015, USDA conducted a second Farm to School Census to measure progress towards reaching this goal.

1

2 years ago

FNS USDA Food Programs Disaster Manual

This manual contains important information for persons in Food and Nutrition Service (FNS) Headquarters, FNS Regional Offices, and Distributing Agencies (DA), which include State Distributing Agencies, and Indian Tribal Organizations that are charged with the responsibility of providing USDA Foods (formerly known as USDA commodities or donated food) to disaster relief organizations in the event of a disaster, emergency, or situation of distress.

1

2 years ago

Cost of Living Adjustment (COLA) Information

We adjust SNAP maximum allotments, deductions, and income eligibility standards at the beginning of each Federal fiscal year. The changes are based on changes in the cost of living. COLAs take effect on October 1 each year. Maximum allotments are calculated from the cost of a market basket based on the Thrifty Food Plan for a family of four, priced in June that year. The maximum allotments for households larger and smaller than four persons are determined using formulas that account for economies of scale. Smaller households get slightly more per person than the four-person household. Larger households get slightly less. Income eligibility standards are set by law. Gross monthly income limits are set at 130 percent of the poverty level for the household size. Net monthly income limits are set at 100 percent of poverty.

1

2 years ago

Daily Food Plan for Preschoolers

A daily food plan shows what and how much your child should eat to meet his or her needs. You can create an eating plan for your preschooler using the SuperTracker's MyPlan. You will be asked to create a profile using your child’s information. You can register to save the profile if you want to. Use the Plan as a general guide to help you feed your child. It will show what and how much to offer your child to meet his or her needs.

1

2 years ago

Cost of Food Distribution Programs

Provide cost of Nutrition Services Incentive Program (NSIP--formerly Nutrition Program for the Elderly), Food Distribution on Indian Reservations (FDPIR), Commodity Supplemental Food (CSFP), Emergency Food Assistance (TEFAP) programs.

1

2 years ago

Child and Adult Care Food Program (CACFP): Assessment of Sponsor Tiering Determinations

The (CACFP) provides reimbursements for nutritious meals and snacks served in family day care homes, child care centers, and other participating facilities and programs. This assessment examines the accuracy of the classification of Family Day Care Homes (FDCHs) participating in the U.S. Department of Agriculture's (USDA) Child and Adult Care Food Program. The assessment provides estimates of the number of FDCHs misclassified by sponsoring agencies into the wrong tier and the resulting erroneous payments for meals and snacks reimbursed at the wrong rate for program year 2013.

1

2 years ago

Amounts of 2016 Dairy Products Available for Reallocation, by Commodity and Country

Amounts of 2016 Dairy Products Available for Reallocation, by Commodity and Country, as of October 1, 2016

1

2 years ago

USDA Rural Development Single Family Section 502 Direct Active Loans by County

Active loan characteristics aggregated at the county level of geography, including number of loans, average loan amount, average loan amount by 5 year ranges, number of loans to Section 523 Mutual Self Help Housing program participants, and number of leveraged loans.

2

2 years ago

USDA Rural Development Multi Family Housing - May 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for May 2018.

1

2 years ago

USDA Rural Development Multi Family Housing - May 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for May 2016.

1

2 years ago

Global Agricultural Trade System

Trade data from US Census Bureau as defined by USDA Foreign Agricultural Service groupings

1

2 years ago

USDA Rural Development Resale Properties - Real Estate Owned

Data provides current information regarding single family homes, and ranches for sale by the U.S. Federal Government. These previously owned properties are for sale by public auction or other method depending on the property.

2

2 years ago

USDA Rural Development Multi Family Housing - October 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for October 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - November 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for November 2017.

1

2 years ago

U.S. General Soil Map (STATSGO2)

This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and non-soil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units and merged into a seamless national dataset. The soil map units are linked to attributes in the National Soil Information system relational database, which gives the proportionate extent of the component soils and their properties. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

4

2 years ago

Soil Survey Geographic Database (SSURGO)

This dataset is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This dataset consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey.

4

2 years ago

National and State Level Estimates of WIC Eligibility and Program Reach in 2019

This report, the latest in a series of annual reports on WIC eligibility, presents 2019 national and state estimates of the number of people eligible for WIC benefits and the percents of the eligible population and the US population covered by the program, including estimates by participant category. The report also provides estimates by region, state, U.S. territory and race and ethnicity.

1

2 years ago

FSIS - Summary of Recall Cases in Calendar Year

This datasets summarizes and lists all the recalls of meat and poultry products produced by FSIS federally inspected establishments for the calendar year. Recalls are characterized by date, recall class, product, reason and pounds recalled. More detailed information can be found in each recall announcement posted on the FSIS website.

1

2 years ago

Major Land Resource Areas (MLRA)

The MLRA Geographic Database was prepared for Agriculture Handbook 296 re-publication and is used to support decisions about regional and national agricultural issues. The database and AH 296 help to identify the need for research and resource inventories. The handbook serves as the vehicle for extrapolating the results of research across political boundaries and is the basis for organizing and operating natural resource conservation programs. Today, USDA soil survey offices are organized to serve groups of the major land resource areas defined in this handbook. The handbook was first published in 1965 as an expansion of the 1950 map entitled “Problem Areas in Soil Conservation”, and was designed primarily for use by the Soil Conservation Service. The handbook was updated in 1978, and the second edition was printed in 1981. The third edition was published in 2006. The 2022 publication is the fourth edition.

3

2 years ago

Gridded Soil Survey Geographic (gSSURGO-30) Database for the Conterminous United States - 30 meter

This dataset is called the Gridded SSURGO (gSSURGO) Database and is derived from the Soil Survey Geographic (SSURGO) Database. SSURGO is generally the most detailed level of soil geographic data developed by the National Cooperative Soil Survey (NCSS) in accordance with NCSS mapping standards. The tabular data represent the soil attributes, and are derived from properties and characteristics stored in the National Soil Information System (NASIS). The gSSURGO data were prepared by merging traditional SSURGO digital vector map and tabular data into a Conterminous US-wide extent, and adding a Conterminous US-wide gridded map layer derived from the vector, plus a new value added look up (valu) table containing "ready to map" attributes. The gridded map layer is offered in an ArcGIS file geodatabase raster format. The raster and vector map data have a Conterminous US-wide extent. The raster map data have a 30 meter cell size. Each cell (and polygon) is linked to a map unit identifier called the map unit key. A unique map unit key is used to link to raster cells and polygons to attribute tables, including the new value added look up (valu) table that contains additional derived data. The value added look up (valu) table contains attribute data summarized to the map unit level using best practice generalization methods intended to meet the needs of most users. The generalization methods include map unit component weighted averages and percent of the map unit meeting a given criteria. The Gridded SSURGO dataset was created for use in national, regional, and state-wide resource planning and analysis of soils data. The raster map layer data can be readily combined with other national, regional, and local raster layers, e.g., National Land Cover Database (NLCD), the National Agricultural Statistics Service (NASS) Crop Data Layer, or the National Elevation Dataset (NED).

2

2 years ago

FSIS - Salmonella PFGE Reports

Pulse-field gel electrophoresis (PFGE) Salmonella reports for FSIS Raw Products from fiscal year (FY) 2016 to FY2019. See FSIS website for additional information.

1

2 years ago

FSIS - Sampling Prevalence Report

FSIS calculates prevalence, volume weighted percent positive, or percent positive calculations for microbial pathogens in FSIS regulated products that are currently sampled through existing sampling projects. FSIS intends to provide new calculations each quarter using the prior 12 months of sampling data. See the FSIS website for additional information.

1

2 years ago

FSIS - Raw Beef Sampling Data

Establishment specific sampling results for Raw Beef sampling projects. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

FSIS - Ready-to-Eat Meat and Poultry Sampling Data

Establishment specific sampling results for FSIS Ready-to-Eat (RTE) sampling projects. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

FSIS - Raw Poultry Sampling Data

Establishment specific sampling results for Raw Poultry sampling projects. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

FSIS - Raw Beef Products PFGE

Pulse-field gel electrophoresis (PFGE) E.Coli data for FSIS Raw Beef Products from fiscal year (FY) 2016 to FY2019. See the FSIS website for additional information.

1

2 years ago

FSIS - Import Refusals

Import refusals for products regulated by FSIS. Current files are updated monthly; archive files are updated quarterly. See the FSIS website for additional information.

3

2 years ago

FSIS - Raw Beef Products Quarterly Sampling Report

Quarterly sampling report for FSIS Raw Beef Products. The report will provide data on the percent positive of E.Coli (O157:H7 and non-O157 Shiga-toxin producing) in FSIS Raw Beef Products. See the FSIS website for additional information.

1

2 years ago

FSIS - Campylobacter PFGE Reports

Pulse-field gel electrophoresis (PFGE) Campylobacter reports for FSIS Raw Products from fiscal year (FY) 2016 to FY2019. See FSIS website for additional information.

1

2 years ago

FSIS - FoodKeeper Data

FSIS’ FoodKeeper application educates users about food and beverages storage to help them maximize the freshness and quality of these items. By helping users understand food storage, the application empowers consumers to choose storage methods that extend the shelf life of their items. By doing so users will be able to keep items fresh longer than if they were not stored properly.

6

2 years ago

Evaluation of the FY 2016 Team Nutrition Training Grants

The U.S. Department of Agriculture's (USDA) Food and Nutrition Service (FNS) Team Nutrition Training Grant (TNTG) program began in 1995 to assist students participating in the National School Lunch Program (NLSP) and School Breakfast Program (SBP) in making healthy food choices and to improve the nutritional content of meals and snacks served by programs receiving Child and Adult Care Food Program (CACFP) funding. The grant funding provided to state agencies in support of these programs is intended to be used for training, technical assistance, and nutrition education to assist schools, child care settings, summer meal sites, parents or caregivers, and children to align with the Dietary Guidelines for Americans.

1

2 years ago

Ecosystem Dynamics Interpretive Tool (EDIT)

The Ecosystem Dynamics Interpretive Tool was developed to offer natural resource professionals, scientists and others a standard framework for cataloging information about how ecosystems respond to different land uses, management practices, and natural phenomena. EDIT now serves as the primary repository of Ecological Site information produced by the USDA Natural Resources Conservation Service (NRCS). The framework is also being used to support the development of other ecological land classifications in the U.S. and elsewhere.

2

2 years ago

Direct Certification in the NSL Program State Implementation Report to Congress SY17-18 and SY18-19

This report responds to the requirement of PL 110-246 to assess the effectiveness of state and local efforts to directly certify children for free school meals. Direct certification is a process conducted by the states and by local educational agencies (LEAs) to certify eligible children for free meals without the need for household applications. The 2004 Child Nutrition and WIC Reauthorization Act (PL 108-265) required LEAs to establish systems to directly certify children from households that receive Supplemental Nutrition Assistance Program (SNAP) benefits by school year (SY) 2008-2009. This report presents information on the outcomes of direct certification for SY 2017-2018 and SY 2018-2019.

1

2 years ago

TreeMap 2016 Volume Standing Dead (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Volume Live (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Trees Per Acre Dead (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Tree Bio Mass Live (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Stand Size Code Field (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Stand Quadratic Mean Diameter (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Live Tree Stocking (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

Timber Appraisal Zones - Ranger Districts (Feature Layer)

Features in the TAZ ranger districts dataset represent individual USFS Ranger Districts or USFS Administrative Forests Boundaries which compose a stumpage market. The schema of this dataset is a copy of S_USA.RangerDistrict (https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.RangerDistrict.xml) with two additional fields: APPRAISALZONES_FK supports a one-to-many relationship between S_USA.Com_TimberAppraisalZone and S_USA.Com_TimberAppraisalZone_RDs COMMENTS contains information about individual stumpage market. TAZ Ranger Districts Metadata

7

2 years ago

Terrestrial Condition Assessment (TCA) Wildfire Potential Moderate Fire Regime 1 and 2 (Map Service)

The percent area of a landscape analysis unit where the Wildland Fire Hazard 2020 class is High and the LANDFIRE Fire Regime is Moderate.

3

2 years ago

Terrestrial Condition Assessment (TCA) Uncharacteristic Fire Severity Moderate (Map Service)

The LANDFIRE Percent Mixed-Severity Fire (PMS) raster dataset (LF US_120_PMS) was combined with the Monitoring Trends in Burn Severity (MTBS) data (1984-2017) to identify areas that have experienced unnaturally severe wildfires in the recent past (1984-2017). Areas mapped are greater than 50% Mixed-Severity Fire and a high severity fire MTBS mapped fire at the same location.

3

2 years ago

USFS Cartographic 2016 Tree Canopy Cover Puerto Rico Virgin Islands (Map Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Hawaii TCC 2011 cartographic dataset is comprised of a single layer. The pixel values range from 0 to 99 percent. The background is represented by the value 255. The dataset has data gaps due to consistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 110.

3

2 years ago

USFS Cartographic 2016 Tree Canopy Cover Coastal AK (Map Service)

The National Land Cover Database 2016 (NLCD2016) percent tree canopy cover (TCC 2016) layer was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management, NASA, and the U.S. Army Corps of Engineers. One of the primary goals of the project was to generate a current, consistent, and seamless national land cover, percent tree canopy cover, and percent impervious cover at medium spatial resolution. TCC 2016 is the NLCD tree canopy cover dataset at medium spatial resolution (30 m). It was produced by the USDA Forest Service Remote Sensing Applications Center (RSAC). The TCC 2016 dataset has two layers: percent tree canopy cover and standard error. For the tree canopy cover layer, the pixel values range from 0 to 100 percent. For the standard error layer, the pixel values range from 0 to 45 percent. The standard error represents the model uncertainty associated with the corresponding pixel in the tree canopy cover layer. The tree canopy cover layer was produced using a Random Forests' regression algorithm and the standard error layer was calculated from the variance of the canopy cover estimates from the random forest regression trees.

3

2 years ago

USFS Cartographic 2016 Tree Canopy Cover Hawaii (Map Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Hawaii TCC 2011 cartographic dataset is comprised of a single layer. The pixel values range from 0 to 99 percent. The background is represented by the value 255. The dataset has data gaps due to consistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 110.

3

2 years ago

USFS Cartographic 2016 Tree Canopy Cover CONUS (Map Service)

The National Land Cover Database 2016 (NLCD2016) percent tree canopy cover (TCC 2016) layer was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management, NASA, and the U.S. Army Corps of Engineers. One of the primary goals of the project was to generate a current, consistent, and seamless national land cover, percent tree canopy cover, and percent impervious cover at medium spatial resolution. TCC 2016 is the NLCD tree canopy cover dataset at medium spatial resolution (30 m). It was produced by the USDA Forest Service Remote Sensing Applications Center (RSAC). The TCC 2016 dataset has two layers: percent tree canopy cover and standard error. For the tree canopy cover layer, the pixel values range from 0 to 100 percent. For the standard error layer, the pixel values range from 0 to 45 percent. The standard error represents the model uncertainty associated with the corresponding pixel in the tree canopy cover layer. The tree canopy cover layer was produced using a Random Forests' regression algorithm and the standard error layer was calculated from the variance of the canopy cover estimates from the random forest regression trees.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Climate Exposure Temperature Fall (Map Service)

The difference in Fall temperature (F) between the reference time period of 1980-2014 and the current time period 2015-2019. Fall months include September, October, and November. Data used are sourced from DAYMET, Daily Surface Weather and Climatological Summaries, Oak Ridge National Laboratory. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Surface Ownership Parcels, detailed (Feature Layer)

An area depicting ownership parcels of the surface estate. Each surface ownership parcel is tied to a particular legal transaction. The same individual or organization may currently own many parcels that may or may not have been acquired through the same legal transaction. Therefore, they are captured as separate entities rather than merged together. This is in contrast to Basic Ownership, in which the surface ownership parcels having the same owner are merged together. Basic Ownership provides the general user with the Forest Service versus non-Forest Service view of land ownership within National Forest boundaries. Surface Ownership provides the land status user with a current snapshot of ownership within National Forest boundaries. Metadata

7

2 years ago

Surface Drinking Water Importance - Forests on the Edge (Feature Layer)

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis'and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

9

2 years ago

Survey Boundaries maintained by the US Forest Service (Feature Layer)

The purpose of this featureclass is to allow national forest system boundary managers to query and report on the status of these boundaries for planning boundary management and maintenance work, and to provide this information to anyone else needing this information for analysis, querying, reporting, mapping. The lines should indicate the current status of the physical marked and posted lines in the field, and their maintenance status. Surface Management Agency (SMA) lines represent the surveyed boundary lines for which the Forest Service is responsible for marking and posting. These include the boundaries between NFS lands and non-NFS lands and the boundaries of congressionally designated areas such as National Wilderness. Metadata

7

2 years ago

Silviculture Timber Stand Improvement Needs (Feature Layer)

The SilvTSI (Silviculture Timber Stand Improvement) feature class represents activities associated with the following performance measure: Forest Vegetation Improved (Release, Weeding, and Cleaning, Precommercial Thinning, Pruning and Fertilization). The Activities data set portrays the areas where activities are accomplished as a part of the silviculture program of work, funded through the budget allocation process and reported through the Forest Service Activity Tracking System (FACTS) database within the Natural Resource Manager (NRM) suite of applications. The activities are part of the Performance Measures used to rate Agency performance in meeting the Department's Strategic Goals. It is important to note that this layer may not contain all accomplished activities; the spatial portion of the activity description is not currently enforced by FACTS and at this time some are optionally reported by Forest Service units. As spatial data reporting is enforced by the application and acceptance of reporting increases for both tabular and spatial we hope to improve the quality and comprehensiveness of the data used for this layer in coming years. Metadata and Downloads.

7

2 years ago

Silviculture Reforestation Needs (Feature Layer)

The Silviculture Reforestation feature class represents activities associated with the following performance measure: Forest Vegetation Establishment (Planting, Seeding, Site Preparation for Natural Regeneration and Certification of Natural Regeneration without Site Preparation). The Activities data set portrays the areas where activities are accomplished as a part of the silviculture program of work, funded through the budget allocation process and reported through the Forest Service Activity Tracking System (FACTS) database within the Natural Resource Manager (NRM) suite of applications. The activities are part of the Performance Measures used to rate Agency performance in meeting the Department's Strategic Goals. It is important to note that this layer may not contain all accomplished activities; the spatial portion of the activity description is not currently enforced by FACTS and at this time some are optionally reported by Forest Service units. As spatial data reporting is enforced by the application and acceptance of reporting increases for both tabular and spatial we hope to improve the quality and comprehensiveness of the data used for this layer in coming years. Metadata and Downloads.

7

2 years ago

Roadless Areas: Idaho Roadless Rule (Feature Layer)

This feature class describes the boundaries of all Roadless Areas managed by the US Forest Service in Idaho. These roadless areas were designated administrative rulemaking to provide management direction for their conservation and management. The Roadless Area Conservation Rule of 2008 designated roadless areas nationwide. Metadata and Downloads

7

2 years ago

Right of Way (Feature Layer)

An area depicting a privilege to pass over the land of another in some particular path; usually an easement over the land of another; a strip of land used in this way for railroad and highway purposes, for pipelines or pole lines and for private and public passage. Metadata

7

2 years ago

USFS Analytical 2011 Tree Canopy Cover Coastal AK (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe Coastal Alaska TCC 2011 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. The dataset has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 127.

3

2 years ago

US Forest Atlas FIA Modeled Abundance, Forest-type Groups, Harvest and Carbon (Rest Services Directory)

FIA Modeled Abundance:�This dataset portrays the live tree mean basal area (square feet per acre) of the species across the contiguous United States. The underlying data publication contains raster maps of live tree basal area for each tree species along with corresponding assessment data. An efficient approach for mapping multiple individual tree species over large spatial domains was used to develop these raster datasets. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-meter (m) pixel size for the contiguous United States. The approach uses the modeling techniques of k-nearest neighbors and canonical correspondence analysis, where model predictions are calculated using a weighting of nearest neighbors based on proximity in a feature space derived from the model. The approach also utilizes a stratification derived from the 2001 National Land-Cover Database tree canopy cover layer.�This data depicts current species abundance and distribution across the contiguous United States, modeled by using FIA field plot data. Although the absolute values associated with the maps differ from species to species, the highest values within each map are always associated with darker colors. The Little's Range Boundaries show the historical tree species ranges across North America. This is a digital representation of maps by Elbert L. Little, Jr., published between 1971 and 1977. These maps were based on botanical lists, forest surveys, field notes and herbarium specimens.Forest-type Groups:This dataset portrays the forest type group. Each group is a subset of the National Forest Type dataset which portrays 28 forest type groups across the contiguous United States. These data were derived from MODIS composite images from the 2002 and 2003 growing seasons in combination with nearly 100 other geospatial data layers, including elevation, slope, aspect, ecoregions, and PRISM climate data.Harvest Growth:This data shows the percentage of timber that is harvested when compared to the total live volume, at a county-by-county level. Timber volume in forests is constantly in flux, and harvest plays an important role in shaping forests. While most counties have some timber harvest, harvest volumes represent low percentages of standing timber volume.Carbon Harvest:The Carbon Harvest raster dataset represents Mg of annual pulpwood harvested (carbon) by county, derived from the Forest Inventory Analysis in 2016.

3

2 years ago

Raw data for urban trees in California communities

This study used data from field plots in urban areas to describe forest structure (e.g., tree numbers, density, basal area, species composition) for six land use categories in six California climate zones: Southern California Coast, Inland Empire, Inland Valley, Southwest Desert, Northern, and Interior West. Two types of field plot data were utilized. The first set of data include 702 randomly sampled 0.04 hectare (ha) plots obtained from i-Tree Eco plot data for Los Angeles (in 2007-2008), Santa Barbara (2012) and the Sacramento area (2007). The second set of data (687 plots, in 2011) consisted of 0.067 ha (four 0.017 ha subplots) plots based on the Forest Service Forest Inventory and Analysis (FIA) plot design. The number of plots collected varied by climate zone and a total of 3,796 trees were sampled. Data collection included percentage of tree canopy cover over the plot, tree species, stem diameter at breast height (1.37 meters above ground, dbh), tree height, crown width, distance and azimuth to buildings that fit the requirements as specified in the i-Tree Eco and Urban FIA manuals.

3

2 years ago

Original Proclaimed National Forests and National Grasslands (Feature Layer)

This layer includes both Proclaimed Forest and National Grassland boundary areas. A Proclaimed Forest boundary is the boundary encompassing National Forest System land within a national forest that is set aside and reserved from the public domain by executive order or proclamation; congressional action is required to terminate a proclaimed boundary; if, at some point in time, no National Forest System land remains within the proclaimed boundary, then there is no legal significance to the proclaimed boundary, however, its legal status remains. National Grasslands are lands designated "National Grasslands" by the Secretary of Agriculture and permanently held by the Department of Agriculture for administration under Title III of the Bankhead-Jones Farm Tenant Act.

7

2 years ago

National Wild and Scenic River Segments (Feature Layer)

This polyline feature class depicts the classification of each wild and scenic river segment designated by Congress and the Secretary of the Interior for the United States and Puerto Rico. This layer was created by a multi-agency effort including the US Forest Service, National Park Service, Bureau of Land Management and the Fish and Wildlife Service. The spatial data were referenced to the latest High Resolution National Hydrological Data Layer (NHD 1:24,000 Scale or better), published by United States Geological Survey (USGS). Wild rivers are free of dams, generally inaccessible except by trail, and represent vestiges of primitive America. Scenic rivers are free of dams, with shorelines or watersheds still largely primitive and shorelines largely undeveloped, but accessible in places by roads. Recreational rivers are readily accessible by road or railroad, may have some development along their shorelines, and may have been dammed in the past. Metadata

7

2 years ago

National USFS Fire Perimeter (Feature Layer)

The FirePerimeter polygon layer represents daily and final mapped wildland fire perimeters. Incidents of 10 acres or greater in size are expected. Incidents smaller than 10 acres in size may also be included. Data are maintained at the Forest/District level, or their equivalent, to track the area affected by wildland fire. Records in FirePerimeter include perimeters for wildland fires that have corresponding records in FIRESTAT, which is the authoritative data source for all wildland fire reports. FIRESTAT, the Fire Statistics System computer application, required by the USFS for all wildland fire occurrences on National Forest System Lands or National Forest-protected lands, is used to enter and maintain information from the Individual Fire Report (FS-5100-29).National USFS fire occurrence final fire perimeters where wildland fires have historically occurred on National Forest System Lands and/or where protection is the responsibility of the US Forest Service. Knowing where wildland fire events have happened in the past is critical to land management efforts in the future.This data is utilized by fire & aviation staffs, land managers, land planners, and resource specialists on and around National Forest System Lands.*This data has been updated to match 2021 National GIS Data Dictionary Standards.Metadata and Downloads

7

2 years ago

National USFS Final Fire Perimeter (Feature Layer)

The FinalFirePerimeter polygon layer represents final mapped wildland fire perimeters. This feature class is a subset of the FirePerimeters feature class. Incidents of 10 acres or greater in size are expected. Incidents smaller than 10 acres in size may also be included. Data are maintained at the Forest/District level, or their equivalent, to track the area affected by wildland fire. Records in FirePerimeter include perimeters for wildland fires that have corresponding records in FIRESTAT, which is the authoritative data source for all wildland fire reports. FIRESTAT, the Fire Statistics System computer application, required by the USFS for all wildland fire occurrences on National Forest System Lands or National Forest-protected lands, is used to enter and maintain information from the Individual Fire Report (FS-5100-29).National USFS fire occurrence final fire perimeters where wildland fires have historically occurred on National Forest System Lands and/or where protection is the responsibility of the US Forest Service. Knowing where wildland fire events have happened in the past is critical to land management efforts in the future.This data is utilized by fire & aviation staffs, land managers, land planners, and resource specialists on and around National Forest System Lands.*This data has been updated to match 2021 National GIS Data Dictionary Standards.Metadata and Downloads

7

2 years ago

National Riparian Area Base Map (Image Service)

This imagery layer shows national riparian areas for the conterminous United States. Riparian areas are an important natural resource with high biological diversity. These ecosystems contain specific vegetation and soil characteristics which support irreplaceable values and multiple ecosystem functions and are very responsive to changes in land management activities. Delineating and quantifying riparian areas is an essential step in riparian monitoring, planning, management, and policy decisions. USDA Forest Service supports the development and implementation of a national context framework with a multi-scale approach to define riparian areas utilizing free available national geospatial datasets. Why was this layer created? To estimate 50-year flood height riparian areas to support statistical analysis, map display, and model parameterization.Provide a framework and an end product to stakeholders and apply the information into management actions and strategies.Multi-scale approach to provide a national and regional report map. Create a product for managers to easily understand where to apply the information at various scales.Develop a national context inventory of riparian areas and their condition within national forests and rangelands.How was this layer created? Using freely available data.Develop cost effective modeling approach & technique.Multi-scale (national, regional, & local).Promote technology transfer to train/reach out to our partners.Fifty-year flood heights were estimated using U.S. Geological Survey (USGS) stream gage information. NHDPlus version 2.1 was used as the hydrologic framework to delineate riparian areas. The U.S. Fish and Wildlife Service's National Wetland Inventory and USGS 10-meter digital elevation models were also used in processing these data.The data are '1' if in the riparian zone and 'NoData' if outside the riparian zone. When displayed on a map, riparian zone cells are color-coded 'blue' with 25% transparency.For additional information regarding methodologies for modeling and processing these data, see Abood et al. (2012) and the National Riparian Areas Base Map StoryMapData Download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0030

3

2 years ago

National Interagency Fire Occurrence Fifth Edition 1992-2018 (Feature Layer)

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the geodatabase or shapefile option. This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2018. It is the fourth update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as a Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG), including an updated wildfire-cause standard (approved August 2020). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. In addition to incorporating data for 2016-2018, some previously unavailable nonfederal wildfire records for the period 1999-2015 were acquired either directly from the state fire services (NH, NJ) or indirectly from an updated National Association of State Foresters database (AR, AZ, CA, CO, FL, HI, ID, IL, OK, SD) and added. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 2.17 million geo-referenced wildfire records, representing a total of 165 million acres burned during the 27-year period. Identifiers necessary to link the point-based, final-fire-reporting information to published large-fire-perimeter and operational-situation-reporting datasets are included. Additional Information

11

2 years ago

National Insect and Disease Risk Map Viewer

FHAAST provides support for both tactical and strategic forest health risk assessments. In addition, this program coordinates, in collaboration with the USDA Forest Service Forest Health Monitoring program (FHM), the development of a National Insect and Disease Risk Map (NIDRM) and database.FHAAST has completed the 2013 - 2027 National Insect and Disease Risk Map (2012 NIDRM); a nationwide strategic assessment and database of the potential hazard for tree mortality due to major forest insects and diseases. The goal of NIDRM is to summarize landscape-level patterns of potential insect and disease activity, consistent with the philosophy that science-based, transparent methods should be used to allocate pest-management resources across geographic regions and individual pest distributions. In other words: prioritize investment for areas where both hazard is significant and effective treatment can be efficiently implemented.NIDRM data can be used to:Identify the potential impacts of pests and pathogens to forest ecosystems throughout the US for the 2013 - 2027 timeframe.Generate forest pest and pathogen risk maps at a scale useful for resource planning and management purposes in many of our National Forests, National Parks, and other local units.Develop an effective strategic planning tool that can inform assessments of natural ecosystems and ensure resources for forest pest prevention, suppression, and restoration reaches the highest priority areas.Detect areas where hazardous fuels treatments coincide with lands at risk for forest pest activity, much of which is density driven. Efficiencies will be gained by prioritizing coincident areas.For a quick overview of the 2013 - 2027 assessment and to learn more information on the differences between the 2006 and 2012 NIDRMs download the executive summary (2 MB PDF).Explore forests vulnerable to attack from major insects and diseases by viewing the Interactive Story Map of the National Insect and Disease Risk Map

3

2 years ago

National Forest System Trails (Feature Layer)

The Trails Layer is designed to provide information about National Forest System trail locations and characteristics to the public. When fully realized, it will describe trail locations, basic characteristics of the trail, and where and when various trail uses are prohibited, allowed and encouraged. Because the data readiness varies between Forests, each Forest will approve which level of attribute subset are published for that forest. Forests can provide no information or one of three attribute subsets describing trails. The attribute subsets include TrailNFS_Centerline which includes the location and trail name and number; TrailNFS_Basic which adds information about basic trail characteristics; and TrailNFS_Mgmt which adds information about where and when users are prohibited, allowed, and encouraged. When a Forest chooses to provide the highest attribute subset, TrailNFS_Mgmt, these attributes must be consistent with the Forest's published Motorized Vehicle Use Map (MVUM). Metadata for the individual Forest feature classes used to compile this feature class are available at data.fs.usda.gov/geodata/edw/dir_trails.php. Metadata

9

2 years ago

National USFS Fire Occurrence Point (Feature Layer)

The FireOccurrence point layer represents ignition points, or points of origin, from which individual USFS wildland fires started. Data are maintained at the Forest/District level, or their equivalent, to track the occurrence and the origin of individual USFS wildland fires. Forests are working to include historical data, which may be incomplete.National USFS fire occurrence locations where wildland fires have historically occurred on National Forest System Lands and/or where protection is the responsibility of the US Forest Service. Knowing where wildland fire events have happened in the past is critical to land management efforts in the future.This data is utilized by fire & aviation staffs, land managers, land planners, and resource specialists on and around National Forest System Lands. The attributes included within the FireOccurrence point layer are needed to meet the needs of the US Forest Service, for data exchange between interagency data systems, to relate to the FirePerimeter polygon data layer and various fire data systems, and to track the locations of wildland fires.*This data has been updated to match 2021 National GIS Data Dictionary Standards.Metadata and Downloads

7

2 years ago

National Snag Hazard (Image Service)

Snags continue to pose an ever-present hazard to responders, and recent increases in fire activity have resulted in an accumulation of these hazards across forested landscapes of the American West. National Snag Hazard is intended to provide a landscape-level view of existing snag hazard to firefighters and other field going employees. National Snag hazard is based on estimated density and median height of snags greater than or equal to 7.9 inches in diameter at breast height. Snag density and median snag height are classified into hazard levels using the breakpoints from Dunn et al. 2019, which are based on the logic that hazard increases with snag density and height. Dunn CJ, O’Connor CD, Reilly MJ, Calkin DE, Thompson MP (2019) Spatial and temporal assessment of responder exposure to snag hazards in post-fire environments. Forest Ecology and Management 441, 202-2014. DOI:10.1016/j.foreco.2019.03.035 This is a strategic landscape level decision support tool intended to help firefighters consider the magnitude and spatial distribution of snag hazard in their incident response strategy planning. Valid uses include identifying areas of higher snag hazard locations on landscape that may require extra mitigation for safe operation or could be avoided to reduce risk to responders. The snag hazard map is not meant to identify individual dead trees or for tactical planning. A rating of low snag hazard does not mean that no overhead hazards are present and should not be interpreted as judgement that an area is safe to occupy. Conditions should always be verified in the field. High levels of awareness for overhead hazards are always recommended regardless of the snag hazard rating.

