The United States Department of Agriculture (USDA), also known as the Agriculture Department, is the U.S. federal executive department responsible for developing and executing federal laws related to farming, agriculture, forestry, and food. It aims to meet the needs of farmers and ranchers, promote agricultural trade and production, work to assure food safety, protect natural resources, foster rural communities and end hunger in the United States and internationally.
Available DatasetsShowing 10 of 10 results
- Best Practices in the SNAP Employment and Training ProgramThis 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.1Licence not specified10 months ago
- Barriers that Constrain the Adequacy of Supplemental Nutrition Assistance Program (SNAP) AllotmentsThis 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.1Licence not specified10 months 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 110.1Licence not specified10 months 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.1Licence not specified10 months 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.1Licence not specified10 months 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.1Licence not specified10 months 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): 2373-2384.1Licence not specified10 months 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.1Licence not specified10 months 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).1Licence not specified10 months 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).1Licence not specified10 months ago
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