3

2 years ago

National Interagency Fire Occurrence Sixth Edition 1992-2020 (Feature Layer)

This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2020. It is the fifth update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as a Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG), including an updated wildfire-cause standard (approved August 2020). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 2.3 million geo-referenced wildfire records, representing a total of 180 million acres burned during the 29-year period. Identifiers necessary to link the point-based, final-fire-reporting information to published large-fire-perimeter and operational-situation-reporting datasets are included. View MetadataAdditional Information

11

2 years ago

National Grassland Units (Feature Layer)

A National Grassland unit designated by the Secretary of Agriculture and permanently held by the Department of Agriculture under Title III of the Bankhead-Jones Farm Tenant Act. Metadata

7

2 years ago

National Forest System Land Units (Map Service)

Note: This map service contains generalized NFS Land Unit boundaries to help with map service performance. Data in this service is not as accurate as the Automated Lands Program published data and will not accurately represent the boundary.National Forest System Land Unit original accurate data can be downloaded from here.An NFS Land Unit is nationally significant classification of Federally owned forest, range, and related lands that are administered by the USDA Forest Service or designated for administration through the Forest Service. NFS Land Unit types include proclaimed national forest, purchase unit, national grassland, land utilization project, research and experimental area, national preserve, and other land area. Each NFS Land Unit is identified by a National Forest Fiscal Identifier (NFFID) code, a unique 4-digit number that is used for accounting purposes.

3

2 years ago

Motor Vehicle Use Map: Roads (Feature Layer)

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and NRM Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only roads with a SYMBOL attribute value of 1, 2, 3, 4, 11, and 12 are Forest Service System roads and contain data concerning their availability for OHV (Off Highway Vehicle) use. This data is published and refreshed on a unit by unit basis as needed. Data for each individual unit must be verified and proved consistent with the published MVUMs prior to publication.The Forest Service's Natural Resource Manager (NRM) Infrastructure (Infra) is the agency standard for managing and reporting information about inventory of constructed features and land units as well as the permits sold to the general public and to partners. Metadata

9

2 years ago

Monitoring Trends in Burn Severity Hawaii (Map Service)

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.�Direct Download

3

2 years ago

Monitoring Trends in Burn Severity Conterminous United States (Map Service)

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project. Direct Download - https://www.mtbs.gov/direct-downloadMTBS Burn Area Boundary Full Metadata - https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.MTBS_BURN_AREA_BOUNDARY.xmlMTBS Fire Occurrence Point Full Metadata - https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.MTBS_FIRE_OCCURRENCE_PT.xmlFS Geodata Clearinghouse Downloads Page - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=MTBS

6

2 years ago

Monitoring Trends in Burn Severity Alaska (Map Service)

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.

3

2 years ago

Missoula Fire Lab Emission Inventory (MFLEI) for CONUS

The Missoula Fire Lab Emission Inventory (MFLEI) is a retrospective, daily wildfire emission inventory for the contiguous United States with a spatial resolution of 250 meters (m). MFLEI was produced using multiple datasets of fire activity and burned area, a newly developed wildland fuels map and an updated emission factor database. This data publication contains the 2003-2015 MFLEI estimates of daily fuel consumed and emissions of CO2, CO, CH4, and PM2.5 at 250 m spatial resolution. The inventory also includes carefully constructed uncertainty estimates for daily fuel consumption and emissions at 250 m spatial resolution. The dataset includes daily emissions and uncertainties aggregated to 10 kilometer (km) � 10 km grid. The aggregated product provides area burned, mass of fuel consumed, and emissions of CO2, CO, CH4, and PM2.5. The emission and emission uncertainty data are provided as comma-delimited ASCII text files. MFLEI fuel consumption and land cover type may be combined with published emission factor datasets to estimate emissions for hundreds of volatile organic compounds and other pollutants present in fresh wildfire smoke. This data publication contains geospatial data in raster format and tabular data. The raster datasets includes a map of the coefficient of variation of the herbaceous fuel loading, a land cover map of herbaceous, shrub, and forest type groups (FIA), and maps of the upper bound, lower bound, and best estimate of herbaceous or shrub fuel loading.

3

2 years ago

Mean Fraction of Summer Runoff from Forest Service Lands (Map Service)

Available water supply varies greatly across the United States depending on topography, climate, elevation and geology. Forested and mountainous locations, such as national forests, tend to receive more precipitation than adjacent non-forested or low-lying areas. However, contributions of national forest lands to regional streamflow volumes is largely unknown. Using outputs from the Variable Infiltration Capacity hydrologic model, we calculated mean annual and mean summer (July and August) streamflow metrics based on total flow and flow from national forest lands for each 1:100,000 scale National Hydrography Dataset stream reach in the contiguous United States. Specifically, this data publication contains twenty-one comma-delimited ASCII text files (for different drainage areas and processing units across the United States) containing 1915-2011 mean annual flow and mean summer flow.Data can be downloaded here: Geodatabase or ShapefileThese files also contain the mean annual and mean summer flows from National Forest System (NFS) lands as well as the portion of total mean annual and summer flow contributed by flow from NFS lands.These data provide insight into 1915-2011 hydrologic regimes and national forest contributions to total water yield. These non-spatial files were then merged and joined to the September 2012 snapshot of the National Hydrography Dataset (NHD), version 2.Note: 'Forest Service lands' are here defined as those lands within the Forest Service administrative boundaries; these include some inholdings and other non-USFS lands enclosed within these boundaries.

3

2 years ago

MTBS Wildfire Occurrence

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2018. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and 2018 for the continental United States, Alaska, Hawaii and Puerto Rico. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. Map Service Feature Layer

3

2 years ago

Mineral Rights (Feature Layer)

An area depicting ownership parcels of the subsurface estate representing mineral rights; it is collected only if the subsurface estate is different than the overlying surface estate. Metadata

7

2 years ago

Hydro Flow Metrics 2040 (Map Service)

This map service represents modeled streamflow metrics from the mid-century time period (2030-2059) in the United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods. These files and additional information are available on the project website, https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from here.

3

2 years ago

Historical winter temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical winter precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical summer temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).\n\n

3

2 years ago

Historical summer precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical snow residence time (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical annual temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Monarch Butterfly Habitat Restoration: Polygon (Feature Layer)

Activities meeting the Monarch Butterfly Habitat Restoration initiative are a subset of activities that benefit native plants, and in doing so, benefit pollinators. Activities such as thinning, prescribed fire and other methods of fuel removal, treating invasive species and acres of native plantings can benefit Monarchs. Activities are self-reported by Forest Service Units and are reported when completed. This layer does not contain all activities that benefit Monarchs because this is a relatively new requirement for the program. The quality and comprehensiveness of this data will increase over time. Metadata

7

2 years ago

Healthy Forest Restoration Act Activities (Feature Layer)

The Healthy Forest Restoration Act feature class depicts National Forest System (NFS) Lands within 38 States designated under section 602 and 603 of the Healthy Forest Restoration Act. Designated areas were selected based on a set of eligibility criteria regarding forest health and do not include any areas coinciding with Wilderness and Wilderness Study Areas. The data is comprised of selected HUC-6 units or other areas of similar size and scope clipped to Proclaimed National Forest System lands. Non-Forest Service land ownership areas (inholdings) are also removed. In some cases, entire National Forests were designated. Some state designations' methodologies may differ from the national standard. Metadata and DownloadsPlease note that this data is current as of the last refresh date, and changes to designated areas will be republished and archived on a weekly basis.

7

2 years ago

Hazardous Fuel Treatment Reduction: Polygon (Feature Layer)

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents activities of hazardous fuel treatment reduction that are polygons. All accomplishments toward the unified hazardous fuels reduction target must meet the following definition: Vegetative manipulation designed to create and maintain resilient and sustainable landscapes, including burning, mechanical treatments, and/or other methods that reduce the quantity or change the arrangement of living or dead fuel so that the intensity, severity, or effects of wildland fire are reduced within acceptable ecological parameters and consistent with land management plan objectives, or activities that maintain desired fuel conditions. These conditions should be measurable or predictable using fire behavior prediction models or fire effects models. Go to this url for full metadata description: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.Activity_HazFuelTrt_PL.xml

9

2 years ago

Great Basin Montane Watersheds - Valley Bottoms (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Terrestrial Condition Assessment (TCA) Grassland Productivity (Map Service)

The average relative difference of the mean inter-annual variability of vegetation production between a reference time period (1984-2014) and the current time period (2015-2019).

3

2 years ago

Climate Shield Cutthroat Trout (0% Brook Trout), 1980 (Feature Layer)

This feature class represents the historical (1970-1999 scenario for cutthroat trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields CT_0BRK - CT_100BRK indicate the probabilities of cutthroat trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

SuperTracker

The SuperTracker is an online tool that helps you track what you currently eat and drink, gives you a personalized plan for what you should eat and drink, and guides you to make better choices.

1

2 years ago

Summer Food Service Program (SFSP) Rural Designation Tool

Allows State agencies and sponsors to determine if a proposed site may be designated as rural for purposes of identifying a site as eligible for increased reimbursement in SFSP. To use the map, users must enter the address of the proposed site in the “Find Address or Place” box located on the right side of the screen. By pressing enter, the map will zoom to the location specified. Locations shaded in purple are non-rural; unshaded locations are rural.

1

2 years ago

SNAP State Information/Hotline Numbers

Use the following numbers to get information on SNAP benefit questions in the States and areas of States listed. Most are toll-free numbers. Some of the numbers that aren't toll free will accept collect calls. * Indicates numbers are for in-State and out-of-State calls. All other 800 numbers are for in-State calls only. ** Indicates numbers accept collect calls.

1

2 years ago

Forest Inventory and Analysis Database

The Forest Inventory and Analysis (FIA) research program has been in existence since mandated by Congress in 1928. FIA's primary objective is to determine the extent, condition, volume, growth, and depletion of timber on the Nation's forest land. Before 1999, all inventories were conducted on a periodic basis. The passage of the 1998 Farm Bill requires FIA to collect data annually on plots within each State. This kind of up-to-date information is essential to frame realistic forest policies and programs. Summary reports for individual States are published but the Forest Service also provides data collected in each inventory to those interested in further analysis. Data is distributed via the FIA DataMart in a standard format. This standard format, referred to as the Forest Inventory and Analysis Database (FIADB) structure, was developed to provide users with as much data as possible in a consistent manner among States. A number of inventories conducted prior to the implementation of the annual inventory are available in the FIADB. However, various data attributes may be empty or the items may have been collected or computed differently. Annual inventories use a common plot design and common data collection procedures nationwide, resulting in greater consistency among FIA work units than earlier inventories. Links to field collection manuals and the FIADB user's manual are provided in the FIA DataMart.

6

2 years ago

FS/NRCS Joint Chiefs' Landscape Restoration Partnership Projects (Feature Layer)

Activities completed under the FS/NRCS Joint Chiefs' Landscape Restoration Partnership (LRP) program. Metadata and Downloads

7

2 years ago

FIA Landcover County Estimates - 2016 (Feature Layer)

This feature class represents forest area estimates (and percent sampling error) by county for the year 2016. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

7

2 years ago

Extent of U.S. Rangelands (Image Service)

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option. An image service that depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands.This raster dataset depicts rangelands in the coterminous U.S., including transitional rangelands and small patch-size rangelands. Each 30 meter pixel is assigned a land cover category, including Rangeland, Afforested Rangeland (experiencing encroachment by trees [> 25% tree cover]) and Transitional Rangeland (currently dominated by herbs or shrubs that will likely become forested without management intervention).Rangeland extent is an important factor for evaluating critical indicators of rangeland sustainability. Rangeland areal extent was determined for the coterminous United States in a geospatial framework by evaluating spatially explicit data from the Landscape Fire and Resource Management Planning Tools (LANDFIRE) project describing historic and current vegetative composition, average height, and average cover through the viewpoint of the Natural Resources Inventory (NRI) administered by the Natural Resources Conservation Service. Three types of rangelands were differentiated using the NRI definition encompassing rangelands, afforested rangelands, and transitory rangelands.Data can be downloaded from here. To learn more rangelands and drought, see this StoryMap; additional drought and rangeland products from the Office of Sustainability and Climate are available in our Climate Gallery.

3

2 years ago

Ecological Sections (Feature Layer)

This data set includes polygons for ecological sections within Subregions within the conterminous United States. This data set contains regional geographic delineations for analysis of ecological relationships across ecological units. Metadata

7

2 years ago

FIA Landcover County Estimates - 2019 (Feature Layer)

This feature class represents forest area estimates (and percent sampling error) by county for the year 2019. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

7

2 years ago

FIA Landcover County Estimates - 2017 (Feature Layer)

This feature class represents forest area estimates (and percent sampling error) by county for the year 2017. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

7

2 years ago

Brush Disposal Funded Activities (Feature Layer)

Depicts the area of activities funded through BDBD and PPPP budget line item and reported through the FACTS database. The objective of the BD Program is to dispose of unwanted slash or other debris created by timber purchaser operations on timber sale contracts, stewardship contracts and permits, not disposed of by the purchaser. Activities are self-reported by Forest Service Units. The Brush Disposal Program (BD) objective of the BD Program was established in 1916. It requires all purchasers of National Forest timber make deposits to the United States for the estimated cost of disposing of brush and other debris resulting from its cutting operations. Brush disposal activities must be consistent with direction established in forest land and resource management plans, identified in environmental documents developed in accordance with the National Environmental Policy Act of 1969 (NEPA). Metadata

7

2 years ago

Updating Standardized State Heating and Cooling Utility Allowance Values

While most Supplemental Nutrition Assistance Program (SNAP) eligibility parameters are set at the federal level, states may establish their own standard utility allowances (SUAs) which are part of the excess shelter expense deduction. The use of SUAs, including heating and cooling SUAs (HCSUAs) for households with heating and cooling expenses, simplifies the application process for both the applicant and the state agency. However, the Food and Nutrition Service (FNS) has found some variation between established HCSUA values and household utility expenses in some states.

1

2 years ago

Characteristics of masticated particles in mixed-conifer forests of the western United States: Field data

This data publication contains the results of field work in masticated materials of mixed-conifer forests in 14 study locations. Mixed-conifer masticated materials were investigated in four states of the western U.S., including Idaho, Colorado, New Mexico, and South Dakota. The data were collected from 2012 through 2016 as part of the MASTIDON project, which was a four-year research project to characterize how burning properties of masticated material are affected when different cutting machines are used to treat the forests and when masticated particles are left on the ground for multiple years to decompose. The project was funded by the Joint Fire Sciences Program (JFSP) and RMRS between 2013 and 2016. The masticated particles within this project were created by four different machines, including a vertical rotating head, horizontal drum, chipper, and mower. They had been decomposing in situ in wet and dry areas of the mixed-conifer forests since their initial treatment. This publication gives GPS locations and laser elevation data for each field site and the GPS locations where depth measurements were taken within each macroplot. It gives depths for each of the five fuel layers distinguished within the masticated materials at two scales. The first scale is at three-meter intervals along each of six transect lines. The second scale is within each microplot and quarter plot where samples were taken from the quarter plots for further lab work. The data also contain estimates of vegetation cover and height at each of the depth-measurement locations.

3

2 years ago

Characteristics of masticated particles in mixed-conifer forests of the western United States: Chemistry, heat content, and mineral percentage results

This data publication contains the results of chemical and mineral analyses on masticated particles from mixed-conifer forests in 15 study locations. These data were collected from 2012 through 2016 as part of the MASTIDON project. The MASTIDON project was a four-year research project to study how masticated material differs when treated with different cutting machines and how the masticated particles decompose when left on the ground for multiple years. It investigated masticated materials in four states of the western United States. The project was funded by the Joint Fire Sciences Program (JFSP) and RMRS between 2013 and 2016. The masticated particles within this project had been decomposing in situ in wet and dry areas of Idaho, Colorado, New Mexico, and South Dakota since their initial treatment. Particles were tested from four shapes (circular, three-sided, four-sided, and small wood chips) and three size classes. Each shape and size class was ground, dried, and analyzed for percent carbon and nitrogen, cellulose and lignin, heat content, and mineral content (from the duff component) using three pieces of equipment. This data publication includes the results of each of these tests and files describing the MASTIDON project and its goals.

3

2 years ago

Beaver monitoring data from the Chequamegon-Nicolet National Forest, Wisconsin

The primary raw data are aerial counts of beaver (Castor canadensis) colonies on streams across the Chequamegon-Nicolet National Forest (CNNF). These aerial counts were performed in the fall of each year from 1987 (Nicolet side of CNNF) or 1997 (Chequamegon side of CNNF). Based on the colony counts, we also provide derived beaver colony density values. The surveyed streams were classified into four categories: managed trout, non-managed trout, managed non-trout, and non-managed non-trout. Trout versus non-trout status was assigned by the CNNF using Wisconsin Department of Natural Resources information. Managed streams were those on which targeted removal of beavers was conducted in the spring of each year under a contract with USDA-Wildlife Services. Data also include proportion of stream-side aspen, temperature, snowfall, and soil moisture as measured by the Palmer Drought Severity Index.

3

2 years ago

Aerial Fire Retardant Avoidance Areas: Terrestrial (Feature Layer)

This layer contains features of aerial fire retardant avoidance areas delivered as part of the 2011 Nationwide Aerial Application of Fire Retardant on National Forest System Land Environmental Impact Statement. This Feature Class shows areas, provided by each National Forest who used aerial fire retardant from 2000-2010, where the aerial application of fire retardant should be avoided in order to prevent the potential of impacts to Federally listed threatened or endangered species as identified through consultation, or Forest Service sensitive species. Data includes location of terrestrial and hydrographic areas where the application of aerial fire retardant is to be avoided. Current aerial fire retardant standards prohibit application within 300 feet of hydrographic features. Therefore, this data may contain duplicate hydrographic areas already covered by existing standards. This data is to be used in planning and implementation phases of USFS fire activities to help prevent misapplication of aerial fire retardant in known areas of TES species or water features throughout National Forest lands. Provided here is a National merged dataset derived from each National Forest contribution. This data has been merged, dissolved, and erased of attributes contained in each original component dataset. For this purpose, specific attributes are not necessary, as any spatial areas depicted simply show areas where aerial fire retardant use is to be avoided as stated in USFS guidelines. Metadata

7

2 years ago

Activity Project Areas Sale Area Improvement (SAI) Plan (Feature Layer)

Activity Project Area Sale Area Improvement (SAI) Plan represents an area (polygon) within which one or more Sale Area Improvement (SAI) related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and Downloads

7

2 years ago

Absolute change in winter temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Absolute change in winter precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Absolute change in summer temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Absolute change in summer precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Local School Wellness Policy Outreach Toolkit

Engage school staff and parents in school wellness using these ready-to-go communication tools. Sharing news about your Local School Wellness Policy is easy with these flyers, presentations, newsletter articles, and social media posts. Your school can personalize them to make them specific to your Local School Wellness Policy activities.

1

2 years ago

Listing of Dairy License Recipients Under Regulation 1

Listing of Dairy License Recipients Under Regulation 1 (as of March 2016)

1

2 years ago

Trends in USDA SNAP Participation Rates FY 2016-19

This report is the latest in a series on SNAP participation rates, which estimate the proportion of people eligible for benefits under federal income and asset rules to those who actually participate in the program. This report presents rates for fiscal year (FY) 2019, comparing them to rates for FY 2016-19 and showing participation rates by household characteristics.

1

2 years ago

Survey of USDA's SNAP E&T Program Case Management

With the passage of the Agricultural Improvement Act of 2018 (known as the 2018 Farm Bill), states are now required to provide case management to all Supplemental Nutrition Assistance Program (SNAP) Employment and Training (E&T) program participants. Although some states have provided case management as part of their SNAP E&T programs for many years, others are now implementing it for the first time or enhancing their services in response to this requirement. States' case management and assessment practices have not been well documented.

1

2 years ago

Special Milk Program

Providing Nutritious Milk to Children The Special Milk Program (SMP) provides milk to children in schools and childcare institutions who do not participate in other federal meal service programs. The program reimburses schools for the milk they serve. Schools in the National School Lunch or School Breakfast Programs may also participate in the Special Milk Program to provide milk to children in half-day pre-kindergarten and kindergarten programs where children do not have access to the school meal programs.

1

2 years ago

Soil Climate Analysis Network (SCAN)

The SCAN data retrieval tools provides an interactive process to identify and retrieve data from individual SCAN sites. The user does not need to know the ID for the site but must know either it's general location or the name of the site

1

2 years ago

School Breakfast Program

The School Breakfast Program (SBP) provides reimbursement to states to operate nonprofit breakfast programs in schools and residential childcare institutions. The Food and Nutrition Service administers the SBP at the federal level. State education agencies administer the SBP at the state level, and local school food authorities operate the program in schools.

1

2 years ago

SNOwpack TELemetry Network (SNOTEL)

The SNOTEL data retrieval tools provides an interactive process to identify and retrieve data from individual SNOTEL sites. The user does not need to know the ID for the site but must know either it's general location or the name of the site

0

2 years ago

USDA Community Eligibility Provision Characteristics Study SY 2016-17

This Community Eligibility Provision (CEP) Characteristics study is the first comprehensive study since CEP became available nationwide in SY 2014-15. The study was designed to provide the U.S. Department of Agriculture’s (USDA) Food and Nutrition Service (FNS) with information about the impact of CEP. The study included both an implementation and impact component. Key findings in this report include: CEP participation increased both student school meal participation and level of federal reimbursements. Seventy-six percent of Local Education Agencies (LEA) participating in CEP elected to participate LEA-wide. The LEA-wide Identified Student Percentage (ISP) was the most important perceived factor in CEP election. Eligible, non-participating LEAs indicated that CEP would be more appealing if the factor used to determine meal reimbursement levels were increased. Financial concerns were the largest barrier to CEP participation for LEAs with lower ISPs.

1

2 years ago

SNAP EBT Third Party Processor (TPP) List and Guidance to Retailers

The list below provides Third Party Processor options for SNAP ‐ authorized retailers who may not know where to obtain EBT equipment and services. All SNAP ‐ authorized retailers, except those exempted below, must pay for their own EBT equipment and services and should arrange for lease or purchase of EBT equipment and services as soon as they can, in order to ensure future participation in SNAP

1

2 years ago

Ready-to-Eat (RTE) Intensified Verification Testing Sampling Data

Establishment specific sampling results for FSIS' Ready-to-Eat Intensified Verification Testing. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

Plant List of Accepted Nomenclature, Taxonomy, and Symbols (PLANTS) Database

The PLANTS Database provides standardized information about the vascular plants, mosses, liverworts, hornworts, and lichens of the U.S. and its territories

3

2 years ago

Pesticide Data Program 2015

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

SNAP Benefits Costs - Latest Available Month

This dataset provides the Supplemental Nutrition Assistance Program (SNAP) benefits costs for each state.

1

2 years ago

Official Soil Series Descriptions (OSDs)

The Official Soil Series Descriptions (OSD) is a national collection of more than 20,000 detailed soil series descriptions, covering the United States, Territories, Commonwealths, and Island Nations served by USDA-NRCS. The descriptions, in a text format, serve as a national standard. The soil series is the lowest category of the national soil classification system. The name of a soil series is the common reference term, used to name soil map units. Soil series are the most homogenous classes in the system of taxonomy. “Official Soil Series Descriptions” define specific soil series in the United States, Territories, Commonwealths, and Island Nations served by USDA-NRCS. They are descriptions of the taxa in the series category of the national system of soil classification. They serve mainly as specification for identifying and classifying soils. The descriptions contain soil properties that define the soil series, distinguish it from other soil series, serve as the basis for the placement of that soil series in the soil family, and provide a record of soil properties needed to prepare soil interpretations.

0

2 years ago

Results of FNS-Administered School Food Authority Survey on Supply Chain Disruption

The vast majority (90 percent) of SFAs are using the Seamless Summer Option Waiver to serve meals in SY 2021-22 which allows schools to offer all students free meals at the higher Summer Food Service Program reimbursement rates. Public and larger SFAs were more likely to use the waiver than smaller or private SFAs. SFAs reported experiencing the most challenges procuring meal service supplies, meat/meat alternates (such as chicken products), and whole grain items (including bakery items, breads and rolls). Many SFAs reported that these challenges are getting worse compared to the beginning of SY 2021-22.

1

2 years ago

Pesticide Data Program 2016

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2014

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Food Safety Research: Farm to School

The U.S. Centers for Disease Control and Prevention estimates that germs on fresh produce cause approximately 46% of foodborne illnesses in the United States. However, there are proven food safety practices–from growing to preparation to service–that can reduce risk of illness associated with fresh produce.

1

2 years ago

FSIS - Salmonella Quarterly Reports

Percent positive and serotype quarterly sampling information for Salmonella and Campylobacter in FSIS Raw Products. Reports are updated quarterly. See FSIS website for additional information.

1

2 years ago

FSIS - Meat, Poultry, and Egg Inspection Directory by Establishment Number

The Meat, Poultry and Egg Product Inspection Directory is a listing of establishments that produce meat, poultry, and/or egg products regulated by USDA's Food Safety and Inspection Service (FSIS) pursuant to the Federal Meat Inspection Act, the Poultry Products Inspection Act, and the Egg Products Inspection Act. The directory is updated weekly, and the current edition replaces all previous editions.

1

2 years ago

FSIS - Establishment Demographic Data – Meat and Poultry Inspection (MPI) Directory Supplement

These data provide additional demographic information about FSIS regulated establishments. Additional demographic data are also available in the FSIS Meat, Poultry, and Egg Inspection Directory (MPI). The Meat, Poultry and Egg Product Inspection Directory is a listing of establishments that produce meat, poultry, and/or egg products regulated by USDA's Food Safety and Inspection Service (FSIS).

1

2 years ago

USDA Rural Development Multi Family Housing - August 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for August 2018.

1

2 years ago

FSIS - Egg Product Sampling Data

Establishment specific sampling results for Pasteurized Egg Products. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

FSIS - Antimicrobial Quarterly Reports

Quarterly Sampling Reports for antimicrobial resistance, specifically in cecal and product. Cecal reports consists of data for Salmonella, Campylobacter, E.coli, and Enterococcus. Product reports consists of data for Salmonella, Campylobacter, and Escherichia coli O157:H7 and non-O157 Shiga toxin-producing Escherichia coli (STEC). All reports are updated quarterly. See the FSIS website for additional information.

1

2 years ago

Cropland Data Layer

The USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) is an annual raster, geo-referenced, crop-specific land cover data layer produced using satellite imagery and extensive agricultural ground reference data. The program began in 1997 with limited coverage and in 2008 forward expanded coverage to the entire Continental United States. Please note that no farmer reported data are derivable from the Cropland Data Layer.

4

2 years ago

FY2020 Farm to School Grant Baseline Webinar

This webinar for 2020 Farm to School Grantees on how to report and transmit both baseline information reports required for the Farm to School Grants. Both parts were recorded live via WebEx on Oct. 1, 2020.

1

2 years ago

Food-a-pedia

Find the calorie content of any food or beverage using the Food-a-pedia, looking at the Nutrition Facts label, or checking product or restaurant websites

1

2 years ago

Federal Cost of School Food Program Data

The mission of FNS is to provide children and needy families better access to food and a more healthful diet through its food assistance programs and comprehensive nutrition education efforts. These dataset provides a summary of all the FNS School Food Program combined into one dataset. It contains cash payments and commodity costs for the National School Lunch Program, School Breakfast Program and the Special Milk Program. (format: html, xls)

1

2 years ago

Wildfire Suppression Difficulty Index 90th Percentile 2022 (Image Service)

Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft). SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.

3

2 years ago

Wildfire Suppression Difficulty Index 97th Percentile 2022 (Image Service)

Wildfire Suppression Difficulty Index (SDI) 97th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 20 mph uphill winds (@ 20 ft).SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.

3

2 years ago

Farm to School Contacts

This dataset provides contact information for Farms to School for each state.

1

2 years ago

Wildfire Suppression Difficulty Index (Map Service)

Wildfire Suppression Difficulty Index (terrestrial) (SDIt) is a quantitative rating of relative difficulty in performing fire control work. In its original formulation for use in Spain, SDI included aerial resource use, however for development and application in the United States we removed the aerial resource component due to a lack of consistent data. We note this distinction of terrestrial only calculations with the inclusion of t in the acronym. SDIt factors in topography, fuels, expected fire behavior under severe fire weather conditions, firefighter line production rates in various fuel types, and accessibility (distance from roads/trails) to assess relative suppression effort. For this dataset severe fire behavior is modeled with 15 mph up-slope winds and fully cured fuels. SDI has a continuous value distribution from 1-10. Here it is binned to six classes from lowest to highest difficulty.

3

2 years ago

Wildfire Hazard Potential, Version 2020 Classified (Image Service)

Wildfire hazard potential (WHP) is an index that depicts the relative potential for wildfire that would be difficult for suppression resources to contain, based on wildfire simulation modeling. This dataset produced by the USDA Forest Service, Fire Modeling Institute in 2020 shows WHP at a spatial resolution of 270 meters across the entire conterminous United States, classified into five WHP classes of very low, low, moderate, high, and very high. Areas mapped with higher WHP values represent fuels with a higher probability of experiencing torching, crowning, and other forms of extreme fire behavior under conducive weather conditions, based primarily on 2014 landscape conditions. This WHP dataset is based on outputs of wildfire simulation modeling published in 2020.Starting with the 2020 version, the WHP dataset is integrated with the Wildfire Risk to Communities project. The 2020 dataset is the first version to include Alaska and Hawaii. There is a spatially-refined, 30-m resolution version of the WHP as part of the downloadable Wildfire Risk to Communities data, and related datasets that depict other components of wildfire hazard and risk to homes.This 2020 version supersedes all previous versions of Wildfire Hazard Potential (2018, 2014) or Wildland Fire Potential (2012, 2010, 2007). We generally do not advise direct comparisons between versions because changes can reflect improvements in methodology at all stages of the WHP calculation in addition to actual land cover changes.For more information and to download the raster data, please visit the Wildfire Hazard Potential website.Map author: Greg Dillon, USDA Forest Service, Rocky Mountain Research Station, Fire Modeling Institute

3

2 years ago

Wildfire Hazard Potential, Version 2014 Classified (Image Service)

The wildfire hazard potential (WHP) is a raster geospatial product at 270-meter resolution covering all lands in the conterminous United States. It can help to inform evaluations of wildfire risk or prioritization of fuels management needs across very large landscapes (millions of acres). Our specific objective with the WHP map is to depict the relative potential for wildfire that would be difficult for suppression resources to contain. For more information, please visit: https://www.firelab.org/project/wildfire-hazard-potential. This dataset can be downloaded from this website: https://doi.org/10.2737/RDS-2015-0046

3

2 years ago

Estimates of State Supplemental Nutrition Assistance Program Participation Rates in 2017

This report, part of an annual series, presents estimates, by state, of the percentage of eligible persons and working poor individuals who participated in SNAP during an average month in fiscal year (FY 2017) and the two previous fiscal years.

1

2 years ago

Western Bark Beetle Strategy: Polygon (Feature Layer)

Depicts the area of activities to implement the Western Bark Beetle Strategy. Activities were self-reported by field units, and center around three main objectives: increasing safety to ensure that people and community infrastructure are protected from the hazards of falling bark beetle-killed trees and elevated wildfire potential, facilitating recovery to re-establish forests damaged by bark beetles, and cultivating resiliency to prevent or mitigate future bark beetle impacts. Metadata

7

2 years ago

Western Bark Beetle Strategy: Line (Feature Layer)

Depicts the area of activities to implement the Western Bark Beetle Strategy. Activities were self-reported by field units, and center around three main objectives: increasing safety to ensure that people and community infrastructure are protected from the hazards of falling bark beetle-killed trees and elevated wildfire potential, facilitating recovery to re-establish forests damaged by bark beetles, and cultivating resiliency to prevent or mitigate future bark beetle impacts. Metadata

7

2 years ago

Summer Meal Capacity Builder

Allows users to search for summer meal sites from the previous summer by zip code, adding “layers” of information, such as free and reduced-price lunch participation rates or area eligibility data. Potential site locations (multi-family housing units, libraries, museums, and schools) can be added to the map, and previous site locations can also be highlighted, helping to prevent site overlap. This tool can also be used to identify locations that are area eligible for participation in other Child Nutrition Programs.

1

2 years ago

Wildfire Hazard Potential, Version 2018 Classified (Image Service)

The wildfire hazard potential (WHP) is a raster geospatial product at 270-meter resolution covering all lands in the conterminous United States. It can help to inform evaluations of wildfire risk or prioritization of fuels management needs across very large landscapes (millions of acres). Our specific objective with the WHP map is to depict the relative potential for wildfire that would be difficult for suppression resources to contain. For more information, please visit: https://www.firelab.org/project/wildfire-hazard-potential. \n\nThis data publication is a second edition. The first edition (https://doi.org/10.2737/RDS-2015-0046) represents WHP mapped in 2014, depicting landscape conditions as of 2010. This second edition is the 2018 version, and depicts landscape conditions as of 2012. (See \Supplements\WHP2014_to_2018_ChangeSummary.pdf for a summary of the changes between the first and second editions of these data.)�To check for the latest version of the WHP geospatial data and map graphics, as well as documentation on the mapping process, see: https://www.firelab.org/project/wildland-fire-potential. Details about the Wildfire Hazard Potential mapping process can be found in Dillon et al. 2015. Steps described in this paper about weighting for crown fire potential have been dropped in the 2018 version due to changes to the FSim modeling products used as the primary inputs to WHP mapping. The FSim products used to create the 2018 version of WHP can be found here in Short et al. 2016. Dillon, Gregory K.; Menakis, James; Fay, Frank. 2015. Wildland fire potential: A tool for assessing wildfire risk and fuels management needs. In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 60-76. https://www.fs.usda.gov/treesearch/pubs/49429 Short, Karen C.; Finney, Mark A.; Scott, Joe H.; Gilbertson-Day, Julie W.; Grenfell, Isaac C. 2016. Spatial dataset of probabilistic wildfire risk components for the conterminous United States. Fort Collins, CO: Forest Service Research Data Archive. This dataset can be downloaded at: https://www.fs.usda.gov/rds/archive/Product/RDS-2015-0046-2

3

2 years ago

Wildfire Crisis Strategy Landscapes (Feature Layer)

The Bipartisan Infrastructure Law (a.k.a Infrastructure Investment Jobs Act) and the Inflation Reduction Act include significant funding to execute fuels mitigation projects. Regions submitted proposed project boundaries designed to address community exposure to wildfire. The Executive Leadership Team of the Forest Service selected "Landscapes" for initial investment in fiscal years 2022 and 2023. Additional landscapes may be selected for future action. This dataset documents the official boundary of the landscapes selected for fuels treatment activities in the Wildfire Crisis Strategy. The public-facing version of these boundaries is called Wildfire Crisis Strategy Landscapes. This Dataset is current from June 9, 2023 to Present.Metadata and Downloads

7

2 years ago

Western Bark Beetle Strategy: Point (Feature Layer)

Depicts the area of activities to implement the Western Bark Beetle Strategy. Activities were self-reported by field units, and center around three main objectives: increasing safety to ensure that people and community infrastructure are protected from the hazards of falling bark beetle-killed trees and elevated wildfire potential, facilitating recovery to re-establish forests damaged by bark beetles, and cultivating resiliency to prevent or mitigate future bark beetle impacts. Metadata

7

2 years ago

Watershed Condition Classification: Priority Watersheds (Feature Layer)

The Watershed Condition Classification feature class represents data on Watershed Condition on Forest Service lands in HUC12 (from the Watershed Boundary Dataset) watersheds that contain more than 5% USFS ownership. The feature class also includes data on high priority watersheds identified in the Watershed Condition Framework (WCF) process. The WCF data identifies priority watersheds, rationale for their designation as such, and information on Watershed Restoration Action Plans. The data are compiled from the NRM Watershed Condition Assesment and Tracking Tool (WCATT) application. Metadata

7

2 years ago

United States annual state-level population estimates from colonization to 1999

The U.S. landscape has undergone substantial changes since Europeans first arrived. Many land use changes are attributable to human activity. Historical data concerning these changes are frequently limited and often difficult to develop. Modeling historical land use changes may be necessary. We develop annual population series from first European settlement to 1999 for all 50 states and Washington D.C. for use in modeling land use trends. Extensive research went into developing the historical data. Linear interpolation was used to complete the series after critically evaluating the appropriateness of linear interpolation versus exponential interpolation.

3

2 years ago

USFS Analytical 2011 Tree Canopy Cover Hawaii (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

USFS Analytical 2011 Tree Canopy Cover CONUS (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse The USFS Analytical CONUS TCC 2011 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. The dataset has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 127.

3

2 years ago

Tribal Lands Ceded to the United States (Feature Layer)

Sixty-seven maps from Indian Land Cessions in the United States, compiled by Charles C. Royce and published as the second part of the two-part Eighteenth Annual Report of the Bureau of American Ethnology to the Secretary of the Smithsonian Institution, 1896-1897 have been scanned, georeferenced in JPEG2000 format, and digitized to create this feature class of cession maps. The mapped cessions and reservations included in the 67 maps correspond to entries in the Schedule of Indian Land Cessions, indicating the number and location of each cession by or reservation for the Indian tribes from the organization of the Federal Government to and including 1894, together with descriptions of the tracts so ceded or reserved, the date of the treaty, law or executive order governing the same, the name of the tribe or tribes affected thereby, and historical data and references bearing thereon, as set forth in the subtitle of the Schedule. Go to this URL for full metadata: https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.TRIBALCEDEDLANDS.xml Each Royce map was georeferenced against one or more of the following USGS 1:2,000,000 National Atlas Feature Classes contained in \NatlAtlas_USGS.gdb: cities_2mm, hydro_ln_2mm, hydro_pl_2mm, plss_2mm, states_2mm. Cessions were digitized as a file geodatabase (GDB) polygon feature class, projected as NAD83 USA_Contiguous_Lambert_Conformal_Conic, which is the same projection used to georeference the maps. The feature class was later reprojected to WGS 1984 Web Mercator (auxiliary sphere) to optimize it for the Tribal Connections Map Viewer. Polygon boundaries were digitized as to not deviate from the drawn polygon edge to the extent that space could be seen between the digitized polygon and the mapped polygon at a viewable scale. Topology was maintained between coincident edges of adjacent polygons. The cession map number assigned by Royce was entered into the feature class as a field attribute. The Map Cession ID serves as the link referencing relationship classes and joining additional attribute information to 752 polygon features, to include the following: 1. Data transcribed from Royce's Schedule of Indian Land Cessions: a. Date(s), in the case of treaties, the date the treaty was signed, not the date of the proclamation; b. Tribe(s), the tribal name(s) used in the treaty and/or the Schedule; and c. Map Name(s), the name of the map(s) on which a cession number appears; 2. URLs for the corresponding entry in the Schedule of Indian Land Cessions (Internet Archive) for each unique combination of a Date and reference to a Map Cession ID (historical references in the Schedule are included); 3. URLs for the corresponding treaty text, including the treaties catalogued by Charles J. Kappler in Indian Affairs: Laws and Treaties (HathiTrust Digital Library), executive order or other federal statute (Library of Congress and University of Georgia) identified in each entry with a reference to a Map Cession ID or IDs; 4. URLs for the image of the Royce map(s) (Library of Congress) on which a given cession number appears; 5. The name(s) of the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text, as well as the name of the present-day Indian tribe or tribes; and 6. The present-day states and counties included wholly or partially within a Map Cession boundary. During the 2017-2018 revision of the attribute data, it was noted that 7 of the Cession Map IDs are missing spatial representation in the Feature Class. The missing data is associated with the following Cession Map IDs: 47 (Illinois 1), 65 (Tennessee and Bordering States), 128 (Georgia), 129 (Georgia), 130 (Georgia), 543 (Indian Territory 3), and 690 (Iowa 2), which will be updated in the future. This dataset revises and expands the dataset published in 2015 by the U.S. Forest Service and made available through the Tribal Connections viewer, the Forest Service Geodata Clearinghouse, and Data.gov. The 2018 dataset is a result of collaboration between the Department of Agriculture, U.S. Forest Service, Office of Tribal Relations (OTR); the Department of the Interior, National Park Service, National NAGPRA Program; the U.S. Environmental Protection Agency, Office of International and Tribal Affairs, American Indian Environmental Office; and Dr. Claudio Saunt of the University of Georgia. The Forest Service and Dr. Saunt independently digitized and georeferenced the Royce cession maps and developed online map viewers to display Native American land cessions and reservations. Dr. Saunt subsequently undertook additional research to link Schedule entries, treaty texts, federal statutes and executive orders to cession and reservation polygons, which he agreed to share with the U.S. Forest Service. OTR revised the data, linking the Schedule entries, treaty texts, federal statues and executive orders to all 1,172 entries in the attribute table. The 2018 dataset has incorporated data made available by the National NAGPRA Program, specifically the Indian tribe or tribes related to each mapped cession, including the name as it appeared in the Schedule or the corresponding primary text and the name of the present-day Indian tribe or tribes, as well as the present-day states and counties included wholly or partially within a Map Cession boundary. This data replaces in its entirety the National NAGPRA data included in the dataset published in 2015. The 2015 dataset incorporated data presented in state tables compiled from the Schedule of Indian Land Cessions by the National NAGPRA Program. In recent years the National NAGPRA Program has been working to ensure the accuracy of this data, including the reevaluation of the present-day Indian tribes and the provision of references for their determinations. Changes made by the OTR have not been reviewed or approved by the National NAGPRA Program. The Forest Service will continue to collaborate with other federal agencies and work to improve the accuracy of the data included in this dataset. Errors identified since the dataset was published in 2015 have been corrected, and we request that you notify us of any additional errors we may have missed or that have been introduced. Please contact Rebecca Hill, Policy Analyst, U.S. Forest Service, Office of Tribal Relations, at rebeccahill@fs.usda.gov with any questions or concerns with regard to the data included in this dataset.

7

2 years ago

TreeMap 2016 Volume Sawlog Board Feet (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Trees Per Acre Live (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Tree Bio Mass Live rendered (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Tree Bio Mass Dead (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Stand Size Code Algorithm (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Stand Density Index (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Live Tree Height (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Live Tree Canopy Cover Pct (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Forest Type Name Field (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Forest Type Field (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Control Number (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Carbon Live Above Ground (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

Tongass Landslide Areas (Feature Layer)

This is a once-over landslide inventory of the Tongass National Forest. This inventory includes all mass wasting features, including talus slopes, snow avalanche fields, and snow avalanche chutes. Each of these are coded differently in the attributes. It may be necessary to exclude several polygons in this data set when using it to determine landslide rates. Most of these landslide polygons were digitized on the 1998 to 2010 orthophotos in GIS. Many of them were age bracketed using air photos back to the 1929 Navy Trimegon photos. It includes both field and photo interpreted landslides. Not all of the landslides included once-over completed in FY2017 were age bracketed. There is an associated Points feature class (Tongass_Landslide_Initiation) that represents the landslide initiation zone approximations. These points only exist for true landslides: debris avalanches, debris torrents, combination debris avalanches/torrents, slumps, rotation failures, and rock fall-initiated failures.�Metadata

7

2 years ago

Terrestrial Condition Assessment (TCA) Wildfire Potential (Map Service)

The percent area of a landscape analysis unit where the Wildland Fire Hazard 2020 class is High.

3

2 years ago

Terrestrial Condition Assessment (TCA) Wildfire Hazard Potential Fire Regime Groups 1 and 2 (Map Service)

The 2018 Wildland Hazard Potential (WHP) represents areas of uncharacteristic fuel buildup. The WFP is a raster geospatial product produced by the USFS Fire Modeling Institute in the Fire, Fuel, and Smoke Program. Higher values have a higher probability of high-intensity fire, with torching, crowning, and other forms of extreme fire behavior. Data used represent areas classified as high or very high hazard. The LANDFIRE Fire Regime Groups (FRG) raster dataset (US_140FRG_10252017) was used to identify moderate fire regimes (Regimes I and II). Fire regime groups represent expected historical fire regimes based on interactions between vegetation dynamics, fire spread, fire effects, and spatial context. The definitions of the regimes are outlined in the Interagency Fire Regime Condition Class Guidebook. The data for this indicator represent areas identified as having high or very high wildland hazard potential and fire regime groups I and II.

3

2 years ago

Terrestrial Condition Assessment (TCA) Tree Mortality 6 to 10 years (Map Service)

Data are derived from 2010-2014 (6-10 years) aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the years recorded in the five-year dataset are thought to have significant impacts and likely mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as very light .

3

2 years ago

Terrestrial Condition Assessment (TCA) Wildfire Hazard Potential (Map Service)

The 2018 Wildland Hazard Potential (WHP) represents areas of uncharacteristic fuel buildup. The WFP is a raster geospatial product produced by the USFS Fire Modeling Institute in the Fire, Fuel, and Smoke Program. Higher values have a higher probability of high-intensity fire, with torching, crowning, and other forms of extreme fire behavior. Data used represent areas classified as �high� or �very high� hazard.

3

2 years ago

Terrestrial Condition Assessment (TCA) Uncharacteristic Fire Severity Low (Map Service)

Identify locations that have had recent catastrophic disturbance including uncharacteristically severe wildfires resulting in needs for reforestation, landslides, or other major disturbances.

3

2 years ago

Terrestrial Condition Assessment (TCA) Tree Mortality 0 to 5 years (Map Service)

Data are derived from 2015-2019 (0 - 5 years) aerial detection surveys for tree defoliation and mortality from the USFS Forest Health Assessment and Applied Sciences Team (FHAAST) National Forest Pest Conditions Database. Polygons are created by aerial sketch mapping, and coded for defoliation and mortality, in addition to other damage codes. Defoliation and mortality layers were created from the polygon data and the attribute codes. The layers were merged to compensate for difficulties in identifying defoliation separately from mortality in hardwoods vs. conifer forests. Areas that were defoliated during three of the years recorded in the five-year dataset are thought to have significant impacts and likely mortality, so these polygons were added to the mortality layer. The layer includes areas with mortality classed as very light.

3

2 years ago

Terrestrial Condition Assessment (TCA) Ozone Chronic Exposure (Map Service)

The average of W126 which is a weighted measure of chronic (lower level) ozone exposure within a landscape analysis unit. Updated through 2018. Data used are sourced from the EPA's Air Quality System Data Mart: https://aqs.epa.gov/aqsweb/airdata/download_files.html

3

2 years ago

Terrestrial Condition Assessment (TCA) Recent Drought Impacts (Map Service)

The average moisture deficit as a z-score calculated from the reference time period of 1900-2017 and current conditions as 2017-2019. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Ozone Acute Exposure (Map Service)

The average of N100 which is the sum of hours over 100 ppb in a given month and captures peak events of high exposure or acute exposure. Updated through 2018. Data used are sourced from the EPA's Air Quality System Data Mart: https://aqs.epa.gov/aqsweb/airdata/download_files.html

3

2 years ago

Terrestrial Condition Assessment (TCA) Insect and Pathogen Hazard (Map Service)

The 2018 National Insect and Disease Risk Map (NIDRM) developed by USFS-FHAAST indicates locations where forests are stressed and susceptible to outbreaks of native and non-native insects and diseases. These include forests with overly high stand densities, and where soil/site conditions contribute to drought. The basis for assigning risk is the expectation that 25% or more of live basal area (three times the natural background rate of mortality) will die over the next 15 years due to insects and diseases.

3

2 years ago

Terrestrial Condition Assessment (TCA) Grassland Encroachment (Map Service)

The indicator uses areas identified as grasslands using LANDFIRE�s Biophysical Settings (BpS) raster dataset (US_140BPS_20180618) along with a cross-walk based on Reeves MC, Mitchell JE (2011) Extent of coterminous US rangelands: quantifying implications of differing agency perspectives. Rangel Ecol Manage 64:1�12 to identify BpS units that are likely to be grasslands during pre-European times. Presence of conifer tree species was developed using USGS National Landcover Database 2011 (NLCD) legend class �42 � Evergreen Forest�. The Evergreen forest class represents �areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.��Grassland conifer encroachment is identified where raster pixels are identified as both �grasslands� and NLCD �Evergreen Forest�

3

2 years ago

Terrestrial Condition Assessment (TCA) Nitrogen Deposition (Map Service)

The average atmospheric deposition of nitrogen from 2015-2017 to 2016-2018 within a landscape analysis unit.

3

2 years ago

Terrestrial Condition Assessment (TCA) Fire Deficit (Map Service)

The percent area of a landscape analysis unit identified as having a fire deficit by comparing modern fire occurrence (MTBS: 1984-2017) with historical fire rotations (LANDFIRE Mean Fire Return Intervals).

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Temperature Winter (Map Service)

The difference in Winter temperature (F) between the reference time period of 1900-2014 and the current time period 2015-2019. Winter months include December, January, and February. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

USDA Rural Development Multi Family Housing - March 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for March 2016.

1

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Temperature Summer (Map Service)

The difference in Summer temperature (F) between the reference time period of 1900-2014 and the current time period 2015-2019. Fall months include June, July, August. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

USDA Rural Development Multi Family Housing - January 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for January 2016.

1

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Temperature Fall (Map Service)

The difference in Fall temperature (F) between the reference time period of 1900-2014 and the current time period 2015-2019. Fall months include September, October, and November. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Winter (Map Service)

The difference in Winter precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Winter months include December, January, and February. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Summer (Map Service)

The difference in Summer precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Summer months include June, July, August. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

USDA Rural Development Multi Family Housing - April 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for April 2017.

1

2 years ago

USDA Rural Development (RD) - Obligations by Location

These ESRI shapefiles show spatial data, points on a map. In addition, shapefiles provide attribute data for each point. Shapefile’s attribute data include spatial information such as latitude and longitude, the address, and obligation amount.

1

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Spring (Map Service)

The difference in Spring precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Spring months include March, April, May. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Temperature Spring (Map Service)

The difference in Spring temperature (F) between the reference time period of 1900-2014 and the current time period 2015-2019. Fall months include March, April, May. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Winter Percent (Map Service)

The percent difference in Winter precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Winter months include December, January, and February. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - September 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for September 2016.

1

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Summer Percent (Map Service)

The percent difference in Summer precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Summer months include June, July, August. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Spring Percent (Map Service)

The percent difference in Spring precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Spring months include March, April, May. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Fall Percent (Map Service)

The percent difference in Fall precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Fall months include September, October, and November. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Climate Exposure Precipitation Fall (Map Service)

The difference in Fall precipitation (in) between the reference time period of 1900-2014 and the current time period 2015-2019. Fall months include September, October, and November. Data used are sourced from PRISM, Oregon State University. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Uncharacteristic Fire Severity Moderate (Map Service)

The percent area of a landscape analysis unit where the LANDFIRE Percent Low Severity (PLS) dataset predicted low severity fires and Monitoring Trends and Burn Severity (MTBS) mapped either moderate or high severity fires occurring 1984-2017.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Tree Mortality 6 to 10 years (Map Service)

The percent area of a landscape analysis unit that has been identified as having tree mortality or defoliation 3 out of 5 years from USFS Forest Health Areal Detection Surveys (2010-2014).

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Insect and Disease Hazard (Map Service)

The percent area of a landscape analysis unit where the USFS FHAAST Insect and Disease Risk Map (NIDRM 2018) is at risk.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Drought (Map Service)

TCA AK drought data represent areas of recent drought stress based on the U.S. Drought Monitor data (https://droughtmonitor.unl.edu/) for two time periods: January 6, 2015 - December 26, 2017 and September 20, 2016 � September 24th, 2019. Using a 30 km grid as a framework over Alaska, the number of drought polygons are intersected for each grid cell, in each drought category, for each time period. This information is then summarized for each time period using the following weighting scheme: (D0 * 1) + (D1 *2) + (D2 * 3) + (D3 * 4) + (D4 * 5). Higher values indicate higher drought intensity and duration.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Uncharacteristic Fire Severity Low

The percent area of a landscape analysis unit where the LANDFIRE Percent Low Severity (PLS) dataset predicted low severity fires and Monitoring Trends and Burn Severity (MTBS) mapped either moderate or high severity fires occurring 1984-2017.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Tree Mortality 0 to 5 years (Map Service)

The percent area of a landscape analysis unit that has been identified as having tree mortality or defoliation 3 out of 5 years from USFS Forest Health Areal Detection Surveys (2015-2019).

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Fire Deficit (Map Service)

The percent area of a landscape analysis unit identified as having a fire deficit by comparing modern fire occurrence (MTBS: 1984-2017) with historical fire rotations (LANDFIRE Mean Fire Return Intervals).

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Climate Exposure Temperature Winter (Map Service)

The difference in Winter temperature (F) between the reference time period of 1980-2014 and the current time period 2015-2019. Winter months include December, January, and February. Data used are sourced from DAYMET, Daily Surface Weather and Climatological Summaries, Oak Ridge National Laboratory. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Climate Exposure Temperature Spring (Map Service)

The difference in Spring temperature (F) between the reference time period of 1980-2014 and the current time period 2015-2019. Winter months include March, April, and May. Data used are sourced from DAYMET, Daily Surface Weather and Climatological Summaries, Oak Ridge National Laboratory. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Climate Exposure Temperature Summer (Map Service)

The difference in Summer temperature (F) between the reference time period of 1980-2014 and the current time period 2015-2019. Summer months include June, July, and August. Data used are sourced from DAYMET, Daily Surface Weather and Climatological Summaries, Oak Ridge National Laboratory. Data were summarized at the Subsection scale of the USFS National Hierarchy of Ecological Units and applied to the corresponding LTA.

3

2 years ago

Survey parcels described by metes and bounds (Feature Layer)

A depiction of a survey parcel described by a metes and bounds description. Examples include: land lots, housing subdivision lots, mineral surveys, and homestead entry surveys. Metadata

7

2 years ago

Stewardship Contracting: Polygon (Feature Layer)

Depicts the area of activities within Stewardship Contracting Project Boundary. Activities are implemented through stewardship contracts or agreements and are self-reported by Forest Service Units through the FACTS database. Metadata

7

2 years ago

Surface Ownership Parcels (Feature Layer)

An area depicted as surface ownership parcels dissolved on the same ownership classification. Metadata

7

2 years ago

Stewardship Contracting: Point (Feature Layer)

Depicts the locations of activities within Stewardship Contracting Project Boundary. Activities are implemented through stewardship contracts or agreements and are self-reported by Forest Service Units through the FACTS database. Metadata

7

2 years ago

Roadless Areas: 2001, Idaho, and Colorado Rules Combined (Feature Layer)

This feature class describes the boundaries of all Roadless Areas managed by the US Forest Service. These roadless areas were designated administrative rulemaking to provide management direction for their conservation and management. The Roadless Area Conservation Rule of 2001 designated roadless areas nationwide. Subsequent rules, the Idaho Roadless Rule of 2008, and the Colorado Roadless Rule of 2012 replaced that direction and designation in the states of Idaho and Colorado. Metadata

7

2 years ago

Listing of Dairy License Recipients Under Regulation 1

Listing of Dairy License Recipients Under Regulation 1 (as of March 2016)

1

2 years ago

Raw urban street tree inventory data for 49 California cities

This data publication contains urban tree inventory data for 929,823 street trees that were collected from 2006 to 2013 in 49 California cities. Fifty six urban tree inventories were obtained from various sources for California cities across five climate zones. The five climate zones were based largely on aggregation of Sunset National Garden Book's 45 climate zones. Forty-nine of the inventories fit the required criteria of (1) included all publicly managed trees, (2) contained data for each tree on species and diameter at breast height (dbh) and (3) was conducted after 2005. Tree data were prepared for entry into i-Tree Streets by deleting unnecessary data, matching species to those in the i-Tree database, and establishing dbh size classes. Data included in this publication include tree location (city, street name and number), diameter at breast height, species name and/or species code, and tree type.

3

2 years ago

Rapid Assessment of Vegetation Condition: Perimeters (Feature Layer)

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually).

7

2 years ago

Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Composite Burn Index (Map Service)

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ('initial assessments'). Late-season fires, however, may be deferred until the following spring or summer ('extended assessments'). National mosaics of each thematic product are prepared annually. Mosaics of extended assessments, if any, are provided separately from initial assessment mosaics. This map service includes annual raster mosaics of published CBI-4 datasets for fires that burned during calendar years 2013 through 2020, excluding 2020 extended assessments. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php).

3

2 years ago

Ranger District Boundaries (Feature Layer)

A depiction of the boundary that encompasses a Ranger District. Metadata

7

2 years ago

Range: Pasture (Feature Layer)

Designates boundaries to establish extent of livestock distribution and management within pastures. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.

7

2 years ago

Range Vegetation Improvement (Feature Layer)

The Range Vegetation Improvement feature class depicts the area planned and accomplished areas treated as a part of the Range Vegetation Improvement program of work, funded through the budget allocation process and reported through the Forest Service Activity Tracking System (FACTS) database within the Natural Resource Manager (NRM) suite of applications. Activities are self-reported by Forest Service Units. Metadata

7

2 years ago

Purchase Units under the Weeks Law (Feature Layer)

A unit designated by the Secretary of Agriculture or previously approved by the National Forest Reservation Commission for purposes of Weeks Law acquisition. Metadata

7

2 years ago

Stewardship Contracting: Line (Feature Layer)

Depicts the linear activities within Stewardship Contracting Project Boundary. Activities are implemented through stewardship contracts or agreements and are self-reported by Forest Service Units through the FACTS database. Metadata

7

2 years ago

Rapid Assessment of Vegetation Condition: Metrics (Feature Layer)

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually). This current dataset is derived from the combined perimeter dataset and adds spatial information about land ownership (National Forest) and wilderness status, as well as the areal extent of forested land (pre-fire) that experience a modeled BA loss above 50 and 75 percent.

7

2 years ago

Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Canopy Cover Percent Change (Map Service)

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ('initial assessments'). Late-season fires, however, may be deferred until the following spring or summer ('extended assessments'). National mosaics of each thematic product are prepared annually. Mosaics of extended assessments, if any, are provided separately from initial assessment mosaics. This map service includes annual raster mosaics of published CC-5 datasets for fires that burned during calendar years 2013 through 2020, excluding 2020 extended assessments. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php).

3

2 years ago

Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Basal Area Percent Change (Alaska) (Map Service)

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ('initial assessments'). Late-season fires, however, may be deferred until the following spring or summer ('extended assessments'). National mosaics of each thematic product are prepared annually. Mosaics of extended assessments, if any, are provided separately from initial assessment mosaics. This map service includes annual raster mosaics of published BA-7 datasets for fires that burned during calendar years 2019 in Alaska. The associated burned area perimeters are available via the Enterprise Data Warehouse.

3

2 years ago

Range: Wild Horse and Burro Territory (Feature Layer)

Designates boundaries to establish extent of distribution and management of Wild Horse and Burro territories. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.

7

2 years ago

Range: Allotment (Feature Layer)

Designates boundaries to establish extent of livestock distribution and management within the allotment. This is a published layer created by combining GIS data managed by each National Forest and attribute data stored in the Forest Service Infra database application. This dataset is designed for reporting and analysis and is not used to enter or edit data.

7

2 years ago

Public and Private Forest Ownership Conterminous United States (Map Service)

The data are designed for strategic analyses at a national or regional scale which require spatially explicit information regarding the extent, distribution, and prevalence of the ownership types represented. The data are not recommended for tactical analyses on a sub-regional scale, or for informing local management decisions. Furthermore, map accuracies vary considerably and thus the utility of these data can vary geographically under different ownership patterns.

3

2 years ago

Public Land Survey Monument (Feature Layer)

A land survey point from a GCDB LX file, survey plat, or captured from a CFF land net coverage. Includes points generated by calculating an aliquot breakdown of a section.

7

2 years ago

Projected Change in Average Number of Days of Precipitation (Map Service)

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

3

2 years ago

Public Land Survey System Townships (Feature Layer)

An area defined by the Public Lands Survey System grid that is referenced by its tier and range numbers, and is normally a rectangle approximately 6 miles on a side with boundaries conforming to meridians and parallels. Metadata

7

2 years ago

Public Land Survey System Quarter Sections (Feature Layer)

This Quarter Section feature class depicts PLSS Second Divisions . PLSS townships are subdivided in a spatial hierarchy of first, second, and third division. These divisions are typically aliquot parts ranging in size from 640 acres to 160 to 40 acres, and subsequently all the way down to 2.5 acres. The data in this feature class was translated from the PLSSSecondDiv feature class in the original production data model, which defined the second division for a specific parcel of land. Metadata

7

2 years ago

Public Land Survey Corner (Feature Layer)

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. A land survey point from a GCDB LX file, survey plat, or captured from a CFF land net coverage. Includes points generated by calculating an aliquot breakdown of a section.

9

2 years ago

Productivity of U.S. Rangelands, Annual Data lbs/acre (Map Service)

Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2021 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values.This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources.The Rangeland Productivity data can be downloaded here:https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php

3

2 years ago

Productivity of U.S. Rangelands, Annual Data Z-scores (Image Service)

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select any of the download options. Data can also be downloaded from the FSGeodata Clearinghouse.More information about rangeland productivity and the effects of drought are available in this StoryMap; additional drought and rangeland products from the Office of Sustainability and Climate are available in our Climate Gallery.Time enabled image service showing estimates of annual production of rangeland vegetation.Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2021 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications. This Z-scores dataset as well as the raw lbs/acre data that the Z-scores were derived from can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.phpMore information about rangeland productivity and the effects of drought are available in this story map.

7

2 years ago

Periodical Cicada Broods (Feature Layer)

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the geodatabase option. Data layer depicting periodical cicada distribution and expected year of emergence by cicada brood and county. The periodical cicada emerges in massive groups once every 13 or 17 years and is completely unique to North America. There are 15 of these mass groups, called broods, of periodical cicadas in the United States. This county-based data, complied by the USFS Northern Research Station, depict where and when the different broods of periodical cicadas are likely to emerge in the US through 2037. The data was compiled for the 2011 publication entitled 'Avian predators are less abundant during periodical cicada emergences, but why?' (Koenig et al. https://dx.doi.org/10.1890/10-1583.1) using data from periodical cicada publications listed below. 1) Marlatt, C. L. 1907. 'The periodical cicada'. Bulletin of the USDA Bureau of Entomology 71:1?181. 2) Simon, C. 1988. 'Evolution of 13- and 17-year periodical cicadas'. (Homoptera: Cicadidae). Bulletin of the Entomological Society of America 34:163?176. 3) Liebhold, A. M., Bohne, M. J., and R. L. Lilja. 2013. 'Active Periodical Cicada Broods of the United States'. USDA Forest Service Northern Research Station, Northeastern Area State and Private Forestry. Metadata and Downloads

7

2 years ago

Percent change in winter precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Percent change in summer precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Parcels withdrawn from settlement, sale, mineral location, and/or entry (Feature Layer)

Federal land parcels that are withdrawn from settlement, sale, location, or entry under some or all of the general land and mineral laws in order to maintain other public values or purposes. A withdrawal area has one or more associated segregations. A segregation is a specific activity from which the area has been withdrawn such as settlement, sale, location, or entry. Metadata

7

2 years ago

PADUS FS Proclaimed NF and National Grassland Boundaries (Feature Layer)

An area depicting designated land boundaries which are designated by proclamation. This data is intended for read-only use. The PAD-US feature classes were developed by the Forest Service for submission to the Protected Areas Database of the United States (PAD-US). It is the official inventory of public parks and other protected open space. With more than 3 billion acres in 150,000 holdings, the spatial data in PAD-US represents public lands held in trust by thousands of national, State and regional/local governments, as well as non-profit conservation organizations. PAD-US is published by the U.S. Geological Survey Gap Analysis Program (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity, conservation, recreation, public health, climate change adaptation, and infrastructure investment. Metadata

7

2 years ago

PADUS FS Managed Surface Ownership Parcels (Feature Layer)

An area depicting ownership parcels of the surface estate. This data is intended for read-only use. The PAD-US feature classes were developed by the Forest Service for submission to the Protected Areas Database of the United States (PAD-US). It is the official inventory of public parks and other protected open space. With more than 3 billion acres in 150,000 holdings, the spatial data in PAD-US represents public lands held in trust by thousands of national, State and regional/local governments, as well as non-profit conservation organizations. PAD-US is published by the U.S. Geological Survey Gap Analysis Program (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity, conservation, recreation, public health, climate change adaptation, and infrastructure investment. USFS PAD-US data is pulled weekly from USFS Lands data. This dataset is more current than the combined annual update of PAD-US from USGS GAP.

7

2 years ago

Other Surface Right (Feature Layer)

An area depicting a right to a surface resource, excluding rights of way. Metadata

7

2 years ago

Number of Frost-Free Days (Map Service)

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

3

2 years ago

Other Sub Surface Right (Feature Layer)

An area depicting ownership parcels of the subsurface estate, excluding mineral rights; it is collected only if the subsurface estate is different than the overlying surface estate. Metadata

7

2 years ago

National Wilderness Areas (Feature Layer)

A parcel of Forest Service land congressionally designated as wilderness such as National Wilderness Area. Click this link for full metadata description: Metadata

7

2 years ago

National Wilderness Areas: Legal Status (Feature Layer)

The Land Status view of National Forest System land parcels that have legal descriptions such as National Wilderness Area, Primitive Area, or Wilderness Study Area. Areas designated by Congress as a part of the National Wilderness Preservation System, with related details including the date of the designation, status of the final boundary description, authority, and land status case and document. Metadata

7

2 years ago

National Wild and Scenic River Eligible and Suitable Segments (Feature Layer)

This vector line dataset represents the river center line that are eligible, eligible and suitable, and eligible and not suitable for designation as a National Wild and Scenic River within the contiguous United States, Alaska, Hawaii, and Puerto Rico.The data was designed for mapping and analysis. This should be used as a companion to the LSRS data.

7

2 years ago

National Wild and Scenic River Lines (Feature Layer)

This polyline feature class depicts the river corridors of each Wild and Scenic River designated by Congress or the Secretary of the Interior for the United States and Puerto Rico. This GIS data layer was created from a mulit-agency effort by the US Forest Service, National Park Service, Bureau of Land Managment, and the US Fish and Wildlife Servce. The spatial data were referenced to the latest High Resolution National Hydrological Data Layer (NHD 1:24,000 Scale or better), published by United States Geological Survey (USGS). Metadata

7

2 years ago

National Wild and Scenic River Eligible and Suitable Lines (Feature Layer)

This vector line dataset represents the river center line that are eligible, eligible and suitable, and eligible and not suitable for designation as a National Wild and Scenic River within the contiguous United States, Alaska, Hawaii, and Puerto Rico.The data was designed for mapping and analysis. This should be used as a companion to the LSRS data.

7

2 years ago

National Wild and Scenic River Active Study Rivers Lines (Feature Layer)

This data set represents the Wild and Scenic Rivers Active Study Rivers Lines

7

2 years ago

National Forest System Roads (Feature Layer)

Existing Forest Service roads with attributes representing their characteristics. This feature layer includes only open Forest Service Roads. Forest Service roads closed to motorized uses can be found here.

7

2 years ago

National Forest Lands with Nationally Designated Management or Use Limitations: Legal Status (Feature Layer)

The legal status of the area depicting National Forest System land parcels that have management or use limits placed on them by legal authority above the Agency level (e.g. Congress and/or President). Areas that have been designated by Congress, Executive Order, Presidential Proclamation, or an Executive branch Department, excluding National Wilderness and National Wild & Scenic Rivers, with related details including the date of the designation, status of the final boundary description, authority, and land status case and document information. Metadata

7

2 years ago

National Forest System Roads closed to motorized uses (Feature Layer)

Existing Forest Service closed roads with attributes representing their characteristics. This feature layer includes only closed Forest Service Roads.

7

2 years ago

Global Agricultural Information Network

Information reports on agricultural situations in more than 130 countries submitted by overseas offices of USDA's Foreign Agricultural Service

1

2 years ago

MTBS Wildfire Burn Severity Mosaics

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.�Map Services

3

2 years ago

Land and Water Conservation Fund Parcels (Feature Layer)

This data is intended for read-only use. Land and Water Conservation Fund (LWCF) data from surface ownership fund table is attached to surface ownership to create a base layer that is used in Forest Service business functions, as well as by other entities such as states, counties, other agencies, and partners. This layer depicts only the Forest Service lands that are acquired through purchase, exchange, donation, and transfer that used LWCF-designated funds. It is not a complete representation of all Forest Service land acquisitions; only those that used LWCF-designated funds. Metadata and Downloads

7

2 years ago

Land Management Planning Unit (Feature Layer)

The Land Management Planning Unit (LMPU) feature class displays the plan revision status for FS land management planning units, their boundaries, FS Region, planning phase milestone and associated date, and link to a related planning website. A land management plan provides a framework for integrated resource management and for guiding project and activity decision-making on a nationalforest, grassland, prairie, or other administrative unit. New plan development is required for new NFS units; an existing plan may be amended at any time.

7

2 years ago

Historical summer temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical summer precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).\n\n

3

2 years ago

Historical annual temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical annual precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical and future temperature trends (Map Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical and future precipitation trends (Map Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical Temperature Change (Map Service)

Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data. Download this data or get more information

3

2 years ago

Historical annual precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Historical and future snow trends (Map Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nSnow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127).\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Terrestrial Condition Assessment (TCA) Alaska Wildfire Potential (Map Service)

The percent area of a landscape analysis unit where the Wildland Fire Hazard 2020 class is High.

3

2 years ago

Historical April 1 snow water equivalent (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Heat Stress Index (Map Service)

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data. Download this data or get more information

3

2 years ago

Hazardous sites (Feature Layer)

Polygons representing FS land areas with a regulated use specification authorized by the Comprehensive Environmental Response, Compensation, and Liability Act of 1980. These areas generally contain hazardous waste considerations. Metadata

7

2 years ago

Great Basin Montane Watersheds - Regions (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Great Basin Montane Watersheds - Mountain Ranges (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Great Basin Montane Watersheds - Heads (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Geopolitical Units adjusted within Administrative Forest Boundaries: State FS revised 2020 Census (Feature Layer)

This featureclass includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration). Metadata

7

2 years ago

Geopolitical Units adjusted within Administrative Forest Boundaries: County FS revised 2020 Census (Feature Layer)

This featureclass includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration). Metadata

7

2 years ago

Landscape Change Monitoring System Conterminous United States Year of Highest Probability of Slow Loss (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Hazardous Fuel Treatment Reduction: Line (Feature Layer)

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents activities of hazardous fuel treatment reduction that are polygons. All accomplishments toward the unified hazardous fuels reduction target must meet the following definition: Vegetative manipulation designed to create and maintain resilient and sustainable landscapes, including burning, mechanical treatments, and/or other methods that reduce the quantity or change the arrangement of living or dead fuel so that the intensity, severity, or effects of wildland fire are reduced within acceptable ecological parameters and consistent with land management plan objectives, or activities that maintain desired fuel conditions. These conditions should be measurable or predictable using fire behavior prediction models or fire effects models. Metadata

7

2 years ago

Great Basin Montane Watersheds - Watersheds (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Great Basin Montane Watersheds - Streams (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Great Basin Montane Watersheds - Pour Points (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Great Basin Montane Watersheds - Longest Stream (Feature Layer)

Multiple research and management partners collaboratively developed a multiscale approach for assessing the geomorphic sensitivity of streams and ecological resilience of riparian and meadow ecosystems in upland watersheds of the Great Basin to disturbances and management actions. The approach builds on long-term work by the partners on the responses of these systems to disturbances and management actions. At the core of the assessments is information on past and present watershed and stream channel characteristics, geomorphic and hydrologic processes, and riparian and meadow vegetation. In this report, we describe the approach used to delineate Great Basin mountain ranges and the watersheds within them, and the data that are available for the individual watersheds. We also describe the resulting database and the data sources. Furthermore, we summarize information on the characteristics of the regions and watersheds within the regions and the implications of the assessments for geomorphic sensitivity and ecological resilience. The target audience for this multiscale approach is managers and stakeholders interested in assessing and adaptively managing Great Basin stream systems and riparian and meadow ecosystems. Anyone interested in delineating the mountain ranges and watersheds within the Great Basin or quantifying the characteristics of the watersheds will be interested in this report. For more information, visit: https://www.fs.usda.gov/research/treesearch/61573Metadata and Downloads

7

2 years ago

Geopolitical Units adjusted within Administrative Forest Boundaries: Recording District (Feature Layer)

This featureclass includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration). Metadata

7

2 years ago

Geopolitical Units adjusted within Administrative Forest Boundaries: Congressional Districts FS revised 2020 Census (Feature Layer)

This featureclass includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration). Metadata

7

2 years ago

Public and Private Forest Ownership Conterminous United States (Image Service)

This data product contains raster data depicting the spatial distribution of forest ownership types in the conterminous United States circa 2020. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local, as well as thre types of private: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, unincorporated partnerships and associations, and Native American tribal lands).

3

2 years ago

GAOA Project Summary (Feature Layer)

This dataset displays the approximate location of US Forest Service, Great American Outdoors Act (GAOA) projects. The data is refreshed on a nightly basis from the US Forest Service database of infrastructure projects which is stewarded by the individual National Forests and Grasslands. This dataset is a spatial data layer of points representing the approximate or general location where the project takes place. The point location is intended for use in small scale maps to indicate the general location of the projects across the country. The location data is maintained by staff on the individual National Forest or Grassland using the database of record. Because a project can be made up of many assets distributed across a land area, a single project location point will not always reflect the specific location and extent of the work in the project. The project detail data can be used to display the individual assets that make up the project. For more information about Forest Service GAOA projects visit our website: https://www.fs.usda.gov/managing-land/gaoaMetadata and Downloads

7

2 years ago

GAOA Project Detail LN (Feature Layer)

This dataset contains the detailed information about the individual asset linear features such as roads and trails that make up Great American Outdoors Act (GAOA) projects. This data can be used together with the project summary data to display general project locations. The data is refreshed on a nightly basis from the US Forest Service database of infrastructure projects which is stewarded by the individual National Forests and Grasslands.\nFor more information about Forest Service GAOA projects visit our website: https://www.fs.usda.gov/managing-land/gaoaMetadata and Downloads

7

2 years ago

Landscape Change Monitoring System Conterminous United States Year of Highest Probability of Fast Loss (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Geopolitical Units adjusted within Administrative Forest Boundaries: Civil Division (Feature Layer)

This featureclass includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration). Metadata

7

2 years ago

GAOA Project Detail PT (Feature Layer)

This dataset contains the detailed information about the individual asset point features such as recreation sites, that make up Great American Outdoors Act (GAOA) projects. This data can be used together with the project summary data to display general project and asset locations. The data is refreshed on a nightly basis from the US Forest Service database of infrastructure projects which is stewarded by the individual National Forests and Grasslands. \nFor more information about Forest Service GAOA projects visit our website: https://www.fs.usda.gov/managing-land/gaoaMetadata and Downloads

7

2 years ago

Public Land Survey System Sections (Feature Layer)

An area defined by the Public Lands Survey System Grid. Normally, 36 sections make up a township. Metadata

7

2 years ago

Future winter temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future winter precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future summer temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future summer precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future snow residence time (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future annual temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).\n\n

3

2 years ago

Future annual precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Landscape Change Monitoring System Conterminous United States Most Recent Year of Gain (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Landscape Change Monitoring System Conterminous United States Land Use (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Landscape Change Monitoring System Conterminous United States Annual Change (Image Service)

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS modeled change classes for each year. See additional information about change in the Entity_and_Attribute_Information section below. LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a 'best available' map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades. Predictor layers for the LCMS model include annual Landsat and Sentinel 2 composites, outputs from the LandTrendr and CCDC change detection algorithms, and terrain information.These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). The raw composite values, LandTrendr fitted values, pair-wise differences, segment duration, change magnitude, and slope, and CCDC September 1 sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences, along with elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the National Elevation Dataset (NED), are used as independent predictor variables in a Random Forest (Breiman, 2001) model. Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss, fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the Landsat time series and serve as the foundational products for LCMS.

3

2 years ago

Forest Service developed sites subject to regulation (Feature Layer)

An area having regulations and/or restrictions related to existing buildings, structures, or resource activities such as a constructed fuel breaks. Metadata

7

2 years ago

Forest Service Regional Boundaries (Feature Layer)

An area encompassing all the National Forest System lands administered by a Region. The area encompasses private lands, other governmental agency lands. All National Forest System lands fall within one and only one Administrative Region Area. Metadata

7

2 years ago

Land Utilization (Feature Layer)

A unit designated by the Secretary of Agriculture for conservation and utilization under Title III of the Bankhead-Jones Farm Tenant Act.

7

2 years ago

Forest Stewardship Program Federal Investment Areas (Image Service)

With the Food, Conservation, and Energy Act of 2008 (the 2008 Farm Bill), Congress tasked states and territories to craft assessments of the forests within their boundaries and develop strategies to address threats and forest management opportunities. Now known as Forest Action Plans, these assessments and strategies provide an analysis of forest conditions and trends in the state and delineate priority forest landscape areas. They offer long-term plans for investing state, federal, and other resources where they can be most effective in achieving national conservation goals by addressing the State and Private Forestry (SPF) national priorities and objectives: 1) Conserve working forest lands, 2) Protect forests from harm, and 3) Enhance public benefits from trees and forests. Administered by the US Forest Service and implemented by State forestry agencies, the SPF Forest Stewardship Program encourages private forest landowners to manage their lands using professionally prepared Forest Stewardship plans. Participation in the Forest Stewardship Program requires that states and territories submit a raster dataset of priority areas specific to the Program - aligned with priority landscapes identified in Forest Action Plans - called Forest Stewardship Program Federal Investment Areas, where they will focus their Program delivery efforts. Program performance measures include acres covered by active Forest Stewardship plans that are within Forest Stewardship Priority Areas.

3

2 years ago

Forest Service Research Data Archive - Index

This archive publishes and preserves short and long-term research data collected from studies funded by:Forest Service Research and Development (FS R&D)Joint Fire Science Program (JFSP)Aldo Leopold Wilderness Research Institute (ALWRI)Of special interest, our collection includes data from a number of Forest Service Experimental Forests and Ranges.Each archived data set (i.e., 'data publication') contains at least one data set, complete metadata for the data set(s), and any other documentation the researcher deemed important to understanding the data set(s). The data catalog entries present the metadata and a link to the data. In some cases the data link is to a different archive.

3

2 years ago

Forest Service Office Locations (Feature Layer)

This data includes offices where Forest Service employees work or where IT equipment is housed. There is no Personally Identifiable Information (PII) data in this dataset, nor telework locations. It includes owned, leased and shared offices. Shared offices are buildings owned or leased by another entity (i.e. a university, other federal agency, etc.) but one or more Forest Service employee(s) work at the building or IT equipment is housed at the building.Depicts the spatial locations for Office locations from the Forest Service CIO Asset Management Office. It includes owned, leased and shared offices. Data is collected, maintained and stewarded by the CIO Asset Management Office. EDW data loading tools extract the office location data from the CIO Asset Mgt. database. Latitude and longitude values are validated and then converted to spatial point data. Spatial point data and associated attributed data describing the office location are inserted into the Office Location Feature class in the Enterprise Data Warehouse. Changes to the Office Location data are checked daily by EDW data loading tools. Data is updated weekly. Data is visible at all scales and zoom levels. Metadata and Downloads.

7

2 years ago

Crop Explorer

Global crop production analysis

1

2 years ago

Forest Health Protection Tree Species Metrics

Natural resource professionals responsible for managing natural resources need detailed resource information to make reasonable assessments of natural resource conditions. The dataset consists of location and density of 300+ individual species mapped at a 30-m resolution, wall-to-wall, and allows the NR professional the ability to go beyond local resource boundaries to characterize landscapes and other resource objectives.

3

2 years ago

Forest Health Protection Insect and Disease Survey

The applications available on this portal access a myriad of state, county and local level forest insect and disease conditions data. In addition it offers a window into near real time forest disturbance information collected from space. Data input applications are restricted to cooperators with specific training and expertise. If you need access to an application not listed in this portal please contact us.

3

2 years ago

Forest Health Advisory System

Healthy forests not only provide a beautiful setting for our outdoor activities, they are at lower risk for catastrophic wild fires, and are more resilient to changes in climate and insect and disease attack.Many forest pests are part of the natural environment. However, as our nation's forests grow older and more dense, they are at greater risk of attack and new invasive pests can become established. Fortunately, we have projections which can identify tree species at risk of attack well ahead of time. Armed with this and other local information we can be proactive about protecting and restoring our forests to a healthy state. By planting new trees, removing unhealthy trees, and limiting the spread of invasive forest pests, we can ensure our nation's forests remain healthy for future generations.

3

2 years ago

Forest Administrative Boundaries (Feature Layer)

An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area. Click this link for full metadata description: Metadata

7

2 years ago

FSTopo topographic quadrangle - Map products

FSTopo is the Forest Service Primary Base Map Series (1:24,000 scale for the lower 48 and Puerto Rico, 1:63,360 for Alaska) quadrangle maps. FSTopo products cover the US Forest Service lands. Quadrangle maps not included in the FSTopo Series may be obtained from the Map Locator & Downloader on the USGS Store or the USGS TNM Download

3

2 years ago

FIRESTAT Fire Occurrence - Yearly Update (Feature Layer)

The FIRESTAT (Fire Statistics System) Fire Occurrence point layer represents ignition points, or points of origin, from which individual wildland fires started on National Forest System lands. The source is the FIRESTAT database, which contains records of fire occurrence, related fire behavior conditions, and the suppression actions taken by management taken from the Individual Wildland Fire Report. This publicly available dataset is updated annually for all years previous to January 1 on or after February 16th.

7

2 years ago

FIA Landcover County Estimates - 2015 (Feature Layer)

This feature class represents forest area estimates (and percent sampling error) by county for the year 2015. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

7

2 years ago

Ecological Subsections (Feature Layer)

This data set includes polygons for ecological subsections within Subregions within the conterminous United States. This data set contains regional geographic delineations for analysis of ecological relationships across ecological units. Metadata

7

2 years ago

Ecological Provinces (Feature Layer)

This data set includes polygons for ecological provinces within the conterminous United States. This data set contains regional geographic delineations for analysis of ecological relationships across ecological units. Metadata

7

2 years ago

Dry Days (Map Service)

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Average historical temperature change, between 1948-1968 and 1996-2016 averages, in Celsius. Calculated using averages of minimum and maximum monthly values during these time periods. Values are based on TopoWx data. Download this data or get more information

3

2 years ago

Drought and Moisture Surplus for the Conterminous United States, Annual Data 3-Year Windows (Image Service)

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option; the data can also be downloaded directly from the FSGeodata Clearinghouse. To summarize this dataset by U.S. Forest Service Lands, see the Drought Summary Tool. You can also explore cumulative drought and moisture changes from this StoryMap; additional drought products from the Office of Sustainability and Climate are available in our Climate Gallery and the OSC Drought page.The Moisture Deficit and Surplus map uses moisture difference z-score datasets developed by scientists Frank Koch, John Coulston, and William Smith of the Forest Service Southern Research Station. A z-score is a statistical method for assessing how different a value is from the mean (average). Mean moisture values were derived from historical data on precipitation and potential evapotranspiration, from 1900 to 2022. The greater the z-value, the larger the departure from average conditions, indicating larger moisture deficits or surpluses. Thus, the dark red areas on this map indicate a three-year period with extremely dry conditions, relative to the average conditions over the past century. For further reading on the methodology used to build these maps, see the publication here: https://www.fs.usda.gov/treesearch/pubs/43361

3

2 years ago

Date of Thaw (Map Service)

Date of thaw for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

3

2 years ago

Experimental Forest and Range Locations (Feature Layer)

This point feature class contains the locations of all 87 experimental forests, ranges and watersheds, including cooperating experimental areas. Metadata.

9

2 years ago

Date of Freeze (Map Service)

Date of freeze for historical (1985-2005) and future (2071-2090, RCP 8.5) time periods, and absolute change between them, based on analysis of MACAv2METDATA. Download this data or get more information

3

2 years ago

Daily black carbon emissions data from fires in Northern Eurasia for 2002-2015

This data publication, the Fire Emission Inventory - Northern Eurasia (FEI-NE), consists of a high spatial resolution (500 meter ? 500 meter) dataset of daily black carbon (BC) emissions from forest, grassland, shrubland, and savanna fires in Northern Eurasia from 2002 to 2015. BC emissions were estimated using land cover maps and detected burned areas based on MODIS (MODerate Resolution Imaging Spectroradiometer) remote sensing products, the Forest Inventory Survey of the Russian Federation, the IPCC Tier-1 Global Biomass Carbon Map for the year 2000, and cover type specific BC emission factors. The data publication includes land cover type, fuel loading, and fuel consumption which are input for the model used to estimate BC emissions. These data provide daily emission sources for the assessment of the transport and deposition of BC on Arctic ice and snow.

3

2 years ago

Current Invasive Plants (Feature Layer)

The Current Invasive Plants (InvasivePlantCurrent) feature class contains only the most recent or latest invasive Plant Infestation polygons collected by the National Invasive Plant Inventory Protocol. Includes most recent and excludes historic observations. Includes Site ID, Plant code, status etc. for the infesting species, date, area and other basic data. Metadata

7

2 years ago

Collaborative Forest Landscape Restoration Project Boundaries (Feature Layer)

Depicts the boundaries for the Collaborative Forest Landscape Restoration (CFLR) and High Priority Restoration (HRP) projects. Metadata

7

2 years ago

Climate Shield Cutthroat Trout (0% Brook Trout), 2040 (Feature Layer)

This feature class represents the mid-century (2030-2059) scenario for cutthroat trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields CT_0BRK - CT_100BRK indicate the probabilities of cutthroat trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Climate Shield Bull Trout (50% Brook Trout), 2080 (Feature Layer)

This feature class represents the end-of-century (2070-2099) scenario for bull trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields BT_0BRK - BT_100BRK indicate the probabilities of bull trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Climate Shield Bull Trout (50% Brook Trout), 1980 (Feature Layer)

This feature class represents the historical (1970-1999) scenario for bull trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields BT_0BRK - BT_100BRK indicate the probabilities of bull trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Climate Shield Bull Trout (0% Brook Trout), 2040 (Feature Layer)

This feature class represents the mid-century (2030-2059) scenario for bull trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields BT_0BRK - BT_100BRK indicate the probabilities of bull trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Aquatic Organism Passage Surveys (Feature Layer)

This dataset provides USFS Aquatic Organism Passage (AOP) survey data. It shows stream passage locations, passage measurements, and passability assessment categories from AOP field surveys. Structure included: culverts, dams, diversion dams, fords, and natural features such as waterfalls.Metadata and Downloads

7

2 years ago

Aquatic Organism Passage Miles Habitat Improved (Feature Layer)

This dataset displays miles of habitat improved upstream from an Aquatic Organism Passage (AOP) structure that was improved by an on the ground activity. Data includes the completed fiscal year and lists species that benefit from the habitat improvement. The miles of habitat improved are displayed as a line or multi-line. Data are from USFS Natural Resource Manager Watershed Improvement Tracking (WIT) database.Metadata and Downloads

7

2 years ago

Absolute change in annual temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Absolute change in annual precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metadata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Absolute change in April 1 snow water equivalent (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Women, Infants and Children (WIC) Income Eligibility Guidelines 2014-2015

On March 5, 2014, a Notice announcing revised WIC Income Eligibility Guidelines was published in the Federal Register. The adjusted income eligibility guidelines are used by State agencies in determining the income eligibility of persons applying to participate in the WIC Program. WIC State agencies must implement the new guidelines not later than July 1, 2014. WIC State agencies may implement the revised income guidelines at the same time States implement revised income eligibility guidelines for the Medicaid Program. On January 22, 2014, the U.S. Department of Health and Human Services (HHS) published its annual update of the poverty guidelines (79 FR 3593). The HHS guidelines are used by a number of Federal programs, including WIC and the Medicaid Program, as the basis for determining and updating program income eligibility limits.

1

2 years ago

White Paper on the Emergency Food Assistance Program (TEFAP)

The Emergency Food Assistance Program (TEFAP) is a U.S. Department of Agriculture (USDA) program that for three decades has helped supplement the diets of low-income Americans, including seniors, by providing them with emergency food and nutrition assistance at no cost. This white paper explains the program and describes some of its key results.

1

2 years ago

WIC Participant and Program Characteristics 2018

WIC Participant and Program Characteristics 2018 (PC 2018) summarizes the demographic characteristics of participants in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) nationwide in April 2018. It includes information on participant income and nutrition risk characteristics, estimates breastfeeding initiation rates for WIC infants, and describes WIC members of migrant farm-worker families.

1

2 years ago

Absolute change in summer temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Absolute change in summer precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Absolute change in snow residence time (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nSnow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.

3

2 years ago

Absolute change in annual temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Women, Infants and Children (WIC) Detailed Policy Guidance in Disaster Situations

A summary of current WIC policy and regulatory citations that are specifically relevant to WIC Program operation during disaster situations, usually hurricanes, in which WIC participants have been evacuated from their homes and relocated to other areas within their home States, or to another State.

1

2 years ago

WIC State Agencies by state agency name

Provides list of WIC state agencies by state agency name in an alphabetical order.

1

2 years ago

WIC Participant and Program Characteristics 2012 Final Report

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA). WIC benefits include nutritious supplemental foods; nutrition education; counseling, including breastfeeding promotion and support; and referrals to health care, social service, and other community providers for pregnant, breastfeeding, and postpartum women, infants, and children up to the age of 5 years. 1 For women and their unborn children, WIC seeks to improve fetal development and reduce the incidence of low birth weight, short gestation, and anemia through intervention during the prenatal period. For infants and children, WIC seeks to provide nutritious foods during critical times of growth and development in an effort to prevent health problems and to improve the health status of these children. The reports, including PC2012, contain information on a census of WIC participants in April of the reporting year.

1

2 years ago

USDA Helps Schools Make Lunchrooms "Smarter" as Students Head Back to Class Notice

This data set explains the USDA's purpose of putting $5.7 million in training grants and other new resources to help schools serve healthier meals and snacks. The data set states how these efforts will help states expand and enhance training programs that help schools encourage kids to make healthy choices.

1

2 years ago

USDA Foods Bone-in-Chicken Products Memo

This memorandum explains the FNS policy that extends the flexibility regarding Meat/Meat Alternate (M/MA) maximums for the school year 2013-2014. This memo allows State agencies to assess compliance based on the minimum daily and weekly serving requirements only, therefore, they are able to exceed the limit on the number of ounces of M/MA that can be served in any given week as long as they are compliant with the calorie requirements of the new meal pattern.

1

2 years ago

Absolute change in annual precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Women, Infants, and Children (WIC) Participating and Cost Data

This dataset contains monthly data for the current fiscal year for each WIC State agency. There are currently 90 WIC State agencies: the 50 geographic states, the District of Columbia, Puerto Rico, Guam, the Virgin Islands, American Samoa, Northern Marianas, and 34 Indian tribal organizations (ITO's). The dataset contains number of Pregnant Women, Breastfeeding Women, Postpartum Women, Total Women, Infants and children participating in the WIC program and the associated food and administrative cost.

1

2 years ago

Understanding the Rates, Causes, and Costs of Churning in the Supplemental Nutrition Assistance Program (SNAP)

This report describes "churning" as a policy concern in regards to the Supplemental Nutrition Assistance Program (SNAP). “Churning” in the Supplemental Nutrition Assistance Program (SNAP) is defined as when a household exits SNAP and then re-enters the program within 4 months. Churning is a policy concern due to the financial and administrative burden incurred by both SNAP households and State agencies that administer SNAP. This study explores the circumstances of churning in SNAP by determining the rates and patterns of churn, examining the causes of caseload churn, and calculating costs of churn to both participants and administering agencies in six States.

1

2 years ago

USDA Foods available for 2017 for Commodity Supplemental Food Program

This dataset provides the list of food available for 2016 for Commodity Supplemental Food Program.

1

2 years ago

USDA Food Plans: Cost of Food report for JULY 2016

This dataset provides cost of Food at Home at Four levels for the USDA Food Plans. The Food Plans represent a nutritious diet at four different cost levels. The nutritional bases of the Food Plans are the 1997- 2005 Dietary Reference Intakes, 2005 Dietary Guidelines for Americans, and 2005 MyPyramid food intake recommendations. In addition to cost, differences among plans are in specific foods and quantities of foods. Another basis of the Food Plans is that all meals and snacks are prepared at home. For specific foods and quantities of foods in the Food Plans, see Thrifty Food Plan, 2006 (2007) and The Low-Cost, Moderate-Cost, and Liberal Food Plans, 2007 (2007). All four Food Plans are based on 2001-02 data and updated to current dollars by using the Consumer Price Index for specific food items.

1

2 years ago

Trends in SNAP Participation Rates - Fiscal Year 2010 to Fiscal Year 2017

Trends in Supplemental Nutrition Assistance Program Participation Rates: Fiscal Year 2010 to Fiscal Year 2017 is the latest in a series on SNAP participation rates, which estimate the proportion of people eligible for benefits under Federal income and asset rules who actually participate in the program. This report presents rates for fiscal year (FY) 2017, comparing them to rates for FYs 2010 through 2016.

1

2 years ago

The Emergency Food Assistance Program (TEFAP): Total Food Cost

Provide total food cost of the Emergency Food Assistance Program on State level.

1

2 years ago

Team Nutrition Resource Catalog

This catalog provides general information about Team Nutrition, including; strategies and messages, Details on Team Nutrition Schools and how to sign up a school to be a Team Nutrition School. The catalog includes details on all Team Nutrition education materials for schools including, kits, posters, games and stickers. Materials for child care and summer meal programs and technical and training materials for schools and child care.

1

2 years ago

Supplemental Nutrition Assistance Program: Examining the Evidence to Define Benefit Adequacy Study

This study examines how to define "adequacy" of SNAP allotments in the context of improving food security and access to a healthy diet, existing data sources that could inform an assessment of the adequacy of existing and potential alternative SNAP allotments, and new data requirements to strengthen the evidence-base and allow for further rigorous analyses.

1

2 years ago

Supplemental Nutrition Assistance Program Persons Partcipation - Latest Available Month

This dataset provides the number of people participating in Supplemental Nutrition Assistance Program (SNAP) for each state.

1

2 years ago

Supplemental Nutrition Assistance Program Education and Evaluation Study (Wave II)

This study presents the evaluation of the three SNAP-Ed demonstration projects. Two of the three demonstration projects studied targeted low-income children in elementary school settings with the goal of increasing children’s consumption of fruits and vegetables. The third project also focused on increasing fruit and vegetable consumption and targeted seniors. One of the child-focused interventions and the Food and Nutrition Service (FNS) developed Eat Smart, Live Strong program for older Americans demonstrated increases in fruit and vegetable consumption. This study also evaluated the self-evaluations conducted by the three demonstration projects.

1

2 years ago

The Emergency Food Assistance Program (TEFAP) Farm Bill Implementation Memo

On February 7, 2014, the Agricultural Act of 2014 (P.L. 113-79, Farm Bill) was signed into law, reauthorizing TEFAP through Fiscal Year 2018. This memorandum implements two provisions of the Farm Bill relative to TEFAP funding levels and the carryover of food entitlement funds.

1

2 years ago

TANF Broad Based Categorical Eligibility rules that apply to SNAP

Broad-based categorical eligibility (BBCE) is a policy that makes most households categorically eligible for SNAP because they qualify for a non-cash Temporary Assistance for Needy Families (TANF) or State maintenance of effort (MOE) funded benefit. The chart below shows which States implemented BBCE, the programs that confer BBCE, the asset limit of the TANF/MOE program, and the gross income limit of the TANF/MOE program. BBCE cannot limit eligibility. Households with seniors or disabled members that are not eligible for the program that confers categorical eligibility may apply for and receive SNAP under regular SNAP rules. Under regular program rules, households with elderly or disabled members do not need to meet the gross income limit, but must meet the net income limit.

1

2 years ago

Supplemental Nutrition Assistance Program on-line applications

This datasets provides the list of state websites to apply online for SNAP benefits.

1

2 years ago

Supplemental Nutrition Assistance Program Participation and Cost Data

Supplemental Nutrition Assistance Program (SNAP) is the new name for the federal Food Stamp Program. This data set contains participation and cost data for SNAP. The data is furthered divided by annual, state, and monthly levels categorized by persons participating, households participating, benefits provided, average monthly benefits per person and average monthly benefits per household.

1

2 years ago

SuperTracker - source code and foods database

SuperTracker was an online tool offered by USDA (2011-2018) that helped users track diet, physical activity and weight. SuperTracker provided a personalized plan based on the 2015-2020 Dietary Guidelines for Americans for what you should eat and drink and guided users to making better choices. This dataset includes the SuperTracker source code (latest update April 2018), including: front end application, database schema, documentation, deployment scripts and a ReadMe.txt file that provides high level instructions for the source code. Database connection strings and actual data are not included. The full foods database spreadsheet is attached as well; these foods are based on the Food and Nutrient Database for Dietary Studies (FNDDS), and the Food Patterns Equivalents Database (FPED), both from the USDA/ARS Food Surveys Research Group. It is important to note that the code is based on 2015-2020 Dietary Guidelines for Americans and will not be updated to reflect future guidance. In addition, the food database is based on FNDDS from 2011-2012 (FNDDS 6.0) and FPED from 2011-2012 and will not be updated with future data releases.

2

2 years ago

Summer Food Service Program (SFSP) Contacts

This dataset provides contact information for Summer Food Service Program (SFSP) for each state.

1

2 years ago

Summer Food Service Participation, Meals, and Costs Data

During the school year, many children receive free and reduced-price breakfast and lunch through the School Breakfast and National School Lunch Programs. What happens when school lets out? Hunger is one of the most severe roadblocks to the learning process. Lack of nutrition during the summer months may set up a cycle for poor performance once school begins again. Hunger also may make children more prone to illness and other health issues. The Summer Food Service Program is designed to fill that nutrition gap and make sure children can get the nutritious meals they need. This data set contains information on summer food service participation, meals served and cash payments provided by state.

1

2 years ago

Study of Food Safety Needs of Adult Day Care Centers in the Child and Adult Care Food Program

The Study of Food Safety Needs of Adult Day Care Centers in the Child and Adult Care Food Program report identified and evaluated food safety knowledge gaps and education needs of adult day care center program operators. To identify and evaluate food safety education needs, the study team administered a 20-minute survey to a nationally representative sample of directors of adult day care centers that participated in CACFP across the United States in 2018. Overall, this study provides information on knowledge gaps related to food safety practices in adult day care centers and illuminates the best way for center staff to receive future food safety training and information support.

1

2 years ago

Summer Food Service Program (SFSP) Mobile Route Maker

This tool is primarily a routing tool for Summer meal sponsors, vendors and State agencies that is overlaid on the Capacity Builder. Routing is especially important for less densely populated areas, such as rural areas. This tool will help a variety of audiences allocate resources efficiently and in a cost effective way. Sponsors can identify potential summer sites for mobile feeding by identifying gaps in service in the neediest areas, plan the most efficient monitoring routes, and find routes to local food vendors to create fresh local meals (http://www.fns.usda.gov/farmtoschool/farm-summer). In addition self-prep central kitchens and vendors can find the best route to deliver meals to each Summer meal site. State agencies can also use this tool to plan the most efficient monitoring routes throughout the summer.

1

2 years ago

Summer Food Service Program (SFSP) Area Eligibility Mapper

Allows users to confirm eligibility of summer meal sites by accessing census tract and census block group (CBG) data. This tool can also be used to conduct weighted averaging as discussed in SFSP Policy Memorandum 10-2015, “Area Eligibility in Child Nutrition Programs,” published Nov. 21, 2014, available at http://www.fns.usda.gov/area-eligibility-child-nutrition-programs-0. Instructions for determining eligibility using these data are available at: http://www.fns.usda.gov/sites/default/files/Census%20Instructions%202014_0.pdf. The instructions offer two different tools to determine eligibility: the FNS Area Eligibility Mapper and a map developed by the Food Research and Action Center (FRAC). The FRAC map is constructed from the same data files and also may be used to determine eligibility. Additionally, State agencies may request the data file for every CBG and census tract, including Federal Information Processing Standards (FIPS) codes for Geographic Information System (GIS) efforts, in their State. State agencies also may provide these data to institutions and partners upon request. This tool can also be used to identify locations that are area eligible for participation in other Child Nutrition Programs.

1

2 years ago

Study of the Administrative Review and Training Grants in the USDA NSLP and SBP

This report examines in-depth the accomplishments, challenges, and lessons learned from 20 states that received and completed Administrative Review and Training (ART) Grants by the end of FY 2017. ART Grants provide funding for diverse activities aimed at reducing administrative error, including training for administrative personnel and improving state-level technologies in the National School Lunch and School Breakfast Program. Data was collected through interviews with stakeholders in the interventions

1

2 years ago

State of Origin for USDA Foods in Fiscal Year 2012

The Food and Nutrition Service (FNS) Food Distribution Programs provide food and nutrition assistance to school children and families and support American agriculture by distributing high quality, 100 percent American-grown USDA Foods. This report analyzes State of origin data for Fiscal Year (FY) 2012, which captures the State where USDA purchased USDA Foods during FY 2012. In FY 2012, USDA purchased over 2 billion pounds of food, worth nearly $2 billion. Purchased USDA Foods included both raw food products such as meats, vegetables, and fruits, as well as finished food products like cereal, crackers, and pasta.

1

2 years ago

Special Nutrition Program Operations Study: State and School Food Authority Policies and Practices for School Meals Programs School Year 2011-12

The Special Nutrition Program Operations Study is a multiyear study designed to provide the Food and Nutrition Service with a snapshot of current State and School Food Authority policies and practices, including information on school meal standards, competitive foods standards, professional standards, school lunch pricing and accounting, and standards for school wellness policies. The information in this first year study (School Year 2011-12) will provide a baseline for observing the improvements resulting from the implementation of the Healthy, Hunger-Free Kids Act.

1

2 years ago

The Extent of Trafficking in the Supplemental Nutrition Assistance Program: 2009-2011

Trafficking of Supplemental Nutrition Assistance Program (SNAP) benefits occurs when SNAP recipients sell their benefits for cash to food retailers, often at a discount. Although trafficking does not increase costs to the Federal Government, it is a diversion of program benefits from their intended purpose of helping low-income families access a nutritious diet. This report, the latest in a series of periodic analyses, provides estimates of the extent of trafficking during the period 2009 through 2011.

1

2 years ago

Smart Snacks Standards for Exempt Foods when Paired Together Notice

The Healthy, Hunger-Free Kids Act of 2010 (HHFKA) directed the Department of Agriculture (USDA) to establish nutrition standards for all foods and beverages sold to students on the school campus during the school day. On June 28, 2013, the Food and Nutrition Service (FNS) published the "Smart Snacks in School" (Smart Snacks) regulation that carefully balances science-based nutrition standards with practical and flexible solutions to promote healthier eating on campus. The purpose of this memorandum is to clarify Smart Snacks standards for exempt food that are paired together as a single snack.

1

2 years ago

School Food Authorities (SFAs) Purchasing Produce from U.S. Department of Defense (DoD) Vendors using Section 4 and 11 for Fresh Fruit and Vegetable Program (FFVP) Funds Memo

This memorandum clarifies how SFAs may use funds provided under Sections 4 and 11 or 19 of the National School Lunch Act (NSLA) to purchase fresh fruits and vegetables from DoD Fresh Fruit and Vegetable Program (DoD) vendors.

1

2 years ago

School Breakfast Participation and Meals Served Data

The School Breakfast Program (SBP) provides cash assistance to States to operate nonprofit breakfast programs in schools and residential childcare institutions. Data here consists of participation, breakfast meals served, and cash provided to states, all by year, month and current.

1

2 years ago

SNAP State Directory of Resources

To apply for benefits, or for information about the SNAP, contact your local SNAP office using the information in the map below. You can find local offices and each state's application. Local offices are also listed in the State or local government pages of the telephone book. The office should be listed under "Food Stamps," "Social Services," "Human Services," "Public Assistance," or a similar title. You can also call your state's SNAP Hotline Number. Most are toll-free numbers.

1

2 years ago

Special Milk Outlets and Milk Served Data

The Special Milk Program provides milk to children in schools and childcare institutions who do not participate in other Federal meal service programs. The program reimburses schools for the milk they serve. Schools in the National School Lunch or School Breakfast Programs may also participate in the Special Milk Program to provide milk to children in half-day pre-kindergarten and kindergarten programs where children do not have access to the school meal programs. The data set consists of number of outlets, number of half-pints served and federal expenditures.

1

2 years ago

School Foodservice Indirect Cost Study

The Healthy, Hunger-Free Kids Act (HHFKA) directed USDA to study the extent to which school food authorities (SFAs) participating in the National School Lunch Program (NSLP) and School Breakfast Program (SBP) pay indirect costs to local education agencies (LEAs). It specifically requested an assessment of the methodologies used to establish indirect costs, the types and amounts of indirect costs that are charged and not charged to the school food service account, and the types and amounts of indirect costs recovered by LEAs. To address the research questions, information was collected from four perspectives: (1) the State education agency finance officer, (2) the State child nutrition director, (3) the LEA business manager, and (4) the SFA director.

1

2 years ago

SNAP Retailer Management 2013 Annual Report

This report details the responsibilities, authorization activities, and oversight findings that the Regional Operations Division (ROD) staff found regarding retailers who participate in the Supplemental Nutrition Assistance Program (SNAP). ROD staff performed front-end authorization, reauthorization, maintenance, and administration related to retailer participation, administrative sanction activities, and retailer investigations in coordination with compliance partners.

1

2 years ago

SNAP Retail Locator

The Supplemental Nutrition Assistance Program (SNAP) Retailer Locator is designed to help recipients find SNAP local stores that welcome SNAP benefits. The tool is intended to offer assistance to program recipients, State eligibility workers, community organizations - such as food banks - and others providing assistance to those in need. SNAP Retail Locator tool will make it easier for SNAP participants, especially those who may be new and unfamiliar with the program, to gain access to food. The locator is available at http://www.fns.usda.gov/snap/retailerlocator.htm

1

2 years ago

SNAP Income Limits

Households have to meet income tests unless all members are receiving TANF, SSI, or in some places general assistance. Most households must meet both the gross and net income tests, but a household with an elderly person or a person who is receiving certain types of disability payments only has to meet the net income test. Households, except those noted, that have income over the amounts listed below cannot get SNAP benefits

1

2 years ago

SNAP Employment and Training Resources

This data set provides resources for state employment and training throughout United States.

1

2 years ago

Report on the Summer Food for Children Demonstration Projects for Fiscal Year 2013

This Congressional report summarizes the implementation and evaluation of two approaches tested in the summers of 2011 through 2013. Summer EBT for Children (SEBTC) uses existing electronic benefits transfer systems to provide household benefits for children. The Enhanced Summer Food Service Program (eSFSP) tests several changes to the traditional program, including incentives to extend operating periods, incentives to add enrichment activities, meal delivery for children in rural areas, and weekend and holiday backpacks.

1

2 years ago

SNAP Participation and Benefits National Summary Current FY and Prior 4 FY

This dataset provides SNAP Participation and Benefits National Summary for Current Fiscal Year and Prior 4 Fiscal Years.

1

2 years ago

SNAP Households Participation - Latest Available Month

This dataset provides the number of household participating in Supplemental Nutrition Assistance Program (SNAP) for each state.

1

2 years ago

SNAP Community Characteristics

Data about communities and SNAP households. Click on a State to find data by congressional district.

1

2 years ago

SNAP Activity Report, Error Rates and Quality Control

This webpage provides reports for SNAP activity, error rates and quality control.

3

2 years ago

Regional Office Review of Applications (RORA) for School Meals 2013

This study is a summarization of the national estimates of administrative error in eligibility determinations and benefits issuance for free or reduced-price school meals.

1

2 years ago

Reaching Those in Need: Estimates of State SNAP Participation Rates in 2014

This report – part of an annual series – presents estimates of the percentage of eligible persons, by State, who participated in the U.S. Department of Agriculture’s Supplemental Nutrition Assistance Program (SNAP) during an average month in fiscal year (FY) 2014 and in the two previous fiscal years. This report also presents estimates of State participation rates for eligible “working poor” individuals (persons in households with earnings) over the same period.

1

2 years ago

Participation and Benefits - National & State Monthly /Annual Data

This dataset provides National & State Monthly/Annual Data from Fiscal Year 1969 to Current Year for SNAP Participation and Benefits.

1

2 years ago

Nutrition Assistance in Farmers Markets: Understanding the Shopping Patterns of SNAP Participants

This study was undertaken to understand why some SNAP participants shop at farmers markets and others in the same geographic area do not. Results suggest that SNAP participants buy most of their fresh fruits and vegetables at farmers markets. Of those who shop at farmers markets, overall value including quality and price are major reasons for shopping at markets. Of those who do not, reasons for not shopping at farmers markets centered on convenience.

1

2 years ago

Nutrition Assistance Programs Keydata

This dataset provide data and research regarding USDA Nutrition Assistance programs.

1

2 years ago

Nutrient Content of the U.S. Food Supply, 1909-2010

The data presented in this dataset is invaluable for monitoring the potential of the food supply to meet nutritional needs; for examining relationships between food supply nutrients and health; and for examining dietary trends of Americans. Additionally, food supply nutrient estimates reflect Federal enrichment and fortification standards and technological advances in the food industry and contribute to the Federal dietary guidance system. As such, these data are of interest to agricultural policymakers, economists, nutrition researchers, and nutrition and public health educators. Data are provided for the following nutrients and their food sources from the major food groups.

1

2 years ago

National and State Level Estimates of Special SNP for WIC Eligibles and Program Reach

This report offers updated estimates of the number of people eligible for WIC benefits in 2011, including (1) estimates by participant category (including children by single year of age) and coverage rates; (2) updated estimates in U.S. territories; and (3) confidence intervals. The national estimates presented in this report are based on a methodology developed in 2003 by the Committee on National Statistics of the National Research Council (CNSTAT). The report’s State-level estimates use a methodology developed by the Urban Institute that apportions the national figures using data from the American Community Survey

1

2 years ago

Nutrition Assistance Programs Performance Report

This report provides data regarding the nutrition assistance programs performance report for August 2014. The report reflects the participation of persons in FNS' programs.

1

2 years ago

Nutrient and MyPyramid Analysis of USDA Foods in Five of Its Food and Nutrition Programs

Through its food distribution programs, USDA purchases a variety of food including fruits, vegetables, meat, grains, and dairy products to be distributed directly to needy households or for use in congregate feeding programs that help Americans obtain access to nutritious food and support American agriculture. This report contains nutrient and food group analyses of the USDA Food distributed through NSLP, CACFP, CSFP, FDPIR, and TEFAP in fiscal year 2009.

1

2 years ago

New Local Educational Agency Second Review of Applications Report

This memorandum provides information on the release of the new form used to report the results of the second review of free and reduced price applications in the National School Lunch Program (NSLP) and School Breakfast Program (SBP).

1

2 years ago

National School Lunch Program: State Implementation Progress, School Year 2012–2013: Report to Congress

This report responds to the requirement of Public Law 110-246 to assess the effectiveness of State and local efforts to directly certify children for free school meals. Direct certification is a process conducted by the States and by local educational agencies (LEAs) to certify eligible children for free meals without the need for household applications. States and LEAs directly certified 12.3 million children at the start of SY 2012-2013, an increase of 740,000, or 6 percent, from the previous school year. Over the same period, the population of school-age SNAP participant children increased by just 1.5 percent. As a result, the share of SNAP participant children certified for free school meals without application increased to 89 percent in SY 2012-2013, up from 86 percent in SY 2011-2012.

1

2 years ago

Policy on Certification Periods for Zero Income Households

This memorandum provides revised policy guidance on certification periods pertaining to zero income households in FDPIR. The revised policy for zero income households provides for certification staff to continue to question and document households that report zero income. The certification periods have been revised to allow for longer certification periods, if the zero income household is verified to be stable with regard to lack of income.

1

2 years ago

National School Lunch Assistance Program Participation and Meals Served Data

The National School Lunch Program (NSLP) is a federally assisted meal program operating in public and nonprofit private schools and residential child care institutions. It provides nutritionally balanced, low-cost or free lunches to children each school day. Information in this dataset consists of participation and lunches served.

1

2 years ago

HUSSC Award Winning Schools

Schools have the opportunity to become certified as Bronze, Silver, Gold, or Gold of Distinction Schools, depending on meeting certain criteria. We are pleased to share the names of those schools that have achieved certification as a Bronze, Silver, Gold, or Gold of Distinction School.

1

2 years ago

Farmers Market Incentive Provider Study

To encourage Supplemental Nutrition Assistance Program participants to shop at farmers markets, various organizations have been providing financial incentives to participants who redeem SNAP benefits at participating farmers markets. This report is meant to be the first systematic study of the roles different organizations play in designing and implementing SNAP based incentive programs, how they choose markets for their programs, and how they evaluate success of their programs.

1

2 years ago

Feasibility of Revising the SNAP Quality Control Review Process

FNS uses a two-tier system to measure errors in eligibility and benefit determination for SNAP. This feasibility study identifies all processes and components that would be required for a one-tier federal SNAP QC system, including the procedural, staffing, and organizational changes and the technological and data-sharing infrastructures. The study does not make recommendations but documents all the changes needed to move from a two-tier to a one-tier QC system.

1

2 years ago

Dynamics and Determinants of Supplemental Nutrition Assistance Program Participation from 2008-2012

This report describes the dynamics of the Supplemental Nutrition Assistance Program participation from 2008-2012. It describe individuals’ patterns of SNAP participation and analyze which factors were associated with their decisions to enter or exit the program. It uses data from the U.S. Census Bureau’s Survey of Income and Program Participation covering the period from 2008 to 2012.

1

2 years ago

Disaster Supplemental Nutrition Assistance Program Income Eligibility Standards and Allotments

This dataset provides information about disaster Supplemental Nutrition Assistance Program (SNAP) income eligibility standards and allotments based on household size.

1

2 years ago

Disaster SNAP Guidance

This handbook describes D - SNAP policy, provides lessons learned from previous D-SNAPs, and contains toolkits to help SNAP offices plan for, organize, and operate a D-SNAP.

1

2 years ago

Demonstration Projects to End Childhood Hunger 2018 Annual Report to Congress

The 2010 Child Nutrition reauthorization also directed the Secretary of Agriculture to submit a report by the end of December each year to the U.S. House of Representatives Committees on Agriculture and Education and the Workforce, in addition to the Senate Committee on Agriculture, Nutrition, and Forestry. The annual reports are to describe the status of each demonstration project and the available results of any evaluations of the demonstration projects completed during the previous fiscal year (FY).

1

2 years ago

Community Eligibility Provision Evaluation

The Healthy, Hunger-Free Kids Act (HHFKA) provided schools and districts that predominately serve low-income children with a new option for meal certification. Under the Community Eligibility Provision, schools do not collect or process meal applications for free and reduced-price meals served in the National School Lunch Program and School Breakfast Program. Schools must serve all meals at no cost with any costs in excess of the Federal reimbursement paid from non-Federal sources. The evaluation, mandated by HHFKA, examined the implementation and impacts of the Community Eligibility Provision.

1

2 years ago

Commodity Supplemental Food Program: Total Participation

Provide Persons participating in Commodity Supplemental Food Program on State level.

1

2 years ago

Child Nutrition Programs Disaster Response Memo

This memorandum provides an overview of ways State agencies, School Food Authorities (SFA) participating in the National School Lunch and School Breakfast Programs (NSLP and SBP), institutions participating in the Child and Adult Care Food Program (CACFP), and sponsors participating in the Summer Food Service Program (SFSP) can respond to situations resulting from damage or disruptions due to natural disasters such as hurricanes, tornadoes, and floods. State agencies should review the avenues available to prepare and plan before a disaster strikes so responses can be as swift as possible.

1

2 years ago

Child and Adult Care Program Monthly Data

This dataset provides the monthly data for Child and Adult Care Program.

1

2 years ago

Child and Adult Care Food Participation, Meals, and Cost Data

Child and Adult Care Food Participation plays a vital role in improving the quality of day care for children and elderly adults by making care more affordable for many low-income families. Through CACFP, nearly 3 million children and 90,000 adults receive nutritious meals and snacks each day as part of the day care they receive. The data set contains participation; meals served, and cash payments to states.

1

2 years ago

Child Nutrition Program Operations Study II, School Year 2015-2016

The Child Nutrition Program Operations Study II (CN-OPS II) is a multiyear study designed to provide the U.S. Department of Agriculture's (USDA) Food and Nutrition Service (FNS) with information on current State Agency (SA) and school food authority (SFA) policies, practices, and needs related to school nutrition service operations, financial management, meal counting, training and professional standards, food service equipment, and technology. Results are used to inform Child Nutrition program management and policy development.

1

2 years ago

Calculating the SNAP Program Access Index: A Step-By-Step Guide for 2012

The Program Access Index (PAI) is one of the measures the USDA Food and Nutrition Service (FNS) uses to reward States for high performance in the administration of the Supplemental Nutrition Assistance Program (SNAP). The Farm Security and Rural Investment Act of 2002 (also known as the 2002 Farm Bill) directed USDA to establish a number of indicators of effective program performance and to award bonus payments to States with the best and most improved performance. The PAI is designed to indicate the degree to which low-income people have access to SNAP benefits.

1

2 years ago

CACFP Monitoring Handbook for State Agencies

This handbook is for state agency monitoring staff and provides detail for administering the Child and Adult Care Food Program (CACFP). The CACFP is a federally-funded program that provides payments for eligible meals served to participants who meet age and income requirements. Meals served by participating institutions and facilities must meet minimum guidelines set by the U.S. Department of Agriculture (USDA). The CACFP helps institutions and facilities serve well-balanced, nutritious meals to the participants in their care. Serving nutritious meals helps improve and maintain the health and nutritional status of participants in a day care environment and can help them develop and maintain good eating habits.

1

2 years ago

Calculating the SNAP Program Access Index: A Step-By-Step Guide

The Program Access Index (PAI) is one of the measures FNS uses to reward states for high performance in the administration of the Supplemental Nutrition Assistance Program (SNAP). Performance awards were authorized by the Farm Security and Rural Investment Act of 2002 (also known as the 2002 Farm Bill). The PAI is designed to indicate the degree to which low-income people have access to SNAP benefits. The purpose of this step-by-step guide is to describe the calculation of the Program Access Index (PAI) in detail. It includes all of the data, adjustments, and calculations used in determining the PAI for every state.

1

2 years ago

CACFP At-Risk Afterschool Care Handbook

The At-Risk Afterschool Meals component of the Child and Adult Care Food Program (CACFP) offers Federal funding to Afterschool Programs that serve a meal or snack to children in low-income areas. Reimbursement for At-Risk Afterschool Snacks has been available since the 1990s. However, reimbursement for At-Risk Afterschool Meals was available only in a few states. The Healthy, Hunger-Free Kids Act of 2010 (P.L. 111-296) expanded the availability for At-Risk Afterschool Meals to all states.

1

2 years ago

Assessment of States' Use of Computer Matching Protocols in SNAP

As required by federal law, state SNAP agencies verify financial and non-financial information by matching SNAP applicant and participant information to various national and state data sources to ensure they meet the program’s eligibility criteria. Data matching is an important tool for ensuring program integrity and benefit accuracy. However, information on states’ data matching practices and protocols is limited. This study was undertaken to address this knowledge gap.

1

2 years ago

Addressing Child Hunger and Obesity in Indian Country: Report to Congress

This report responds to requirements found in the Healthy, Hunger-Free Kids Act of 2010 (HHFKA) and summarizes hunger, obesity, and Type II diabetes among American Indian and Alaska Native children living on or near reservations or other tribal lands (Indian Country). The report provides a summary of available statistics on hunger, obesity, and Type II diabetes among children living in Indian Country and offers comparable statistics for the general population for context and comparison.

1

2 years ago

2013 WIC Vendor Management Study

This study is part of a larger FNS effort to ensure WIC program integrity and to comply with the Improper Payments Information Act of 2002 (IPIA) (Public Law 107-300), which requires FNS to estimate improper payments (IP) in its programs. To evaluate program integrity, the 2013 report includes two complementary studies: A study, comparable to the 1998 and 2005 WIC Vendor Management Studies, which examined purchases made through compliance buys using paper- or Electronic Benefit Transfer (EBT)-based FIs, and a cash value voucher study, which examined purchases made through compliance buys using the CVVs or, in the case of EBT, cash value benefits (CVBs) to purchase fruits and vegetables.

1

2 years ago

Assessment of the Administrative Review Process in School Meal Programs

The Administrative Review (AR) is the process state agencies use to assess compliance with Federal requirements of SFAs participating in the National School Lunch Program and the School Breakfast Program. The current AR process was implemented in school year (SY) 2013-2014. This study assesses the AR process by examining the results from a sample of Administrative Review forms selected and submitted by the 52 state agencies utilizing the AR process during school years 2013-2014, 2014-2015, and 2016-2017. The study also describes in-depth how nine selected state agencies conduct their ARs, and ways the process could be further improved.

1

2 years ago

Approaches for Promoting Healthy Food Purchases by SNAP Participants Project

Due to the United States' high rates of obesity and diet-related chronic diseases, this project aims to develop a plan for front of package (FOP) and shelf-labeling systems that identifies healthy choices, develops theory-based approaches that leverage FOP and shelf-labeling systems to promote healthier food purchases by SNAP participants, and identifies further exploration through the implementation and testing of a future pilot study.

1

2 years ago

Able-Bodied Adults Without Dependents (ABAWDs) rules

Most SNAP participants who can work, do work. SNAP rules require all recipients meet work requirements unless they are exempt because of age or disability or another specific reason. Children, seniors, and those with disabilities comprise almost two-thirds of all SNAP participants. Among households that include someone who is able to work, more than 75 percent* had a job in year before or after receiving SNAP. Forty-three percent of SNAP participants live in a household with earnings. Some of these working individuals are ABAWDs, or able-bodied adults without dependents. ABAWDs must meet special work requirements, in addition to the general work requirements, to maintain their eligibility. An ABAWD is a person between the ages of 18 and 49 who has no dependents and is not disabled. ABAWD stands for Able Bodied Adult Without Dependents.

1

2 years ago

World Agricultural Production

Monthly report on crop acreage, yield and production in major countries worldwide. Sources include reporting from FAS’s worldwide offices, official statistics of foreign governments, and analysis of economic data and satellite imagery.

2

2 years ago

Production, Supply, and Distribution Database

Official USDA data on production, supply, and distribution of agricultural commodities for the United States and key producing and consuming countries.

1

2 years ago

Export Sales Reporting

Information on US export sales, by commodity and country of destination, updated weekly.

1

2 years ago

Amounts of 2016 Licenses Issued, by Commodity and Country

1

2 years ago

Agricultural Tariff Tracker

The Agricultural Tariff Tool is a web application that queries tariff schedules and rate information resulting from Free Trade Agreements (FTAs). All exporters/importers need to determine how competitive their product will be in a market. One of the key cost components is the import tariff that will be applied to a product by the importing country. The FAS Agricultural Tariff Tool will allow exporters/importers to quickly and easily determine the tariff rate that will be applied to their product by the importing country.

1

2 years ago

USDA Rural Development Single Family Section 502 Direct Active Loans by Congressional District

Active loan characteristics aggregated at the Congressional District level of geography, including number of loans, average loan amount, average loan amount by 5 year ranges, number of loans to Section 523 Mutual Self Help Housing program participants, and number of leveraged loans.

2

2 years ago

USDA Rural Development Single Family Section 502 Direct Active Borrowers by County

Active borrower characteristics aggregated at the county level of geography, including number of borrowers, income levels, race, ethnicity, marital status, number of children in household, and average household size.

2

2 years ago

USDA Rural Development Section 538 Multifamily Guaranteed Loans as of 7.13.2016

Active loan characteristics in USDA RD Section 538 Multifamily Guaranteed Loan program, including loan, property, and community characteristics. Loan characteristics include obligation fiscal year, lender, borrower, loan closing date, loan amount, total development cost, loan to cost ratio, and federal LIHTC tax credit indicator. Property characteristics include location and address, colonias or tribal location indicator, EZ/EC location indicator, project size, project type, construction type, number of units by bedroom size, and average contract rent by bedroom size. Community characteristics include the area population and median household income at time of obligation.

2

2 years ago

USDA Rural Development Section 502 Guaranteed Loan Program Active Loans by Congressional District

Borrower, property and loan characteristics for all active Section 502 Guaranteed Loans, aggregated by Congressional District. Borrower characteristics include: income, debt-income ratio, race, ethnicity, marital status, dependents, household size, first-time homebuyer status, age and disability status. Property characteristics include: project type (PUD, Condo, Coop), housing structure (detached, attached), manufactured home, living area. Loan characteristics include: loan request amount, loan amount, loan-to-value ratio, and appraised value.Property characteristics include: project type (PUD, Condo, Coop), housing structure (detached, attached), manufactured home, living area. Loan characteristics include: loan request amount, loan amount, loan-to-value ratio, and appraised value.

2

2 years ago

USDA Rural Development Resale Properties - Foreclosure

Data provides current information regarding single family homes and ranches for sale by the U.S. Federal Government. These previously owned properties are for sale by public auction or other method depending on the property.

3

2 years ago

USDA Rural Development Property Eligibility - Rural Business Service (RCDI, REDL, REDG)

This data is used to determine eligibility for certain USDA Intermediary Relending Programs.

2

2 years ago

USDA Rural Development Single Family Section 502 Direct Active Borrowers by Congressional District

Active borrower characteristics aggregated at the Congressional District level of geography, including number of borrowers, income levels, race, ethnicity, marital status, number of children in household, and average household size.

2

2 years ago

USDA Rural Development Section 502 Guaranteed Loan Program Active Loans by County

Borrower, property and loan characteristics for all active Section 502 Guaranteed Loans, aggregated by county. Borrower characteristics include: income, debt-income ratio, race, ethnicity, marital status, dependents, household size, first-time homebuyer status, age and disability status. Property characteristics include: project type (PUD, Condo, Coop), housing structure (detached, attached), manufactured home, living area. Loan characteristics include: loan request amount, loan amount, loan-to-value ratio, and appraised value.

2

2 years ago

USDA Rural Development Property Eligibility Water and Environmental Programs

This data is used to determine eligibility for certain USDA Water and Environmental Programs.

2

2 years ago

USDA Rural Development Property Eligibility - Rural Business Service (B&I Guaranteed, RBEG, RBOG, REAP, RMAP, BAP and IRP)

This data is used to determine eligibility for certain USDA RBS loan and grant programs.

2

2 years ago

USDA Rural Development Property Eligibility (SFH/MFH)

This data is used to determine eligibility for certain USDA Single Family Housing and Multi-Family Housing loan and grant programs.

2

2 years ago

USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Property Characteristics

Property locations and characteristics for USDA Rural Development Multifamily Direct Loan programs: Section 515 Rural Rental Housing and Section 514 Farm Labor Housing. Includes latitude and longitude coordinates, property address, type of development, date of operation, profit type, management agent, loan program identifier, Low Income Housing Tax Credit identifier and expiration date, Multifamily Preservation and Revitalization program identifier, total units, USDA Section 521 rental assistance units, units by bedroom size, and vacant units.

2

2 years ago

USDA Rural Development Multi-Family Housing Program Exit Data

This dataset provides loan-level information on when USDA Section 514 and 515 properties are projected to pay off their loans and exit USDA’s Multi-Family Housing program. Includes estimated property exit year, whether the loan is prepay eligible and when, loan amount, original loan term and remaining term days, borrower characteristics, property location and characteristics, and more.

2

2 years ago

USDA Rural Development Multi Family Housing - September 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for September 2016.

1

2 years ago

USDA Rural Development Multi Family Housing - October 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for October 2016.

1

2 years ago

USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Tenant Characteristics

Aggregated tenant characteristics for USDA Rural Development Multifamily Direct Loan programs: Section 515 Rural Rental Housing and Section 514 Farm Labor Housing. Includes property address and aggregated demographic information including female headed-households, elderly aged 62 or older, minors, disability status, race, and ethnicity. Also includes average annual income, average annual income by source of income, cost-burden indicator, zero income indicator, and rental assistance subsidy counts by type of assistance. Can be merged with “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Property Characteristics” to link to property characteristics, as well as “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Properties Transfers, Consolidations, and Sales” to link with property transaction histories.

2

2 years ago

USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Properties Transfers, Consolidations, and Sales

Transaction history of property transfers, consolidations, and sales within the USDA Rural Development Multifamily Direct Loan programs: Section 515 Rural Rental Housing and Section 514 Farm Labor Housing. Includes new property ID numbers and associated old property ID numbers, transaction type indicators, and effective dates. Requires merging with “USDA Rural Development Multifamily Section 515 Rural Rental Housing and Section 514 Farm Labor Housing Property Characteristics” to obtain property address and other characteristics based on new property ID number.

2

2 years ago

USDA Rural Development Multi Family Housing - September 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for September 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - June 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for June 2018.

1

2 years ago

USDA Rural Development Multi Family Housing - May 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for May 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - June 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for June 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - July 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for July 2018.

1

2 years ago

USDA Rural Development Multi Family Housing - June 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for June 2016.

1

2 years ago

USDA Rural Development Multi Family Housing - December 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for December 2016.

1

2 years ago

USDA Rural Development Community Facilities Loan, Grant, and Guaranteed Loan Data

Locations and characteristics of projects that have received USDA Rural Development Community Facilities Loans, Grants, and Guaranteed Loans. Includes latitude and longitude coordinates, facility name and address, NAICS Code, funding type, obligation date and amount, total development cost, borrower name and type, and more

2

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - September 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for September 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - October 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for October 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - November 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for November 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - November 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for November 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - May 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for May 2016.

1

2 years ago

USDA Rural Development Multi Family Housing - February 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for February 2017.

1

2 years ago

Food Allergies

Created in collaboration with the Institute of Child Nutrition, this series of fact sheets provide an overview of food allergies, the top 8 food allergies, how to manage food allergies, and common questions regarding food allergies in adult day care programs.

1

2 years ago

FSIS - Poultry Categorization Data

Salmonella verification testing for individual establishments and the aggregated data for Young Chicken and Turkey Carcasses, Raw Chicken Parts, and NRTE Comminuted Poultry establishments. Data is updated on the 20th of every month, or the following business day if the 20th falls on a weekend or holiday. See the FSIS website for additional information.

2

2 years ago

FSIS - Chemical Residue Quarterly Report

This quarterly report summarizes chemical residue results for the United States National Residue Program for meat, poultry, and egg products. The results in this report cover the domestic (scheduled and inspector-generated) and import sampling programs. Data is updated quarterly. See the FSIS website for additional information.

2

2 years ago

Electronic Directives System (eDirectives)

The Electronic Directives System (eDirectives) contains the written policies and procedures used by NRCS employees to provide information and services to customers and partners to meet the diverse needs of people, farm production, programs, and resource needs of the public served by the United States Department of Agriculture.

0

2 years ago

TreeMap 2016 Live Tree Growing Stock (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Live Tree Basal Area (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Forest Type Name Algorithm (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Forest Type Algorithm (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Carbon Standing Dead (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

TreeMap 2016 Carbon Down Dead (Image Service)

TreeMap 2016 provides a tree-level model of the forests of the conterminous United States.Metadata and DownloadsWe matched forest plot data from Forest Inventory and Analysis (FIA) to a 30x30 meter (m) grid. TreeMap 2016 is being used in both the private and public sectors for projects including fuel treatment planning, snag hazard mapping, and estimation of terrestrial carbon resources. We used a random forests machine-learning algorithm to impute the forest plot data to a set of target rasters provided by Landscape Fire and Resource Management Planning Tools (LANDFIRE: https://landfire.gov). Predictor variables consisted of percent forest cover, height, and vegetation type, as well as topography (slope, elevation, and aspect), location (latitude and longitude), biophysical variables (photosynthetically active radiation, precipitation, maximum temperature, minimum temperature, relative humidity, and vapour pressure deficit), and disturbance history (time since disturbance and disturbance type) for the landscape circa 2016. The main output of this project (the GeoTIFF included in this data publication) is a raster map of imputed plot identifiers at 30X30 m spatial resolution for the conterminous U.S. for landscape conditions circa 2016. In the attribute table of this raster, we also present a set of attributes drawn from the FIA databases, including forest type and live basal area. The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB). The dataset has been validated for applications including percent live tree cover, height of the dominant trees, forest type, species of trees with most basal area, aboveground biomass, fuel treatment planning, and snag hazard. Application of the dataset to research questions other than those for which it has been validated should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding. This raster dataset represents model output generated by a random forests method that assigns Forest Inventory Analysis plot identifiers to a 30x30m grid (Riley et al. 2016 and Riley et al. 2021). Some attributes provided have been validated as detailed below, and we have high confidence they would be suitable for stand, county, and national scale analyses. Other attributes have not been validated as of this writing on 2/25/2022. Accuracy may vary regionally. This dataset is for the landscape circa 2016 and does not capture disturbances such as fire and land management after that date. Based on a set of FIA validation plots, these data have moderate to high accuracy at point locations for forest cover, height, vegetation group, and recent disturbance by fire and insects and disease (Riley et al. 2021). Summary statistics at Baileys section and subsection levels indicate high accuracy in most sections and subsections when compared to FIA statistics for live basal area, number of live trees greater than or equal to 1 diameter, live cubic-foot volume, and live-tree biomass. Estimates of number of dead trees greater than or equal to 5 diameter and dead tree above-ground biomass have lower correlations with FIA estimates, which are driven largely by the fact that TreeMap does not include areas where live tree cover is less than 10% while FIA does, meaning that severely disturbed areas are not included in mapping. In general, the TreeMap data are appropriately used for planning and policy-level analyses and decisions. Local map accuracy is suitable for many local-scale decisions regarding questions around forest cover, height, vegetation group, and recent disturbances. For other attributes provided here, formal validation has not been completed, and assessment at local scales is advised and must be driven by project-specific needs. References: Riley, Karin L., Isaac C. Grenfell, and Mark A. Finney. 2016. Mapping Forest Vegetation for the Western United States Using Modified Random Forests Imputation of FIA Forest Plots. Ecosphere 7 (10): e01472. https://doi.org/10.1002/ecs2.1472. Riley, Karin L., Isaac C. Grenfell, Mark A. Finney, and John D. Shaw. 2021. TreeMap 2016: A Tree-Level Model of the Forests of the Conterminous United States circa 2016. https://doi.org/10.2737/RDS-2021-0074.

3

2 years ago

Tongass Landslide Initiation (Feature Layer)

This is the point feature class for the once-over landslide inventory of the Tongass National Forest. Most of the landslide polygons were digitized on the 1998 to 2010 orthophotos in GIS. Many of them were age bracketed using air photos back to the 1929 Navy Trimegon photos. It includes both field and photo interpreted landslides. This point layer represents initiation points for debris avalanches, debris torrents, combination-debris avalanches/torrents, slumps, rock fall initiated failures, and rotational failures. There is an accompanying polygon feature class (Tongass_Landslide_Areas), which represents all mass wasting features, including talus slopes, snow avalanche fields, and snow avalanche chutes. Metadata

7

2 years ago

Timber Harvests (Feature Layer)

Depicts the area planned and accomplished acres treated as a part of the timber harvest program of work, funded through the budget allocation process and reported through the FACTS database. Activities are self-reported by Forest Service Units. Metadata

7

2 years ago

Timber Appraisal Zones (Feature Layer)

This dataset is a spatial display of US Forest Service stumpage market appraisal zones. A zone may encompass a Region, a National Forest, a group of Ranger Districts, or combinations thereof. Each unique market appraisal zone defines a localized stumpage market. In each market area, stumpage values reflect the market value of standing trees (on the stump) prior to felling, removal, and utilization in a value-added manufacturing activity. The zone boundary is typically determined by factors including, but not limited to, manufacturing facilities, hauling distances, species yield compositions, timber quality, market area competition, and logging methods. Timber Appraisal Zone Metadata

7

2 years ago

Terrestrial Ecological Unit Inventory Data Status (Feature Layer)

The purpose of this dataset is to display the extent of existing Terrestrial Ecological Unit inventory (TEUI) data internally to facilitate inter-agency collaboration. The feature class for this dataset will display polygons of the ecological unit plots, acreages, and percent coverages of National Forest and Grassland administrative boundaries using their common names, with a percent coverage for Land Type and acres of forest per plot.Metadata and Downloads

7

2 years ago

USFS Analytical 2016 Tree Canopy Cover Coastal AK (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse The Coastal Alaska TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and line up. The USFS's GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/and download the FS-Cartographic version of the 2011 and/or 2016 datasets for their cartographic applications.

3

2 years ago

Special Uses Communications Sites (Feature Layer)

This data is intended for read-only use. The purpose of the data is to provide display, identification, and analysis tools for determining locations of designated communications sites located on National Forest System lands, for Forest Service managers and other interested parties.

7

2 years ago

USFS Analytical 2011 Tree Canopy Cover Standard Error Puerto Rico Virgin Islands (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

USFS Analytical 2011 Tree Canopy Cover Standard Error Coastal AK (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands andPuerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include:The initial model outputs referred to as the Analytical data;A masked version of the initial output referred to as Cartographic data;And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel�s values meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe Coastal Alaska TCC 2011 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. The dataset has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 127.

3

2 years ago

USFS Analytical 2011 Tree Canopy Cover Puerto Rico Virgin Islands (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

Simulated data for Secondary organic aerosol formation in biomass-burning plumes: Theoretical analysis from lab studies and ambient plumes

The volatile nature of biomass burning organics may complicate the evolution of organics in laboratory smog-chamber experiments and in ambient plumes. We simulate the evolution of organic mass (including gas and particles) in the chamber experiments using the TwO-Moment Aerosol Sectional (TOMAS) microphysics model combined with a secondary organic aerosol (SOA) production matrix. We estimate the effect of vapor wall loss by turning off the vapor wall loss, and also added Gaussian dispersion to our aerosol-microphysical model to SOA formation under different ambient-plume conditions. A detailed description of model setup and results can be found in Bian et al. 2017. The data publication here contains simulation datasets generated using the TOMAS microphysics model combined with a secondary organic aerosol (SOA) production matrix. Datasets are organized according to the figures in Bian et al. 2017 and include 1) chemistry-only simulation data; 2) data generated using the TOMAS model combined with particle and vapor wall-loss algorithms and a SOA production matrix with varying parameters; and 3) simulation data generated using the TOMAS model assuming the plume volume follows the Gaussian dispersion. Each ASCII dataset contains the time series of individual vapors and particles that were distributed in 36 size bins from 3 nanometers to 10 micrometers.

3

2 years ago

Silviculture Timber Stand Improvement (Feature Layer)

This layer portrays the area where activities accomplished as a part of the silviculture program of work, funded through the budget allocation process and reported through the Forest Service Activity Tracking System (FACTS) database and are part of the Performance Measures used to rate Agency performance in meeting the Department's Strategic Goals. It is important to note that this layer may not contain all accomplished activities; the spatial portion of the activity description is not currently enforced by FACTS and at this time some are optionally reported by Forest Service units. This layer only represents those activities associated with the performance measure Forest Vegetation Improved (Release, Weeding, and Cleaning, Precommercial Thinning, Pruning and Fertilization). As spatial data reporting is enforced by the application and acceptance of reporting increases for both tabular and spatial we hope to improve the quality and comprehensiveness of the data used for this layer in coming years.�Metadata

7

2 years ago

Silviculture Reforestation (Feature Layer)

This layer portrays the area where activities accomplished as a part of the silviculture program of work, funded through the budget allocation process and reported through the Forest Service Activity Tracking System (FACTS) database and are part of the Performance Measures used to rate Agency performance in meeting the Department's Strategic Goals. It is important to note that this layer may not contain all accomplished activities; the spatial portion of the activity description is not currently enforced by FACTS and at this time some are optionally reported by Forest Service units. This layer only represents those activities associated with the performance measure Forest Vegetation Improved (Release, Weeding, and Cleaning, Precommercial Thinning, Pruning and Fertilization). As spatial data reporting is enforced by the application and acceptance of reporting increases for both tabular and spatial we hope to improve the quality and comprehensiveness of the data used for this layer in coming years.Metadata

7

2 years ago

Roadless Areas: Colorado Roadless Rule (Feature Layer)

This feature class describes the boundaries of Roadless Areas designated by the Colorado Roadless Rule of 2012 and managed by the US Forest Service. These roadless areas were designated by administrative rule making to provide management direction for their conservation and management. These roadless area designations supersede the roadless areas designated by the Roadless Area Conservation Rule of 2001 for Colorado. Upper tier areas are a subset of Colorado Roadless Areas which have limited exceptions to provide a high level of protection. The North Fork Coal Mining area is a subset of Colorado Roadless Areas which has an exception for coal mining related activities. Metadata and Downloads

7

2 years ago

Recreation Sites (Feature Layer)

This dataset shows information about the USDA Forest Service constructed recreation sites used to populate the public facing webpages. This information is the descriptive and qualitative information used to set appropriate expectations for visitor use and may not contain all the exact engineering, constructed features. View Metadata.

7

2 years ago

Recreation Information Database - RIDB

The RIDB is an API accessible database of US Gov't Recreation site data contributed by twelve participating agencies in the Recreation One Stop program. This data is used on Recreation.gov and is available to the public for various other uses.

3

2 years ago

National Wild and Scenic River Active Study Rivers Segments (Feature Layer)

This data set represents the Wild and Scenic Rivers Active Study Rivers segments.

7

2 years ago

NLCD 2011 Tree Canopy Cover Puerto Rico Virgin Islands (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

NLCD 2011 Tree Canopy Cover Coastal AK (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe Coastal Alaska TCC 2011 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. The dataset has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 127.

3

2 years ago

Forest Common Names (Feature Layer)

This dataset contains the common names of the national forests and grasslands and their respective FS WWW URL information that is used for both display of the national forest and national grassland boundaries on any map product and for dynamic interactivity of the map. This dataset exhibits the following characteristics: 1. Granularity of the polygon features - The spatial extent of the national forests and the grasslands match the way the agency would like to communicate with the public. 2. Preferred /Common Name of the National Forest Units - The common names of the national forest and grassland match the preferred name column that is present in the common names decision table maintained by the FS Office of Communication. 3. Hyperlinks to FS WWW Home page - This column contains the national forest and their respective FS WWW URL information. This URL could be used on any interactive map applications to link users directly to a forest's home page. Data Source - This dataset is derived from the following FS ALP (Automated Lands Program) Land Status Records System authoritative data sources: 1. Administrative Forest Boundaries 2. Proclaimed Forest Boundaries 3. Ranger District Boundaries 4. National Grassland Areas. The common names decision table maintained by the FS Office of Communication contains the common name and its respective Land Status Records System authoritative data source to be used for building the spatial polygon. The spatial polygons for every feature in this dataset comes from one or more authoritative data sources listed above. The process to create the common names dataset is reusing the already existing ALP names from the data sources listed above.

9

2 years ago

FSTopo Tiled Basemap ArcGIS Online Web Map

ArcGIS Online Web Map containing ESRI Streets at small scales and FSTopo Basemap at scales larger than 1:144,448. This basemap web map is designed to be used in ArcGIS Online mapping applications with other map services or features services overlayed on the FSTopo basemap.

3

2 years ago

Experimental Forest and Range Areas (Feature Layer)

This polygon feature class contains the boundaries of 86 of 87 experimental forests, ranges and watersheds, including cooperating experimental areas. Experimental Forest and Range Areas Metadata

9

2 years ago

Existing Vegetation: Region 5 (Map Service)

A map service depicting Forest Service existing vegetation polygons for Region 5.This Existing Vegetation (EVeg) polygon feature class is a CALVEG (Classification and Assessment with LANDSAT of Visible Ecological Groupings) map product from a scale of 1:24,000 to 1:100,000. The geographic extent entails the northeastern portion of CALVEG Zone 6, Central Coast. Source imagery for this layer ranges from the year 1998 to 2015. The CALVEG classification system was used for vegetation typing and crosswalked to other classification systems in this database including the California Wildlife Habitat Relationship System (CWHR).Metadata and Downloads

3

2 years ago

Expenditures on Children by Families, 2015

Since 1960, the U.S. Department of Agriculture has provided estimates of expenditures on children from birth through age 17. This technical report presents the most recent estimates for married- couple and single-parent families using data from the 2011-15 Consumer Expenditure Survey (all data presented in 2015 dollars). Data and methods used in calculating annual child-rearing expenses are described. Estimates are provided for married-couple and single-parent families with two children for major components of the budget by age of child, family income, and region of residence. For the overall United States, annual child-rearing expense estimates ranged between $12,350 and $13,900 for a child in a two-child, married-couple family in the middle-income group. Adjustment factors for households with less than or greater than two children are also provided. Expenses vary considerably by household income level, region, and composition, emphasizing that a single estimate may not be applicable to all families. Results of this study may be of use in developing State child support and foster care guidelines, as well as public health and family-centered educational programs. i

1

2 years ago

Evaluation of the Healthy Incentives Pilot (HIP) Final Report-Summary

This final report summary describes the background, methods, and findings of the Healthy Incentives Pilot (HIP). This pilot project enabled SNAP participants to receive an incentive of 30 cents for every SNAP dollar spent on targeted fruits and vegetables at participating retailers. The comprehensive data concluded that HIP participants consumed 26 percent more of targeted fruits and vegetables compared to non-participants. The summary report also details the implications for HIP retailer participants and total costs.

1

2 years ago

Evaluation of the Direct Certification with Medicaid for Free and Reduced-Price Meals - Year 1

The demonstration of Direct Certification with Medicaid for Free and Reduced-Price Meals (DCM-F/RP) allows authorized States and school districts to use information from Medicaid data files to identify students eligible to receive free or reduced-price (F/RP) National School Lunch Program (NSLP) and School Breakfast Program (SBP) meals. The Food and Nutrition Service (FNS) contracted with Mathematica Policy Research to conduct a study of the first two years of this demonstration to describe the implementation process and explore the effects on certification, participation, Federal reimbursements, and State administrative costs. This report presents the findings from the first year of the demonstration evaluation, school year (SY) 2016–2017.

1

2 years ago

USDA Rural Development Multi Family Housing - March 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for March 2018.

1

2 years ago

USDA Rural Development Multi Family Housing - July 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for July 2016.

1

2 years ago

USDA Rural Development Multi Family Housing - January 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for January 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - February 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for February 2018.

1

2 years ago

USDA Rural Development Multi Family Housing - August 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for August 2016.

1

2 years ago

USDA Rural Development Multi Family Housing - April 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for April 2016.

1

2 years ago

USDA Rural Development (RD) Property Eligibility - Broadband

This data is used to determine eligibility for certain USDA broadband loan and grant programs.

2

2 years ago

USDA Rural Development Multi Family Housing - November 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for November 2016.

1

2 years ago

USDA Rural Development Multi Family Housing - November 2015

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program.

1

2 years ago

USDA Rural Development Multi Family Housing - March 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for March 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - December 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for December 2017.

1

2 years ago

State Distributing Agency (DA) Contacts for Disaster Situations

For information about how and where to apply for USDA commodities in disaster situations, please contact the State Distributing Agency (SDA) that administer the Food Distribution Programs in your State. This dataset provides contact information for SDAs which maintain stocks of USDA commodity foods in disaster situations.

1

2 years ago

Food Distribution Program on Indian Reservations: Persons Participating

Provide Persons participating in Food Distribution Program on Indian Reserves on State level.

1

2 years ago

Farm to School Census Explorer Tool

This dataset allows users to drill-down into the data from the USDA Farm to School Census. Once you’ve conducted your query, you can easily download your results in an excel file.

1

2 years ago

FNS' Food Assistance Program Cost Information Report

This report contains key data regarding the cost of FNS' food assistance programs. The report summarizes data submitted by various reporting agencies for the United States during fiscal year 2013 and fiscal year 2014.

1

2 years ago

FNS Regions

This dataset provides location and contact information for FNS regions.

1

2 years ago

Examining the Growth of Zero-Income SNAP Caseload: Characteristics, Circumstances, and Dynamics of Zero-Income SNAP Participants

This study describes the characteristics, circumstances, and participation and income dynamics of zero-income SNAP households and seeks to assess whether economic and policy changes may have affected this growth.

1

2 years ago

Evaluation of the Elementary Schools Pilot Project for Canned, Frozen, or Dried Fruits and Vegetables

This report presents results from a pre/post study comparing the fall of 2014 with the spring of 2015, to evaluate the impacts of a Pilot project under which States had the option to serve canned, frozen, and dried fruits and vegetables.

1

2 years ago

Indicators of Diet Quality Nutrition and Health for Americans by Program Participation Status

This study is the fourth in a series that uses the National Health and Nutrition Examination Survey data to examine the relationship between SNAP participation and indicators of diet quality, nutrition, and health. As in previous studies, this study compares SNAP participants with income-eligible and higher income nonparticipants, by age and gender.

1

2 years ago

WIC Participant and Program Characteristics 2018 Food Packages and Costs Report - Full Report

This report supplements FNS administrative data on total food costs by estimating the average monthly food costs for each WIC participant category and food package type. It also estimates total pre- and post-rebate dollars spent on 18 major categories of WIC-eligible foods in FY 2018. This report is an update to the previous WIC Food Package Report for FY 2016 and WIC Food Package Costs Report for FY 2014.

1

2 years ago

Evaluation of Alternatives to Improve Elderly Access to SNAP

Historically, approximately a third of the eligible elderly population has participated in the Supplemental Nutrition Assistance Program (SNAP), the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA). In response to the low participation levels and unique economic circumstances of elderly households, FNS has implemented specific eligibility criteria for elderly households, and has developed several demonstration projects and opportunities to waive federal regulations that seek to address elderly access to SNAP.

1

2 years ago

Dietary Guidelines for Americans, 2010

The new 2010 Dietary Guidelines for Americans focus on balancing calories with physical activity, and encourage Americans to consume more healthy foods like vegetables, fruits, whole grains, fat-free and low-fat dairy products, and seafood, and to consume less sodium, saturated and trans fats, added sugars, and refined grains.

1

2 years ago

FSIS - Risk-based Listeria monocytogenes Sampling Data

Establishment specific sampling results for FSIS’ routine risk-based Listeria monocytogenes (RLm) sampling projects. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

Trends in SNAP Rates: Fiscal Year 2010 to Fiscal Year 2012 Report

This report presents the estimated percentage of individuals eligible under federal SNAP income and asset rules who choose to participate in the program

1

2 years ago

FSIS - Import Volume

Import volume for products re-inspected by FSIS at the port of entry. Both current and archive files are posted quarterly. See the FSIS website for additional information.

3

2 years ago

Reaching Those in Need: State SNAP Participation Rates for 2011

This brochure provides a graphical representation of State SNAP participation rates for 2011.

1

2 years ago

Program Integrity for Nutrition Assistance Programs

Improving stewardship of Federal money by reducing recipient fraud, reducing retailer fraud, ensuring accurate eligibility determinations, and reducing improper payments. Click on any state to Report Nutrition Assistance Fraud in that location.

1

2 years ago

Quarterly Agricultural Export Forecast

USDA quarterly forecasts for U.S. agricultural exports, in value and volume, by commodity and region

1

2 years ago

Motor Vehicle Use Map: Trails (Feature Layer)

The feature class indicates the specific types of motorized vehicles allowed on the designated routes and their seasons of use. The feature class is designed to be consistent with the MVUM (Motor Vehicle Use Map). It is compiled from the GIS Data Dictionary data and Infra tabular data that the administrative units have prepared for the creation of their MVUMs. Only trails with the symbol value of 5-12, 16, 17 are Forest Service System trails and contain data concerning their availability for motorized use. This data is published and refreshed on a unit by unit basis as needed. Individual unit's data must be verified and proved consistent with the published MVUMs prior to publication in the EDW. Click this link for full metadata description: Metadata

7

2 years ago

Integrated Resource Restoration (IRR): Point (Feature Layer)

Depicts the area of activities funded through the NFRR Budget Line Item and reported through the FACTS database. (The activities fall under number of acres treated annually to sustain or restore watershed function: acres of forestlands treated using timber sales, acres of forestland vegetation improved, acres of forestland vegetation established, acres of rangeland vegetation improved, acres treated for noxious weeds/invasive plants on NFS lands, and acres of hazardous fuels treated outside the wildland/urban interface (WUI) to reduce the risk of catastrophic wildland fire) and are self-reported by Forest Service Units. Metadata

7

2 years ago

Hydro Flow Metrics Percent Change 2080 (Map Service)

This map service represents the percent change in modeled streamflow metrics between the historical (1977-2006) and end-of-century (2070-2099) time periods in the western United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods.�These files and additional information are available on the project website,�https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from�here.

3

2 years ago

Hydro Flow Metrics Absolute Change 2040 (Map Service)

This map service represents the absolute change in modeled streamflow metrics between the historical (1977-2006) and mid-century (2030-2059) time periods in the United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods.�These files and additional information are available on the project website,�https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from�here.

3

2 years ago

FSTopo PBS Reference Quadrangle (Feature Layer)

This data portrays the FSTopo quad footprint. Quadrangles with a Vintage (greater than zero) make up the FSTopo area of interest.Within the FSTopo database, features are represented as lines, points, or polygons, with descriptive subtype attribute codes attached to describe the cartographic symbology characteristics of features. Annotation features are represented as stand-alone map text collected relative to the scale of the topographic quadrangle. The FSTopo database was originally populated with Cartographic Feature File (CFF) data which was digitized from either the Primary Base Series (PBS) quadrangles or U.S. Geological Survey (USGS) topographic map series quadrangles. Over time, the legacy CFF data is being replaced (at least partially) with data from nationally standardized sources. Data completeness reflects the content of the original source graphic, digital correction guide information, stereoscopic source, monoscopic source, supplemented with cadastral source information. Forests and Quadrangles may have undergone revision at varying dates. The update revision uses a variety of sources, including Digital Orthophoto Quad (DOQ) imagery, NAIP imagery, cadastral information, other vector data sources, and field-prepared correction guides in hardcopy or digital format.

7

2 years ago

FS National Forests Dataset (US Forest Service Proclaimed Forests)

The FS National Forests Dataset (US Forest Service Proclaimed Forests) is a depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.Metadata and Downloads - https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=Original+Proclaimed+National+Forests

9

2 years ago

Collaborative Forest Landscape Restoration Program: Polygon (Feature Layer)

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

7

2 years ago

Collaborative Forest Landscape Restoration Program: Point (Feature Layer)

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

7

2 years ago

Climate Shield Cutthroat Trout (50% Brook Trout), 2040 (Feature Layer)

This feature class represents the mid-century (2030-2059) scenario for cutthroat trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields CT_0BRK - CT_100BRK indicate the probabilities of cutthroat trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Climate Shield Cutthroat Trout (50% Brook Trout), 2080 (Feature Layer)

This feature class represents the end-of-century (2070-2099) scenario for cutthroat trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields CT_0BRK - CT_100BRK indicate the probabilities of cutthroat trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Climate Shield Cutthroat Trout (50% Brook Trout), 1980 (Feature Layer)

This feature class represents the historical (1970-1999) scenario for cutthroat trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields CT_0BRK - CT_100BRK indicate the probabilities of cutthroat trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Climate Shield Cutthroat Trout (0% Brook Trout), 2080 (Feature Layer)

This feature class represents the end-of-century (2070-2099) scenario for cutthroat trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields CT_0BRK - CT_100BRK indicate the probabilities of cutthroat trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

FIA Landcover County Estimates - 2018 (Feature Layer)

This feature class represents forest area estimates (and percent sampling error) by county for the year 2018. The data was generated from the Forest Inventory Analysis (FIA) using the EVALIDator web tool (https://apps.fs.usda.gov/Evalidator/evalidator.jsp). The areas were calculated within county limits using the US Census Bureau's county spatial data (https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html). Features and attributes of the county layer were adapted to match attributes within the FIA database (FIADB) and features have been generalized by removing vertices to enhance performance. Future iterations of this dataset will be produced using refined methods and higher resolution spatial data. Metadata and Downloads

7

2 years ago

Aquatic Organism Passage Activities (Feature Layer)

This dataset provides USFS watershed improvement activities to barriers to upstream migration. This includes improving existing passage structures or removing them entirely. Structures include culverts, dams, diversion dams. Also included are where structures have been added to purposely create barriers to protect native populations from invasive species. Data include the planned fiscal year and planned cost, the completed fiscal year, approximate completed cost, and partners involved. Each AOP activity is displayed as a single point.Metadata and Downloads

7

2 years ago

Aerial Fire Retardant Hydrographic Avoidance Areas: Aquatic (Map Service)

Aerial retardant avoidance area for hydrographic feature data are based on high resolution National Hydrographic Dataset (NHD) produced by USGS and available from the USFS. Forests and/or regions have had the opportunity to modify the default NHD water representation (300ft buffer from all water features) for their areas of interest to accurately represent aerial fire retardant avoidance areas as described in the 2011 Record of Decision for the Nationwide Aerial Application of Fire Retardant on National Forest System Land EIS. These changes have been integrated into this dataset depicting aerial fire retardant avoidance areas for hydrographic features. The following process was used to develop the hydrographic areas to be avoided by aerial fire retardant. Initially, all intermittent/ephemeral and perennial features were buffered by 300 feet by the Forest/Region units. Subsequently, Forest/Region units may have extended these buffers locally based on their requirements. There may be overlapping features that have been created during the buffering process, so one must dissolve features before calculating any area values. Data is symbolized by FCODE. Using the FCODE attribute, streams/rivers/waterbodies are categorized into perennial and intermittent/ephemeral types. Avoidance features (streams and rivers) with FCODES 46003 and 46007, as well as features (lakes and other waterbody) with FCODES 39001, 39005, 39006, 43614, 46601 are considered intermittent/ephemeral features. All other FCODES are considered to be perennial features. All underground and covered water features (e.g., pipelines) are excluded. When symbolizing this information, perennial features should be drawn on top of intermittent/ephemeral features to give the proper message. Data displays at scales larger than 1:1,250,000. Data uses a Web Mercator projection. The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Metadata and Downloads

3

2 years ago

Activity Project Areas Timber Sale (Feature Layer)

Activity Project Area Timber Sale represents an area (polygon) within which one or more Timber Sale related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and Downloads

9

2 years ago

Activity Project Areas NEPA (Feature Layer)

Activity Project Area NEPA represents an area (polygon) within which one or more activities related to the National Environmental Policy Act (NEPA) are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and Downloads

9

2 years ago

Absolute change in winter temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - October 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for October 2015.

1

2 years ago

USDA Rural Development Multi Family Housing - July 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for July 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - January 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for January 2018.

1

2 years ago

USFS Cartographic 2011 Tree Canopy Cover Hawaii (Map Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel�s values meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Hawaii TCC 2011 cartographic dataset is comprised of a single layer. The pixel values range from 0 to 99 percent. The background is represented by the value 255. The dataset has data gaps due to consistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 110.

3

2 years ago

USFS Cartographic 2011 Tree Canopy Cover CONUS (Map Service)

The National Land Cover Database 2011 (NLCD2011) percent tree canopy cover (TCC 2011) layer was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management, NASA, and the U.S. Army Corps of Engineers. One of the primary goals of the project was to generate a current, consistent, and seamless national land cover, percent tree canopy cover, and percent impervious cover at medium spatial resolution. TCC 2011 is the NLCD tree canopy cover dataset at medium spatial resolution (30 m). It was produced by the USDA Forest Service Remote Sensing Applications Center (RSAC). The TCC 2011 dataset has two layers: percent tree canopy cover and standard error. For the tree canopy cover layer, the pixel values range from 0 to 100 percent. For the standard error layer, the pixel values range from 0 to 45 percent. The standard error represents the model uncertainty associated with the corresponding pixel in the tree canopy cover layer. The tree canopy cover layer was produced using a Random Forests' regression algorithm and the standard error layer was calculated from the variance of the canopy cover estimates from the random forest regression trees.

3

2 years ago

USFS Analytical 2016 Tree Canopy Cover Standard Error CONUS (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include:The initial model outputs referred to as the Analytical data;A masked version of the initial output referred to as Cartographic data;And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel�s values meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe CONUS TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and �line up�.The USFS�s GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/and download the �FS-Cartographic� version of the 2011 and/or 2016 datasets for their cartographic applications.

3

2 years ago

USFS Analytical 2016 Tree Canopy Cover Hawaii (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

USFS Analytical 2016 Tree Canopy Cover Standard Error Hawaii (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

USFS Analytical 2016 Tree Canopy Cover CONUS (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe CONUS TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and line up. The USFS's GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/and download the FS-Cartographic version of the 2011 and/or 2016 datasets for their cartographic applications.

3

2 years ago

USFS Analytical 2011 Tree Canopy Cover Standard Error Hawaii (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

USFS Analytical 2011 Tree Canopy Cover Standard Error CONUS (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse

3

2 years ago

Monitoring Trends in Burn Severity Fire Occurrence Locations (Feature Layer)

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and the current MTBS release for CONUS, Alaska, Hawaii and Puerto Rico. Please visit https://mtbs.gov/announcements to determine the current release. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. Metadata

7

2 years ago

Monitoring Trends in Burn Severity Burned Area Boundaries (Feature Layer)

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector polygon of the location of all currently inventoried and mappable MTBS fires occurring between calendar year 1984 and the current MTBS release for CONUS, Alaska, Hawaii and Puerto Rico. Please visit https://mtbs.gov/announcements to determine the current release.  Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery. Metadata

7

2 years ago

Knutson-Vandenberg (Feature Layer)

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. The Knutson-Vandenberg Act (K-V) of June 9, 1930 (16 U.S.C. 576-576b; 46 Stat. 527), as amended by the National Forest Management Act of October 22, 1976 (16 U.S.C. 1600 et seq.) authorized collection of deposits from federal timber purchasers for prompt and efficient use of funds to reestablish, protect, and improve the production of renewable resources on timber sale areas. This includes performing soil improvement and watershed restoration, wildlife habitat improvement, control of insects, disease, and noxious weeds, tree planting, seeding and other cultural treatments necessary to maintain and improve land productivity. Since its creation millions of acres of National Forest System lands (NFS) have been treated and restored to resilient conditions and terrestrial and aquatic habitat improved. Public Law 109-54 of August 2, 2005, Title IV General Provisions, Sec 412 further amended the K-V Act to allow the collection and use of CWKV funds for watershed restoration, wildlife habitat improvement, to prepare timber sales, control of insects, disease, and noxious weeds, fire community protection activities, and the maintenance of forest roads within the Forest Service region in which the timber sale occurred. Provided that such activities may be performed through the use of contracts, forest product sales, and cooperative agreements. Note that these activities are to be performed by contract and not Forest Service personnel. The Forest Service used this amendment to administratively create two K-V programs within the K-V fund; CWKV (Cooperative Work, Knutson-Vandenberg, Sale Area Projects) and CWK2 (Cooperative Work, Knutson-Vandenberg, Regional Projects). This layer shows the spatial representation where activities accomplished and funded with CWKV and CWK2 funds and reported through the Forest Service Activity Tracking System (FACTS) database. It is important to note that this layer may not contain all CWKV or CWK2 accomplished activities; the spatial portion of the activity description is not currently enforced by FACTS and at this time some are optionally reported by Forest Service units. As spatial data reporting is enforced by the application and acceptant of reporting both tabular and spatial we hope to improve the quality and comprehensiveness of the data used for this layer in coming years. Metadata

9

2 years ago

Integrated Resource Restoration (IRR): Polygon (Feature Layer)

Depicts the area of activities funded through the NFRR Budget Line Item and reported through the FACTS database. (The activities fall under number of acres treated annually to sustain or restore watershed function: acres of forestlands treated using timber sales, acres of forestland vegetation improved, acres of forestland vegetation established, acres of rangeland vegetation improved, acres treated for noxious weeds/invasive plants on NFS lands, and acres of hazardous fuels treated outside the wildland/urban interface (WUI) to reduce the risk of catastrophic wildland fire) and are self-reported by Forest Service Units. Metadata

7

2 years ago

Integrated Resource Restoration (IRR): Line (Feature Layer)

Depicts the area of activities funded through the NFRR Budget Line Item and reported through the FACTS database. (The activities fall under number of acres treated annually to sustain or restore watershed function: acres of forestlands treated using timber sales, acres of forestland vegetation improved, acres of forestland vegetation established, acres of rangeland vegetation improved, acres treated for noxious weeds/invasive plants on NFS lands, and acres of hazardous fuels treated outside the wildland/urban interface (WUI) to reduce the risk of catastrophic wildland fire) and are self-reported by Forest Service Units. Metadata

7

2 years ago

Hydro Flow Metrics Percent Change 2040 (Map Service)

This map service represents the percent change in modeled streamflow metrics between the historical (1977-2006) and mid-century (2030-2059) time periods in the United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods.�These files and additional information are available on the project website,�https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from�here.

5

2 years ago

Hydro Flow Metrics Absolute Change 2080 (Map Service)

This map service represents the absolute change in modeled streamflow metrics between the historical (1977-2006) and end-of-century (2070-2099) time periods in the United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods.�These files and additional information are available on the project website,�https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from�here.

3

2 years ago

Hydro Flow Metrics 2080 (Map Service)

This map service represents modeled streamflow metrics from the end-of-century time period (2070-2099) in the United States. In addition to standard NHD attributes, the streamflow datasets include \nmetrics on mean daily flow (annual and seasonal), flood levels \nassociated with 1.5-year, 10-year, and 25-year floods; annual and \ndecadal minimum weekly flows and date of minimum weekly flow, center of \nflow mass date; baseflow index, and average number of winter floods. These files and additional information are available on the project website, https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from here.

3

2 years ago

Future winter temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future winter precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future summer temperature (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future summer precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future annual temperature (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; average temperature values were calculated as the mean of monthly minimum and maximum air temperature values (degrees C), averaged over the season of interest (annual, winter, or summer). Absolute change was then calculated between the historical and future time periods.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future annual precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Future April 1 snow water equivalent (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Climate Shield Bull Trout (0% Brook Trout), 2080 (Feature Layer)

This feature class represents the end-of-century (2070-2099) scenario for bull trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields BT_0BRK - BT_100BRK indicate the probabilities of bull trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Climate Shield Bull Trout (0% Brook Trout), 1980 (Feature Layer)

This feature class represents the historical (1970-1999) scenario for bull trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields BT_0BRK - BT_100BRK indicate the probabilities of bull trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Characteristics of masticated particles in mixed-conifer forests of the western United States: Shape, particle, and fuel load characteristics

This data publication contains the results of sorting masticated particles from mixed-conifer forests in 15 study locations. These data were collected from 2012 through 2016 as part of the MASTIDON project. The MASTIDON project was a four-year research project to study how masticated material differs when treated with different cutting machines and how the masticated particles decompose when left on the ground for multiple years. It investigated masticated materials in four states of the western United States. The project was funded by the Joint Fire Sciences Program (JFSP) and RMRS between 2013 and 2016. The masticated particles within this project were created by four different machines, including a vertical rotating head, horizontal drum, chipper, and mower. They had been decomposing in situ in wet and dry areas of Idaho, Colorado, New Mexico, and South Dakota since their initial treatment. Particles were broken down into 15 shape and three size classes. Each shape and size class was counted for total particles and weighed (in grams) for total fuel load by class. The total weights by shape and size class were then aggregated for a total fuel load for the 0.5 x 0.5 sample area at each location and converted to fuel loads for a 1 x 1 meter area. Subsamples of each shape and size class were taken to obtain specific information on the characteristics of particles in each class, such as average length, width, weight, particle density, volume, and surface area. This data publication includes field data on fuel loads, depth measurements, and bulk densities of five fuel layers; lab data from the sorting, characterization, and bulk density measurements of the fuel particles; and files describing the MASTIDON project and its goals.

3

2 years ago

School Breakfast Program Monthly Data

This dataset provides the monthly data for School Breakfast Program.

1

2 years ago

Official USDA Food Plans: Cost of Food at Home at Four Levels, U.S. Average, July 2014

The Food Plans represent a nutritious diet at four different cost levels: thrifty plan, low-cost plan, moderate-cost plan, and a liberal plan. The report is based on the costs of home-prepared meals and snacks.

1

2 years ago

My Cookbook

My Cookbook is an online tool that helps you compile your favorite recipes in one central place and search SNAP, household, and quantity recipes. You can also submit personal recipes to the repository and browse submitted cookbooks. My Cookbook also provides USDA Foods Fact Sheets.

1

2 years ago

Third National Survey of WIC Participants

The National Survey of WIC Participants (NSWP) study series is designed to describe state and local agency characteristics, examine participants’ characteristics, assess participants’ experiences with WIC, and estimate improper payments resulting from certification errors in WIC. The study is conducted approximately every 10 years, and the current study is the third iteration (NSWP-III) in the series.

1

2 years ago

State of Origin for USDA Foods in Fiscal Year 2021

The U.S. Department of Agriculture (USDA), Food and Nutrition Service’s (FNS) food distribution programs provide food and nutrition assistance to schoolchildren and families and support American agriculture by distributing high-quality, 100-percent American-grown USDA Foods. This report shows which USDA Foods products were purchased from each state. Although USDA is unable to provide state of origin information prior to ordering due to the competitive nature of the procurements, this report provides a retrospective look.

1

2 years ago

USDA Rural Development Multi Family Housing - December 2015

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for December 2015.

1

2 years ago

USDA Rural Development Multi Family Housing - August 2017

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for August 2017.

1

2 years ago

USDA Rural Development Multi Family Housing - April 2018

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for April 2018.

1

2 years ago

National School Lunch Program Monthly Data

This dataset provides the monthly data for National School Lunch Program.

1

2 years ago

FNS Regional Offices

This dataset lists all FNS regional offices as well as contact information for administrator and director's offices.

1

2 years ago

Reaching Those in Need: Estimates of USDA's SNAP Program Participation Rates in 2019

This report–part of an annual series–presents estimates of the percentage of eligible persons, by state, who participated in the U.S. Department of Agriculture's Supplemental Nutrition Assistance Program (SNAP) during an average month in fiscal year (FY) 2019 and in the two previous fiscal years. SNAP eligibility criteria include maximum income and resource thresholds, as well as certain nonfinancial criteria, such as age and disability status.

1

2 years ago

National Soil Information System (NASIS)

The National Soil Information System (NASIS) data system consists of multiple interrelated soil applications and databases. This data system aids in the collection, storage, manipulation and dissemination of soil information.

3

2 years ago

NCSS Soil Characterization Database

The National Cooperative Soil Survey - Soil Characterization Database (NCSS-SCD) contains laboratory data for more than 65,000 locations (i.e. xy coordinates) throughout the United States and its Territories, and about 2,100 locations from other countries. It is a compilation of data from the Kellogg Soil Survey Laboratory (KSSL) and several cooperating laboratories. The data steward and distributor is the National Soil Survey Center (NSSC). Information contained within the database includes physical, chemical, biological, mineralogical, morphological, and mid infrared reflectance (MIR) soil measurements, as well a collection of calculated values. The intended use of the data is to support interpretations related to soil use and management. Data Usage Access to the data is provided via the following user interfaces: 1. Interactive Web Map 2. Lab Data Mart (LDM) for querying data and generating reports 3. Soil Data Access (SDA) web services for querying data 5. Direct download of the entire database in several formats Data at each location includes measurements at multiple depths (e.g. soil horizons). However, not all analyses have been conducted for each location and depth. Typically, a suite of measurements was collected based upon assumed or known conditions regarding the soil being analyzed. For example, soils of arid environments are routinely analyzed for salts and carbonates as part of the standard analysis suite. Standard morphological soil descriptions are available for about 60,000 of these locations. Mid-infrared (MIR) spectroscopy is available for about 7,000 locations. Soil fertility measurements, such as those made by Agricultural Experiment Stations, were not made. Most of the data were obtained over the last 40 years, with about 4,000 locations before 1960, 25,000 from 1960-1990, 27,000 from 1990-2010, and 13,000 from 2010 to 2021. Generally, the number of measurements recorded per location has increased over time. Typically, the data were collected to represent a soil series or map unit component concept. They may also have been sampled to determine the range of variation within a given landscape. Although strict quality-control measures are applied, the NSSC does not warrant that the data are error free. Also, in some cases the measurements are not within the applicability range of the laboratory methods. For example, dispersion of clay is incomplete in some soils by the standard method used for determining particle-size distribution. Soils producing incomplete dispersion include those that are derived from volcanic materials or that have a high content of iron oxides, gypsum, carbonates, or other cementing materials. Also note that determination of clay minerals by x-ray diffraction is relative. Measurements of very high or very low quantities by any method are not very precise. Other measurements have other limitations in some kinds of soils. Such data are retained in the database for research purposes. Also, some of the data for were obtained from cooperating laboratories within the NCSS. The accuracy of the location coordinates has not been quantified but can be inferred from the precision of their decimal degrees and the presence of a map datum. Some older records may correspond to a county centroid. When the map datum is missing it can be assumed that data prior to 1990 was recorded using NAD27 and with WGS84 after 1995. For detailed information about methods used in the KSSL and other laboratories refer to "Soil Survey Investigation Report No. 42". For information on the application of laboratory data, refer to "Soil Survey Investigation Report No. 45". If you are unfamiliar with any terms or methods feel free to consult your NRCS State Soil Scientist. Terms of Use This dataset is not designed for use as a primary regulatory tool in permitting or citing decisions but may be used as a reference source. This is public information and may be interpreted by organizations, agencies, units of government, or others based on needs; however, they are responsible for the appropriate application. Federal, State, or local regulatory bodies are not to reassign to the Natural Resources Conservation Service or the National Cooperative Soil Survey any authority for the decisions that they make. The Natural Resources Conservation Service will not perform any evaluations of these data for purposes related solely to State or local regulatory programs.

6

2 years ago

Empirical Bayes Shrinkage Estimates of State SNAP Participation Rates

This report presents estimates that, for each state, measure the need for SNAP and the program’s effectiveness in each of the three years from 2009 to 2011.

1

2 years ago

Disaster SNAP Tool Kit

1

2 years ago

Characteristics of SNAP Households: Fiscal Year 2012

This annual report provides details on the demographic characteristics and economic circumstances of SNAP households at both the national and the State level. In 2012, one-person households comprised more than half the caseload (50.3 percent) and the average SNAP household benefit declined by $7 to $274.

1

2 years ago

FSIS - Raw Siluriformes Product Sampling Data

Establishment specific sampling results for Raw Siluriformes Products. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

FSIS - Raw Pork Products Sampling Data

Establishment specific sampling results for Raw Pork Products. Current data is updated quarterly; archive data is updated annually. See the FSIS website for additional information.

3

2 years ago

Toll-Free Numbers for Women, Infants and Children (WIC) State Agencies

Provides Toll-Free numbers for WIIC state agencies.

1

2 years ago

NLCD 2011-2016 Tree Canopy Cover Change Hawaii (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

NLCD 2011-2016 Tree Canopy Cover Change Coastal AK (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include:The initial model outputs referred to as the Analytical data;A masked version of the initial output referred to as Cartographic data;And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel�s values meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe Coastal Alaska TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of �2011 TCC + change in TCC = 2016 TCC�. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and �line up�.The USFS�s GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/and download the �FS-Cartographic� version of the 2011 and/or 2016 datasets for their cartographic applications.

3

2 years ago

NLCD 2011-2016 Tree Canopy Cover Change CONUS (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse

3

2 years ago

NLCD 2011 Tree Canopy Cover Hawaii (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

Recreation Area Activities (Feature Layer)

This dataset contains the recreation opportunity information that the Forest Service collects through the Recreation Portal and shares with the public on https://www.recreation.gov, the Forest Service World Wide Web pages (https://www.fs.usda.gov/) and the Interactive Visitor Map. This recreation data contains detailed descriptions of recreational sites, areas, activities & facilities. This published dataset consists of one point feature class for recreational areas, one spatial view and three related tables such as activities, facilities & rec area advisories. The purpose of each related table is described belowRECAREAACTIVITIES: This related table contains information about the activities that are associated with the rec area.RECAREAFACILITIES: This related table contains information about the amenities that are associated with the rec area.RECAREAADVISORIES: This table contains events, news, alerts and warnings that are associated with the rec area.RECAREAACTIVITIES_V: This spatial view/feature class is generated by joining the RECAREAACTIVITIES table to the RECREATION OPPORTUNITIES Feature Class. Please note that the RECAREAID is the unique identifier present in point feature class and in the related tables as well. The RECAREAID is used as foreign key to access relate records.This published data is updated nightly from an XML feed maintained by the CIO Rec Portal team. This data is intended for public use and distribution. Metadata

7

2 years ago

Productivity of U.S. Rangelands, Annual Data lbs/acre (Image Service)

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select any of the download options. Data can also be downloaded from the FSGeodata Clearinghouse.More information about rangeland productivity and the effects of drought are available in this StoryMap; additional drought and rangeland products from the Office of Sustainability and Climate are available in our Climate Gallery. Time enabled image service showing estimates of annual production of rangeland vegetation.Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2021 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.phpMore information about rangeland productivity and the effects of drought are available in this story map.

7

2 years ago

Percent change in winter precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

Percent change in summer precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).\n\n

3

2 years ago

Percent change in annual precipitation (Alaska) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). Monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

NLCD 2016 Tree Canopy Cover Puerto Rico Virgin Islands (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

NLCD 2016 Tree Canopy Cover Hawaii (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

NLCD 2016 Tree Canopy Cover Coastal AK (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:AnalyticalUSFS Tree Canopy Cover DatasetsUSFS Enterprise Data WarehouseCartographicUSFS Tree Canopy Cover DatasetsNLCDMulti-Resolution Land Characteristics (MRLC) ConsortiumUSFS Enterprise Data WarehouseThe Coastal Alaska TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and line up. The USFS's GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/and download the FS-Cartographic version of the 2011 and/or 2016 datasets for their cartographic applications.

3

2 years ago

NLCD 2016 Tree Canopy Cover CONUS (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse The CONUS TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. The dataset has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 127.

3

2 years ago

NLCD 2011-2016 Tree Canopy Cover Change Puerto Rico Virgin Islands (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016. The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixels values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Image Service) Cartographic USFS Tree Canopy Cover Datasets (Download) USFS Enterprise Data Warehouse (Map Service) NLCD Multi-Resolution Land Characteristics (MRLC) Consortium (Download) USFS Enterprise Data Warehouse (Image Service) The Puerto Rico and the US Virgin Islands TCC NLCD change dataset is comprised of a single layer. The pixel values range from -97 to 98 percent where negative values represent canopy loss and positive values represent canopy gain. The background is represented by the value 127 and data gaps are represented by the value 110 since this is a signed 8-bit image.

3

2 years ago

Absolute change in winter precipitation (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\n\nHistorical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.\n\nRaster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

USDA Rural Development Multi Family Housing - February 2016

Multi Family Housing: A downloadable database file that identifies pertinent information related to USDA Rural Development housing assistance and the Multi Family Housing Section 515 Program for February 2016.

1

2 years ago

Climate Shield Bull Trout (50% Brook Trout), 2040 (Feature Layer)

This feature class represents the mid-century (2030-2059) scenario for bull trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields BT_0BRK - BT_100BRK indicate the probabilities of bull trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

7

2 years ago

Recreation Opportunities (Feature Layer)

This dataset contains the recreation opportunity information that the Forest Service collects through the Recreation Portal and shares with the public on https://www.recreation.gov, the Forest Service World Wide Web pages (https://www.fs.usda.gov/) and the Interactive Visitor Map. This recreation data contains detailed descriptions of recreational sites, areas, activities & facilities. This published dataset consists of one point feature class for recreational areas, one spatial view and three related tables such as activities, facilities & rec area advisories. The purpose of each related table is described below RECAREAACTIVITIES: This related table contains information about the activities that are associated with the rec area.RECAREAFACILITIES: This related table contains information about the amenities that are associated with the rec area. RECAREAADVISORIES: This table contains events, news, alerts and warnings that are associated with the rec area.RECAREAACTIVITIES_V: This spatial view/feature class is generated by joining the RECAREAACTIVITIES table to the RECREATION OPPORTUNITIES Feature Class. Please note that the RECAREAID is the unique identifier present in point feature class and in the related tables as well. The RECAREAID is used as foreign key to access relate records.This published data is updated nightly from an XML feed maintained by the CIO Rec Portal team. This data is intended for public use and distribution. Metadata

9

2 years ago

Percent change in snow residence time (CONUS) (Image Service)

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.Snow residence time (in days) and April 1 snow water equivalent (in mm) were modeled using the spatial analog models of Luce et al., 2014 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2013WR014844); see also Lute and Luce, 2017 (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020752). These models are built on precipitation and snow data from Snowpack Telemetry (SNOTEL) stations across the western United States and temperature data from the TopoWx dataset (https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.4127). They were calculated for the historical (1975-2005) and future (2071-2090) time periods, along with absolute and percent change.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

3

2 years ago

NLCD 2011 Tree Canopy Cover CONUS (Image Service)

The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include: The initial model outputs referred to as the Analytical data; A masked version of the initial output referred to as Cartographic data; And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.The Analytical data are the initial model outputs generated in the production workflow. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2011 and 2016 are available. The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of 2011 TCC + change in TCC = 2016 TCC. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel's values meet the criterion of 2011 TCC + change in TCC = 2016 TCC. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified. These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below: Analytical USFS Tree Canopy Cover Datasets USFS Enterprise Data Warehouse Cartographic USFS Tree Canopy Cover Datasets NLCD Multi-Resolution Land Characteristics (MRLC) Consortium USFS Enterprise Data Warehouse The CONUS TCC 2011 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. The dataset has data gaps due to persistent clouds/shadows in the Landsat images used for modeling. These data gaps are represented by the value 127.

3

2 years ago

Monitoring Trends in Burn Severity Puerto Rico (Map Service)

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.�Direct Download

3

2 years ago

Hydro Flow Metrics Historical (Map Service)

This map service represents modeled streamflow metrics from the historical time period (1977-2006) in the United States. In addition to standard NHD attributes, the streamflow datasets include metrics on mean daily flow (annual and seasonal), flood levels associated with 1.5-year, 10-year, and 25-year floods; annual and decadal minimum weekly flows and date of minimum weekly flow, center of flow mass date; baseflow index, and average number of winter floods.�These files and additional information are available on the project� website,�https://www.fs.usda.gov/rm/boise/AWAE/projects/modeled_stream_flow_metrics.shtml. Streams without flow metrics (null values) were removed from this dataset to improve display speed; to see all stream lines, use an NHD flowline dataset.Hydro flow metrics data can be downloaded from�here.

3

2 years ago

Collaborative Forest Landscape Restoration Program: Line (Feature Layer)

The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information aboutactivities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service. The application allows tracking and monitoring of NEPA decisions as well as the ability to create and manage KV trust fund plans at the timber sale level. This application complements its companion NRM applications, which cover the spectrum of living and non-living natural resource information. This layer represents Collaborative Forest Landscape Restoration (CFLR) Program project activities. Also included are other High Priority Restoration projects that are funded outside of CFLR. It is important to note that this layer does not contain all of the approved project activities. Instead, these are the accomplishments that project groups uploaded to the Forest Service corporate data holdings in FACTS. As spatial data is a new requirement for the program, improvements to the quality and comprehensiveness of this data is expected in coming years. Metadata

7

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - May 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for May 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - March 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for March 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - March 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for March 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - June 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for June 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - June 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for June 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - July 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for July 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - July 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for July 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - January 2017

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for January 2017.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - January 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for January 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - January 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for January 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - February 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for February 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - February 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for February 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - December 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for December 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - August 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for August 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - August 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for August 2015.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - April 2016

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for April 2016.

1

2 years ago

Rural Development Obligations and Disbursements (non ARRA) - April 2015

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development non-ARRA program obligations for April 2015.

1

2 years ago

Rural Development Disaster Assistance

Rural Development Disaster Assistance Declarations - April30Ver2

1

2 years ago

RD Monthly Obligations and Disbursements (ARRA)

In accordance with the Federal Funding Accountability and Transparency Act of 2006 (FFATA) and the American Recovery and Reinvestment Act of 2009 (ARRA), this downloadable file identifies Rural Development ARRA program obligation and disbursement activities.

1

2 years ago

zRolesApplicationLog

Application roles and permissions

0

2 years ago

zRoles

Application roles and permissions

0

2 years ago

webTCAS

Employee Timesheet data

0

2 years ago

watershed

Supports tractreview

0

2 years ago

tractreview

Tract review spotcheck system for compliance

0

2 years ago

techreg

Technical Service Provider registration

0

2 years ago

sdm

Soils Data Mart

0

2 years ago

program

NRCS Programs listing

0

2 years ago

prms

Legacy progress reporting system

0

2 years ago

oipdb

Office Information Profile.

0

2 years ago

navsde

Legacy Supporting spatial database for NRCS applications

0

2 years ago

navigation

Supporting spatial database for NRCS applications

0

2 years ago

nasischg

Nasis accumulation of change transactions

0

2 years ago

mrtxdb

Master Reference Tables

0

2 years ago

iasprofile

Legacy Systems Permissions

0

2 years ago

iascustomer

Legacy Client Records

0

2 years ago

iascams

Personnel data feed

0

2 years ago

iasaccount

Legacy User Management

0

2 years ago

geoObserverDams

Inventory for managing NRCS dams

0

2 years ago

geoObserverDB

Inventory for managing NRCS dams

0

2 years ago

eContractsUsers

NRCS agreements and cost-sharing data

0

2 years ago

eContractsLedger

NRCS agreements and cost-sharing data

0

2 years ago

eContracts

NRCS agreements and cost-sharing data

0

2 years ago

eConsplanMessages

Legacy National Conservation Planning database

0

2 years ago

eConsplan

Legacy National Conservation Planning database

0

2 years ago

WebRoles

Legacy application permissions

0

2 years ago

WAI

Supporting spatial database for NRCS applications

0

2 years ago

USDA Service Center Locator

This site provides addresses, telephone numbers and other key information for USDA Service Center locations. These office locations will provide customers with information and assistance for available disaster programs. Agencies shown include the Farm Service Agency, Rural Development and the Natural Resources Conservation Service.

1

2 years ago

U.S. General Soil Map (STATSGO2) for Individual States

This dataset consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) dataset published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped of 1:250,000 in the continental U.S., Hawaii, Puerto, and the Virgin Islands and 1:1,000,000 in Alaska. The dataset was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the entire map unit. This dataset consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1- by 2-degree topographic quadrangle units. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. These data provide information about soil features on or near the surface of the Earth. Data were collected as part of the National Cooperative Soil Survey. These data are intended for geographic display and analysis at the state, regional, and national level. The data should be displayed and analyzed at scales appropriate for 1:250,000-scale data.

1

2 years ago

TSPR

Technical Service Provider registration

0

2 years ago

SDW

Soils Data Warehouse

0

2 years ago

ReportServer

Legacy SQL 2005 SSRS database

0

2 years ago

ProtrainRS

ProTracts training report server

0

2 years ago

ProtractsCMT

Conservation Measurement Tool - Application Ranking

0

2 years ago

ProTractsReports

ProTracts reporting data

0

2 years ago

ProTractsDW

ProTracts data warehouse

0

2 years ago

PlacesMBRMetrics

Supporting spatial database for NRCS applications

0

2 years ago

PlacesMBR

Supporting spatial database for NRCS applications

0

2 years ago

PhotoGallery-B

Internal Conservation-related pictures

0

2 years ago

PhotoGallery-A

Conservation-related pictures for download

1

2 years ago

PSA

Payment Schedule Application - Cost Lists

0

2 years ago

PRSGoals

Supports NRCS Goals application

0

2 years ago

PRS

Performance Results System - progress reporting data

0

2 years ago

PRISM

Climate data provided by the National Water & Climate Center

1

2 years ago

POINTS

Program Operations data

0

2 years ago

PMMART

PRS marted data

0

2 years ago

NRT

NRCS Reference Tables

0

2 years ago

NRCSSpatialmart

Supporting spatial domains for geoObserver Apps

0

2 years ago

NRCSRoot

Review of Open Obligations Tool - Internal obligation reconciliation database

0

2 years ago

NRCSOpData

Operations branch log

0

2 years ago

NRCSAcct

Operations branch log of accounts

0

2 years ago

NPAD-D

Mobile conservation planning applications

0

2 years ago

NPAD-C

Client access to conservation plan information

0

2 years ago

NPAD-B

Conservation planning system

0

2 years ago

NPAD-A

Application roles and permissions

0

2 years ago

NFAT

Energy Tools - Nitrogen Estimator

1

2 years ago

NEST

Inventory of Easement contracts and management actions.

0

2 years ago

NEDCtraining

eLearning support database

0

2 years ago

Mappersde

ArcMap supporting data

0

2 years ago

LinkMgrAudit

Links SCIMS with eAuth accounts

0

2 years ago

Ideamartspatial

Online data query and reporting system for field offices

0

2 years ago

IdeaRS

Online data query and reporting system for field offices

0

2 years ago

IdeaMart

Online data query and reporting system for field offices

0

2 years ago

IPAT

Energy Tools - Irrigation Estimator

1

2 years ago

GatewayOrders

Geospatial Data Layers available to public via Geospatial Gateway

1

2 years ago

GatewayMetrics

Geospatial Data Layers available to public via Geospatial Gateway

1

2 years ago

GatewayCatalog

The Geospatial Data Gateway (GDG) is the One Stop Source for environmental and natural resources data, at anytime, from anywhere, to anyone.

1

2 years ago

GAMS

Grants and Agreements inventory and management

0

2 years ago

Fundmanager

NRCS agreements and cost-sharing data

0

2 years ago

FMMART

Financial Management data mart

0

2 years ago

FFISVendor

NFC data on payees

0

2 years ago

FFIS

NFC financial system data

0

2 years ago

EnergyCalc

Energy Calculators

1

2 years ago

Eligibility

Interagency client data from FSA

0

2 years ago

DOD

Database of Domains

0

2 years ago

Conservation Effects Assessment Project (CEAP) Cropland Baseline Conservation Condition for Upper Mississippi River Basin

The dataset consists of estimates of erosion, sediment loss, soil organic carbon, nutrient loss, and pesticide loss from a statistically selected set of sample points within the Upper Mississippi River Basin. Results for the Baseline Conservation Condition are reported for the region as a whole and for each of the 14 subbasins within the region.

1

2 years ago

COP

Cost of Practices, practice average cost

0

2 years ago

COMET-FARM

COMET-FARM is a whole farm and ranch carbon and greenhouse gas accounting system.

1

2 years ago

CARS

Comprehensive Agency Reporting System

0

2 years ago

Appeals & Equitable Relief

Internal tracking system

0

2 years ago

AWDB

Atmospheric & Water Database available to public through National Water and Climate Center

1

2 years ago

ABCReports

Activity Based Costing Reporting Mart

0

2 years ago

ABC

Activity Based Costing

0

2 years ago

Warehouses Cancelled and/or Terminated in CY 2010

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2010

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2009

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2009

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2008

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2008

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2007

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2007

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2006

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2006

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2005

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2005

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2004

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2004

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2003

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2003

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2002

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2002

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2001

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2001

1

2 years ago

Warehouses Cancelled and/or Terminated in CY 2000

List of United States Warehouse Act warehouse licenses revoked, by commodity and state for calendar year 2000

1

2 years ago

USWA Warehouses Removed/Suspended/Reinstated

List of Unites State Warehouse Act Warehouses removed, suspended or reinstated

1

2 years ago

USWA Licensed Warehouses Map

Clickable map tool to locate and find information about United States Warehouse Act Licensed Warehouses

1

2 years ago

USWA Active Warehouses

United States Warehouse Act Licensed Warehouse(s) for state selected. Listed by city and county.

1

2 years ago

Sweetener Market Data Report FY 2009 - Revised

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Report FY 2008

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Report FY 2007

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Report FY 2006

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Report FY 2005

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Report FY 2004

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Report FY 2003

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Report FY 2002

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks, production, imports, sales, and deliveries to FSA on a monthly basis.

1

2 years ago

Sweetener Market Data Historical Stocks

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of stocks on a monthly basis.

1

2 years ago

Sweetener Market Data Historical Sales

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of sales by type of processor on a monthly basis.

1

2 years ago

Sweetener Market Data Historical Production

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of production on a monthly basis.

1

2 years ago

Sweetener Market Data Historical Imports and Exports

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of imports and exports on a monthly basis.

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Wholesale

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Wholesale Grocers, Jobbers and Sugar Dealers" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - West

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use in the West region on a monthly basis. Uses include "Bakery, Cereal and Related Products"; "Confectionery and Related Products"; "Ice Cream and Related Products"; "Beverages"; "Canned, Bottled and Frozen Foods"; "Multiple and All Other Food Uses", "Non-Food Uses", "Hotels, Restaurants and Institutions"; "Wholesale Grocers, Jobbers and Dealers", "Retail Grocers and Chain Stores", "Deliveries to Government Agencies", and "All Other Deliveries".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - South

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use in the South region on a monthly basis. Uses include "Bakery, Cereal and Related Products"; "Confectionery and Related Products"; "Ice Cream and Related Products"; "Beverages"; "Canned, Bottled and Frozen Foods"; "Multiple and All Other Food Uses", "Non-Food Uses", "Hotels, Restaurants and Institutions"; "Wholesale Grocers, Jobbers and Dealers", "Retail Grocers and Chain Stores", "Deliveries to Government Agencies", and "All Other Deliveries".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Retail

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Retail Grocers and Chain Stores" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Puerto Rico

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use in Puerto Rico on a monthly basis. Uses include "Bakery, Cereal and Related Products"; "Confectionery and Related Products"; "Ice Cream and Related Products"; "Beverages"; "Canned, Bottled and Frozen Foods"; "Multiple and All Other Food Uses", "Non-Food Uses", "Hotels, Restaurants and Institutions"; "Wholesale Grocers, Jobbers and Dealers", "Retail Grocers and Chain Stores", "Deliveries to Government Agencies", and "All Other Deliveries".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - North Central

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use in the North Central region on a monthly basis. Uses include "Bakery, Cereal and Related Products"; "Confectionery and Related Products"; "Ice Cream and Related Products"; "Beverages"; "Canned, Bottled and Frozen Foods"; "Multiple and All Other Food Uses", "Non-Food Uses", "Hotels, Restaurants and Institutions"; "Wholesale Grocers, Jobbers and Dealers", "Retail Grocers and Chain Stores", "Deliveries to Government Agencies", and "All Other Deliveries".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Non-Food

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Non-Food Uses" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - New England

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use in the New England region on a monthly basis. Uses include "Bakery, Cereal and Related Products"; "Confectionery and Related Products"; "Ice Cream and Related Products"; "Beverages"; "Canned, Bottled and Frozen Foods"; "Multiple and All Other Food Uses", "Non-Food Uses", "Hotels, Restaurants and Institutions"; "Wholesale Grocers, Jobbers and Dealers", "Retail Grocers and Chain Stores", "Deliveries to Government Agencies", and "All Other Deliveries".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Multiple

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Multiple and All Other Food Uses" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Mid Atlantic

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use in the Mid Atlantic region on a monthly basis. Uses include "Bakery, Cereal and Related Products"; "Confectionery and Related Products"; "Ice Cream and Related Products"; "Beverages"; "Canned, Bottled and Frozen Foods"; "Multiple and All Other Food Uses", "Non-Food Uses", "Hotels, Restaurants and Institutions"; "Wholesale Grocers, Jobbers and Dealers", "Retail Grocers and Chain Stores", "Deliveries to Government Agencies", and "All Other Deliveries".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Ice Cream

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Ice Cream and Dairy Products" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Hotels

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Hotels, Restaurants and Institutions" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Government

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Government Agencies" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Confectionery

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Confectionery and Related Products" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Canned

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Canned, Bottled and Frozen Foods" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Beverages

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Beverages" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - Bakery

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Bakery, Cereal and Related Products" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Use - All Other Uses

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "All Other Uses" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

1

2 years ago

Sweetener Market Data Historical Deliveries by Package Size

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by package size on a monthly basis. Package sizes include "Packages < 50 lbs", "Packages 50 lbs and Greater", and "Unpackaged (Bulk)"

1

2 years ago

Receivables

Information relating the collections of funds, for FSA agriculture programs, Agency Contracts and Receivables from an individual, organizations or other Government Agencies

0

2 years ago

Program Solicitation

Information detailing contract offer and bidding for the providing of services and commodity transportation, storage or processing as issued by FSA for support of agricultural based programs.

0

2 years ago

Program Performance

Information used to determine the effectiveness of Agriculture programs offered by FSA as to enrollments, approvals, funds disbursements, collections and business analytics.

0

2 years ago

Program Enrollment

Information detailing the enrollment of individuals and/or organizations into FSA offered agriculture program offerings.

0

2 years ago

Program Eligibility

Information detailing the post enrollment eligibility determination processing of individuals and organizations who have enrolled for FSA administered agriculture program offerings and services.

0

2 years ago

Program Disbursement of Benefit

Information supporting the allocation of and disbursement of approved funds for FSA agriculture programs at either FSA, individual or organization levels .

0

2 years ago

Program Definition and Rules

All the information about a specific agricultural program offered by FSA, the specified rules for eligibility, disbursement and possible repayment options and continuing service activity.

0

2 years ago

Program Approval / Award

Information detailing the enrollment approval process of individuals or organizations which seek services or offerings from FSA agriculture programs. This information can also include the awarding of solicitations as issued from the FSA.

0

2 years ago

Planting / Harvest (Yield)

Information detailing the crop yields, resulting commodities or livestock production for a specific period of time.

0

2 years ago

Payables

Information supporting the disbursement of approved funds for FSA agriculture programs, Agency contract and payables to an individual, organizations or other Government Agencies

0

2 years ago

Organization

Information detailing organizations or businesses (which are comprised of individuals) which have some sort of affiliation to FSA offerings. These would include the FSA organization itself.

0

2 years ago

Noninsured Crop Disaster Assistance Program

USDA's Farm Service Agency's (FSA) Noninsured Crop Disaster Assistance Program (NAP) provides financial assistance to producers of noninsurable crops when low yields, loss of inventory or prevented planting occur due to a natural disaster.

1

2 years ago

Livestock Management

Information detailing the management of livestock through grazing, land management and regulated facilities.

0

2 years ago

Livestock Indemnity Program

The 2014 Farm Bill makes the Livestock Indemnity Payments (LIP) a permanent program and provides retroactive authority to cover eligible livestock losses back to Oct. 1, 2011. LIP provides compensation to eligible livestock producers who have suffered livestock death losses in excess of normal mortality due to adverse weather and attacks by animals reintroduced into the wild by the federal government or protected by federal law, including wolves and avian predators. LIP payments are equal to 75 percent of the market value of the applicable livestock on the day before the date of death of the livestock as determined by the Secretary.

1

2 years ago

Livestock Forage Disaster Program Eligibility Maps

These maps depict the Livestock Forage Disaster Program eligibility by county for the US and Puerto Rico from 2008 to the present, based on grazing periods, drought intensity, and forage types.

1

2 years ago

Livestock Forage Disaster Program

The 2014 Farm Bill makes the Livestock Forage Disaster Program (LFP) a permanent program and provides retroactive authority to cover eligible losses back to Oct. 1, 2011. LFP provides compensation to eligible livestock producers who have suffered grazing losses due to drought or fire. LFP payments for drought are equal to 60 percent of the monthly feed cost for up to five months. LFP payments for fire on federally managed rangeland are equal to 50 percent of the monthly feed cost for the number of days the producer is prohibited from grazing the managed rangeland, not to exceed 180 calendar days. The grazing losses must have occurred on or after Oct. 1, 2011. Sign-up will begin on or before April 15, 2014, at any local Farm Service Agency (FSA) service center. Additional details on the types of information required for an application will be provided as part of the sign-up announcement. Some eligibility restrictions may apply. Please consult your local FSA office for details.

1

2 years ago

Livestock

Reference catalogs of ranch, dairy or fishery grown agricultural products.

0

2 years ago

Licensing and Inspection Services

Information which supports the licensing of eligible contractors of services or the licensing and inspection of agriculture facilities.

0

2 years ago

Information Technology Internal Services and Support

Information which details the procurement, management, development or support of information technology driven agency solutions and services.

0

2 years ago

Individual

Any kind of information required about individual people having some kind of affiliation with the FSA. These would include agency personnel as well.

0

2 years ago

Human Resource Management

Information supporting the management, payment and benefits of FSA personnel.

0

2 years ago

Geospatial Land Verification

Images of specific land units as produced by aerial photography or satellite technologies.

0

2 years ago

Geographic Location

Information which constitutes the geography or location of a land unit, farm, ranch or facility. This could include latitudinal/longitudinal points, boundaries, borders, addresses.

0

2 years ago

General Ledger Accounting

Information relating to the transactional accounting, for the exchange of funds between an Individual, Organizations or other Government Agencies and FSA.

0

2 years ago

Farm Service Agency State News Widget

This Widget provides access to all FSA State National News releases. The widget may be embedded into your website or blog with code provided using either Flash or Javascript.

1

2 years ago

Farm Service Agency Notices

Feed of administrative notices published by the US Department of Agriculture, Farm Service Agency.

1

2 years ago

Farm Service Agency News and Events Widget

This Widget provides access to all FSA National News releases. The widget may be embedded into your website or blog with code provided using either Flash or Javascript.

1

2 years ago

Farm Service Agency News Releases

Feed of news releases from the US Department of Agriculture, Farm Service Agency.

1

2 years ago

Farm Service Agency Market News Widget

This Widget provides access to all FSA Daily Terminal Market Prices information releases. The widget may be embedded into your website or blog with code provided using either Flash or Javascript.

1

2 years ago

Farm Service Agency Emergency Designation News Releases

Feed of news releases from the US Department of Agriculture, Farm Service Agency.

1

2 years ago

Farm Programs Payments

Payments made by the Department of Agriculture, Farm Service Agency to US agricultural producers participating in Farm Bill programs including commodity, price support, disaster assistance and conservation. Payments may be searched by payee, program, year, commodity, state, county, farm, payment date and amount paid.

1

2 years ago

Farm Practices

Farming practices applied to planted crops or that have an effect on harvested yield..

0

2 years ago

Farm Ownership Loans (Direct and Guaranteed)

"The Farm Service Agency (FSA) makes farm ownership loans to farmers and ranchers who are temporarily unable to obtain private, commercial credit at reasonable rates and terms. Farm ownership loans are used to purchase farmland, construct and repair buildings, and make farm improvements. Both guaranteed and direct loans are available through this program. FSA guaranteed loans provide lenders (e.g., banks, Farm Credit System institutions, credit unions) with a guarantee of up to 95 percent of the loss of principal and interest on a loan. The maximum FSA guaranteed farm ownership loan is $1,302 ,000 (adjusted annually based on inflation). Your lender can tell you if a guarantee is the right loan for you. Applicants who are unable to qualify for a guaranteed loan may be eligible for a direct loan from FSA. Direct loans are made and serviced by FSA officials using government funds. FSA provides direct loan customers with supervision and credit counseling so that they have a greater chance to be successful. The maximum direct farm ownership loan is $300,000."

1

2 years ago

Farm Operating Loans (Direct and Guaranteed)

"The Farm Service Agency (FSA) offers farm operating loans to farmers who are temporarily unable to obtain private, commercial credit at reasonable rates and terms. Operating loans are used to purchase items such as livestock and feed, machinery and equipment, fuel, farm chemicals, and insurance; pay family living expenses and general farm operating expenses; and make minor improvements or repairs to buildings and fencing. Both guaranteed loans and direct loans are available through this program. FSA guaranteed loans provide lenders (e.g., banks, Farm Credit System institutions, credit unions) with a guarantee of up to 95 percent of the loss of principal and interest on a loan. The maximum FSA guaranteed operating loan is $1,302,000 (adjusted annually based on inflation). Applicants unable to qualify for a guaranteed loan may be eligible for a direct loan from FSA. Direct loans are made and serviced by FSA officials, who also provide borrowers with supervision and credit counseling. The maximum amount for a direct farm operating loan is $300,000. FSA also provides Microloans, which are direct operating loans designed to meet the unique financial operating needs of many socially disadvantaged and beginning farmers, niche farm operations, the smallest of family farm operations, and those serving local and regional food markets, including urban farmers. The maximum loan amount for a Microloan is $35,000. The repayment terms vary according to the type of loan made, collateral securing the loan, and the applicant's ability to repay. Term operating loans are normally repaid within 7 years and annual operating loans are generally repaid within 12 months or when the commodities produced are sold."

1

2 years ago

Farm Loans Disaster Set-Aside Program

When Farm Service Agency (FSA) borrowers located in designated disaster areas or contiguous (adjoining) counties are unable to make their scheduled payment on any FSA debt, FSA is authorized to consider set-aside of one payment to allow the operation to continue. This program is authorized under Section 331A of the Consolidated Farm and Rural Development Act. Assistance is available in counties, or contiguous counties, who have been designated as emergencies by the President, Secretary or FSA Administrator.

1

2 years ago

Farm Layout

The organization of a farm or ranch that details land usage and the available acreage for agricultural production.

0

2 years ago

Farm Emergency Loans

"The U.S. Department of Agriculture's (USDA) Farm Service Agency (FSA) provides emergency loans to help farmers and ranchers who own or operate a farm/ranch located in a county declared by the President or designated by the Secretary of Agriculture as a primary disaster area or quarantine area. Emergency loan funds may be used to: Restore or replace essential property Pay all or part of production costs associated with the disaster year Pay essential family living expenses Reorganize the farming operation Refinance certain debts, excluding real estate Loan applicants may borrow up to 100 percent of their total actual production and/or physical losses. The maximum loan amount is $500,000. Loans for crops, livestock, and non-real estate losses have a repayment term usually between 1 to 7 years depending upon the loan purpose, collateral, and repayment ability. Loans for physical losses to real estate normally have a 30-year repayment term, not to exceed 40 years."

1

2 years ago

Facility Location

Information which details the location of a facility or specific areas of usage within a facility. For example bins within a storage facility or floor plan layouts of office buildings.

0

2 years ago

Facilities Management

The management of contracts for the leasing or renting of a facility. These facilities could be for cargo loading/unloading, both long or short term commodity storage and building office space, supply and operational services..

0

2 years ago

FSA District Director Boundaries

The FSA district boundaries are internal administrative collections of counties that are established at the state level. FSA does not have a formal national geospatial layer for District Directors’ district boundaries. The Deputy Administrator for Field Operations (DAFO) has been working with the states to update/adjust the number of districts in each state. DAFO manages these boundaries and they change as office closures and resource changes occur. Such a layer could be created, but the updated information must be received before a boundary file can be created. If a geographic boundary file were to be created, FSA would have to organize a regular cycle when DAFO provides updated information as changes occur, establish a process for creating that geospatial boundary layer, and determine where and how FSA would host this and make it accessible for the future. With the exception of NAIP imagery, none of the other geospatial layers are publicly releasable data. Under these circumstances, FSA has information that routinely changes and does not have the resources to provide this geospatial data at this time. Such spatial data would be primarily useful for FSA internal administrative use.

0

2 years ago

Emergency Haying and Grazing of Conservation Reserve Program

Emergency haying and grazing of CRP acreage may be authorized to provide relief to livestock producers in areas affected by a severe drought or similar natural disaster. Emergency authorization is provided by either a national FSA office authorization or by a state FSA committee determination utilizing the U.S. Drought Monitor.

1

2 years ago

Emergency Forest Restoration Program

The Emergency Forest Restoration Program (EFRP) helps the owners of non-industrial private forests restore forest health damaged by natural disasters. The EFRP does this by authorizing payments to owners of private forests to restore disaster damaged forests. The local FSA County Committee implements ERFP for all disasters with the exceptions of drought and insect infestations. In the case of drought or an insect infestation, the national FSA office authorizes ERFP implementation.

1

2 years ago

Emergency Conservation Program for Agricultural Producers

The U.S. Department of Agriculture (USDA) Farm Service Agency’s (FSA) Emergency Conservation Program (ECP) provides emergency funding and technical assistance to farmers and ranchers to rehabilitate farmland damaged by natural disasters and for implementing emergency water conservation measures in periods of severe drought. Funding for ECP is appropriated by Congress. ECP may be available in areas without regard to a Presidential or Secretarial emergency disaster designation.

1

2 years ago

Emergency Assistance for Livestock, Honey Bees, and Farm-raised Fish

Provides Emergency relief to producers of livestock, honey bees, and farm-raised fish. Covers losses from disaster such as adverse weather or other conditions, such as blizzards and wildfires not adequately covered by any other disaster program.

1

2 years ago

Disaster Declarations USDA

State- and county-level records of disaster designations made by the US Secretary of Agriculture in response to widespread and severe drought.

1

2 years ago

Crop Year 2014 Listing of Drought-specific Disaster Designated Counties

Download a list of crop year 2014 drought-specific designated Primary and Contiguous Counties in PDF File Format.

1

2 years ago

Crop Year 2014 Listing of Disaster Designated Counties

Download a list of crop year 2014 designated Primary and Contiguous Counties in PDF File Format.

1

2 years ago

Crop Year 2014 Drought Specific Disaster Designations Map

Crop year 2014 US map at the county level shows drought-specific disaster designations across the country under USDA's amended rule.

1

2 years ago

Crop Year 2014 Disaster Map

Crop year 2014 US map at the county level shows designations across the country under USDA's amended rule. The faster, more efficient process will immediately expand assistance to more than 1,000 counties in 26 states.

1

2 years ago

Crop Year 2014 Disaster Declarations

Tabular records of State and County level records of crop year 2014 disaster designations made by the US Secretary of Agriculture.

1

2 years ago

Crop Year 2013 Listing of Drought-specific Disaster Designated Counties

Download a list of crop year 2013 drought-specific designated Primary and Contiguous Counties in PDF File Format.

1

2 years ago

Crop Year 2013 Listing of Disaster Designated Counties

Download a list of crop year 2013 designated Primary and Contiguous Counties in PDF File Format.

1

2 years ago

Crop Year 2013 Drought Specific Disaster Designations Map

Crop year 2013 US map at the county level shows drought-specific disaster designations across the country under USDA's amended rule.

1

2 years ago

Crop Year 2013 Disaster Map

Crop year 2013 US map at the county level shows designations across the country under USDA's amended rule. The faster, more efficient process will immediately expand assistance to more than 1,000 counties in 26 states.

1

2 years ago

Crop Year 2013 Disaster Declarations

Tabular records of State and County level records of crop year 2013 disaster designations made by the US Secretary of Agriculture.

1

2 years ago

Crop Year 2012 Listing of Drought-specific Disaster Designated Counties

Download a list of crop year 2012 drought-specific designated Primary and Contiguous Counties in PDF File Format.

1

2 years ago

Crop Year 2012 Listing of Disaster Designated Counties

Download a list of crop year 2012 designated Primary and Contiguous Counties in PDF File Format.

1

2 years ago

Crop Year 2012 Drought Specific Disaster Designations Map

Crop year 2012 US map at the county level shows drought-specific disaster designations across the country under USDA's amended rule.

1

2 years ago

Crop Year 2012 Disaster Map

Crop year 2012 US map at the county level shows designations across the country under USDA's amended rule. The faster, more efficient process will immediately expand assistance to more than 1,000 counties in 26 states.

1

2 years ago

Crop Year 2012 Disaster Declarations

Tabular records of State and County level records of crop year 2012 disaster designations made by the US Secretary of Agriculture.

1

2 years ago

Crop

Ground planted, greenhouse or orchid agricultural products existing in a growing or preharvest state. Commonly estimated in acreage allotment by crop and planting season.

0

2 years ago

Conservation Reserve Program Enrollment by County for 1986-2008

Conservation Reserve Program Contract Scheduled Enrollment (acreage under contract at the end of period) by fiscal year and County for 1986 to 2008.

1

2 years ago

Conservation Reserve Program Contract Expirations for 2005-2009

"Conservation Reserve Program Contract Expirations (acreage that expired and left the program) by fiscal year and State for 2005-2009."

1

2 years ago

Conservation Reserve Program Contract Expirations by County for 2009-2025

Conservation Reserve Program Contract Scheduled Expirations (acreage scheduled to expire and leave the program) by fiscal year and County for 2005-2009.

1

2 years ago

Conservation Reserve Program Average Payments by State

Average Conservation Reserve Program Rental Payments by State.

1

2 years ago

Conservation Reserve Program Average Payments by County

Average Conservation Reserve Program Rental Payments by County.

1

2 years ago

Commodity

Post harvest agricultural products which may or may not require processing prior to purchase. These products are usually evaluated by weight or container and may possibly require storage.

0

2 years ago

Budgeting

Information relating the allocation of funds for expenditures for a given period of time. This could be divided into Capital Budgets, Operating Budgets and Cash Budgets.

0

2 years ago

Affiliation and Membership Roles

The associations of individuals to/and organizations to FSA as driven by business events and by stated role within each event.

0

2 years ago

Summary of Business

The Risk Management Agency (RMA) Summary of Business includes a variety of reports, data files, and an application that provide insurance experience for commodities grown and insured. This includes the most current information, some national reports, and the ability to create ad-hoc queries. Data for the past five years, which is updated each Monday, includes all of the business data that has been validated and accepted throughout the previous week with a cutoff every Friday. Data for the older years is static and no longer updated.

1

2 years ago

Price Discovery

The Price Discovery is a web based tool that allows users to view pricing information for the following crops covered by the Common Crop Insurance and the Area Risk Protection policies: barley, canola (including rapeseed), corn, cotton, grain sorghum, rice, soybeans, sunflowers, and wheat, and coverage prices, rates and actual ending values for the Livestock Risk Protection program, and expected and actual gross margin information for the Livestock Gross Margin program.

1

2 years ago

Pesticide Data Program 2013

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2012

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2011

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2010

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Information Reporting System

The RMA Information Reporting System (RIRS) is a web based tool that allows users to create parameter driven reports for various types of RMA data such as commodity programs, insurance offer dates and prices. Users may create reports in a variety of formats such as Excel, Word, or PDF.

1

2 years ago

County Crop Programs

The Risk Management Agency (RMA) County Crop Programs provide maps and associated text files to display the insurable commodities at a county level.

1

2 years ago

Cause of Loss Historical Files

The Risk Management Agency (RMA) Cause of Loss Historical Files summarize participation information broken down by the causes of loss. Each link contains a ZIP file with compressed data containing CSV flat-files that can be imported into any standard spreadsheet and/or database for further analysis. Record description file located in each subfolder.

1

2 years ago

Agent and Insurance Provider Locators

The Risk Management Agency (RMA) provides agent and company information as a service to our customers. All data displayed is provided by insurance providers operating under a reinsurance agreement with RMA.

1

2 years ago

Actuarial Information Browser

The Actuarial Information Browser is a web based tool that allows users to view actuarial data and other information regarding commodities insured under the Federal Crop Insurance program. The information is retrieved based on the following selectable criteria: reinsurance year, commodity, insurance plan, state and county. The information is displayed in reports, including but not limited to, rates, commodity prices, and special provisions.

1

2 years ago

Wholesale Markets

This is USDA's Agricultural Marketing Service's list of wholesale markets, or facilities where wholesalers receive large quantities of commodities by rail, truck, and air from local growers as well as producers around the world for sale to grocers, restaurants, institutions, and other businesses. About 90% of wholesale markets sell fresh fruits and vegetables, but there are also seafood, meat, and flower wholesale markets.

1

2 years ago

Weekly Barge Freight Rates for Southbound Only Shipments

Table 9: Weekly Barge Freight Rates for Southbound Only Shipments

1

2 years ago

Up Bound Empty Barges

Figure 11: Up Bound Empty Barges

1

2 years ago

USDA Meat Grading Reports

Summary reports of the volume of meat gradied for quality by the USDA Agricultural Marketing Service

1

2 years ago

U.S. Gulf Vessel Loading Activity

Figure 16: U.S. Gulf Vessel Loading Activity

1

2 years ago

U.S. Grain Inspections: U.S. Gulf and PNW

Figure 15: U.S. Grain Inspections: U.S. Gulf and PNW

1

2 years ago

U.S. Export Balances and Cumulative Exports

Table 12: U.S. Export Balances and Cumulative Exports

1

2 years ago

Truck Rates For Selected Southern Brazilian Soybean Export Transportation Routes

Compares monthly truck rates from western center Paraná, north Mato Grosso, southwest Mato Grosso do Sul, and South Goiás to the port of Santos and Paranaguá. This is figure 3 of the Brazil Soybean Transportation report.

1

2 years ago

Truck Rates For Selected North, South, And Northeastern Brazilian Soybean Export Transportation Routes

Compares monthly truck rates from north Mato Grosso and East Tocantins to the ports Itaituba, Porto Velho, Santarém, São Luís, Santos, and Paranaguá. This is figure 4 of the Brazil Soybean Transportation report

1

2 years ago

Truck Rates For Selected Brazilian Soybean Export Transportation Routes

Quarterly costs of shipping a metric ton (mt) of soybeans per 100 miles by truck of 33 routes in 12 states, representing about 83 percent of Brazilian soybean production. This is table 7 of the Brazil Soybean Transportation report.

1

2 years ago

Top 5 Importers of U.S. Soybeans

Table 14: Top 5 Importers of U.S. Soybeans

1

2 years ago

Top 5 Importers of U.S. Corn

Table 13: Top 5 Importers of U.S. Corn

1

2 years ago

Top 10 Importers of U.S. Wheat

Table 15: Top 10 Importers of U.S. Wheat

1

2 years ago

Tariff Rail Rates for Unit and Shuttle Train Shipments

Table 7: Tariff Rail Rates for Unit and Shuttle Train Shipments

1

2 years ago

Tariff Rail Rates for U.S. Bulk Grain Shipments to Mexico

Table 8: Tariff Rail Rates for U.S. Bulk Grain Shipments to Mexico

1

2 years ago

Railroad Fuel Surcharges, North American Weight Average

Figure 7: Railroad Fuel Surcharges, North American Weight Average

1

2 years ago

Quarterly costs of transporting Brazilian soybeans from the southern ports to Hamburg, Germany

Quarterly total landed costs (truck and ocean) of shipping Brazilian soybeans through the ports of Santos, Paranaguá, and Rio Grande to Hamburg, Germany. This is table 4 of the Brazil Soybean Transportation report.

1

2 years ago

Quarterly Ocean Freight Rates For Shipping Soybeans From Selected Brazilian Ports To Hamburg, Germany And Shanghai, China

Shows 2005 to present quarterly ocean freight rates per metric ton from the ports of Santos, Paranaguá, Rio Grande, Santarém, São Luís, and Barcarena to Shanghai, China, and Hamburg, Germany. This is table 9 of the Brazil Soybean Transportation report.

1

2 years ago

Quarterly Costs Of Transporting Brazilian Soybeans To Shanghai, China

Compares the quarterly total landed costs (truck and ocean) of shipping Brazilian soybeans through the ports of Santos, Paranaguá, and Rio Grande to Shanghai, China, to the same period a year earlier. This is table 1 of the Brazil Soybean Transportation report.

1

2 years ago

Quarterly Costs Of Transporting Brazilian Soybeans From The Southern Ports To Shanghai, China

Quarterly total landed costs (truck and ocean) of shipping Brazilian soybeans through the ports of Santos, Paranaguá, and Rio Grande to Shanghai, China.

1

2 years ago

Quarterly Costs Of Transporting Brazilian Soybeans From The Southern Ports To Hamburg, Germany

Quarterly total landed costs (truck and ocean) of shipping Brazilian soybeans through the ports of Santos, Paranaguá, and Rio Grande to Hamburg, Germany.

1

2 years ago

Quarterly Costs Of Transporting Brazilian Soybeans From The North And Northeastern Ports To Shanghai, China

Quarterly total landed costs (truck and ocean) of shipping Brazilian soybeans through the ports of Santarém and São Luís Santos to Shanghai, China. This is table 5 of the Brazil Soybean Transportation report.

1

2 years ago

Quarterly Costs Of Transporting Brazilian Soybeans From The North And Northeastern Ports To Hamburg, Germany

Quarterly total landed costs (truck and ocean) of shipping Brazilian soybeans through the ports of Santarém and São Luís to Hamburg, Germany. This is table 6 of the Brazil Soybean Transportation report.

1

2 years ago

Quarterly Cost Of Transporting Brazilian Soybeans To Hamburg, Germany

Compares the quarterly total landed costs (truck and ocean) of shipping Brazilian soybeans through the ports of Santos, Paranaguá, and Rio Grande to Hamburg, Germany, to the same period a year earlier. This is table 2 of the Brazil Soybean Transportation report.

1

2 years ago

Poultry Market News Search

The primary function of the Livestock, Poultry, and Grain Market News Division of the Livestock, Poultry and Seed Programs is to compile and disseminate information that will aid producers, consumers, and distributors in the sale and purchase of poultry and their related products nationally and internationally.

1

2 years ago

Poultry Market News Search

The primary function of the Livestock, Poultry, and Grain Market News Division of the Livestock, Poultry and Seed Program is to compile and disseminate information that will aid producers, consumers, and distributors in the sale and purchase of poultry and their related products nationally and internationally.

1

2 years ago

Port Region Grain Ocean Vessel Activity

Table 17: Weekly Port Region Grain Ocean Vessel Activity (number of vessels)

1

2 years ago

Plant Variety Protection Office Certificate Database

The Plant Variety Protection Office (PVPO) Scanned Certificates Database is a collection of Certificates of Protection for new plant varieties that are seed reproduced or tuber propagated. A variety may be represented by seeds, transplants, plants, tubers, tissue culture plantlets and other matter. A Certificate of Protection is awarded to an owner of a variety after an examination shows that it is new, distinct from other varieties, and genetically uniformed and stable through successive generations. This tool allows stakeholders access to the breeding history and morphological characteristics used in developing new plant varieties. This tool contains over 5,000 varieties that have been issued a Certificate of Protection since 1975.

1

2 years ago

Pesticide Data Program 2009

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2008

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2007

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2006

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2005

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2004

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2003

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2002

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2001

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 2000

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1999

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1998

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1997

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1996

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1995

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1994

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1993

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program 1992

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

1

2 years ago

Pesticide Data Program

The USDA Pesticide Data Program (PDP) database provides national data on pesticide residues in food and water, with an emphasis on foods consumed by infants and children. PDP data are used primarily by EPA to prepare realistic pesticide dietary exposures for pesticide registration activities. Data for each calendar-year survey are stored in a separate dataset.

0

2 years ago

Perishable Agricultural Commodities Act Search Engine

The Perishable Agricultural Commodities Act (PACA) was enacted at the request of the fruit and vegetable industry to promote fair trade in the industry. The PACA protects businesses dealing in fresh and frozen fruits and vegetables by establishing and enforcing a code of fair business practices and by helping companies resolve business disputes. The PACA Branch is responsible for administering the PACA and offers many services to the industry. PACA Branch experts receive hundreds of telephone calls each week from companies requesting assistance on problems unique to the industry such as interpretation of inspection certificates, advice on contract disputes, and bankruptcy payments. The PACA Search Engine is an online tool that allows the public to determine if a business is licensed under the PACA, users can also see companies trade name(s), branch location(s), principal(s), and other related license information.

1

2 years ago

National Summary of Meats Graded by Month for September