Web Soil Survey (WSS) provides soil data and information produced by the National Cooperative Soil Survey. It is operated by the USDA Natural Resources Conservation Service (NRCS) and provides access to the largest natural resource information system in the world. The site is updated and maintained online as the single authoritative source of soil survey information. The USDA-NRCS Soil and Plant Science Division refreshes the publicly available soil survey database once a year, on October 1st.

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. The complete census includes over 260 separate commodities. This dataset is a subset of 23 commodities selected for publishing. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Agricultural commoditiesGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer: Almonds Animal Totals Barley, Cattle Chickens Corn Cotton Crop TotalsFarm Operations Government Programs Grain Grapes Hay Hogs Labor Machinery Totals Milk Producers Rice Sorghum Soybean Tractors Trucks Turkeys Wheat Winter WheatGeography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The CDL is a crop-specific land cover classification product of more than 100 crop categories grown in the United States. CDLs are derived using a supervised land cover classification of satellite imagery. The supervised classification relies on first manually identifying pixels within certain images, often called training sites, which represent the same crop or land cover type. Using these training sites, a spectral signature is developed for each crop type that is then used by the analysis software to identify all other pixels in the satellite image representing the same crop. Using this method, a new CDL is compiled annually and released to the public a few months after the end of the growing season through the online CropScape data portal.

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Government programsGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Govt Programs, Federal - Operations with ReceiptsGovt Programs, Federal - Receipts, Measured in US Dollars ($) / OperationGovt Programs, Federal - Receipts, Measured in US Dollars ($)Govt Programs, Federal, (Excl Conservation & Wetlands) - Operations with ReceiptsGovt Programs, Federal, (Excl Conservation & Wetlands) - Receipts, Measured in US Dollars ($) / OperationGovt Programs, Federal, (Excl Conservation & Wetlands) - Receipts, Measured in US Dollar ($)Govt Programs, Federal, Conservation & Wetlands - AcresGovt Programs, Federal, Conservation & Wetlands - Number of OperationsGovt Programs, Federal, Conservation & Wetlands - Operations with ReceiptsGovt Programs, Federal, Conservation & Wetlands - Receipts, Measured in US Dollars ($) / OperationGovt Programs, Federal, Conservation & Wetlands - Receipts, Measured in US Dollars ($) Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, or water) were mapped using an object-based image analysis approach and supervised classification. These data are designed for conducting geospatial analyses and for producing cartographic products. In particular, these data are intended to depict the _location of tree cover in the county. The mapping procedures were developed specifically for agricultural landscapes that are dominated by annual crops, rangeland, and pasture and where tree cover is often found in narrow configurations, such as windbreaks and riparian corridors. Because much of the tree cover in agricultural areas of the United States occurs in windbreaks and narrow riparian corridors, many geospatial datasets derived from coarser-resolution satellite data (such as Landsat), do not capture these landscape features. This dataset is intended to address this particular data gap. These data can be downloaded by county at the Forest Service Research Data Archive. Nebraska: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0038 South Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0068 North Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0067 A Kansas dataset was also developed using the same methods and is located at: Kansas data download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0052 Kansas map service: https://data-usfs.hub.arcgis.com/documents/high-resolution-tree-cover-of-kansas-2015-map-service/explore

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers.Dataset SummaryPhenomenon Mapped: Crop totalsGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: StaticData Vintage: 2022Publication Date: April 2024AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations.Commodities included in this layer:Crop Totals - Operations With SalesCrop Totals - Sales, Measured In US Dollars ($)Crop Totals, Production Contract - Operations With Production Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area.What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Analysis of ‘OC USDA Hardiness Zones’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/94f86fb0-fff7-4d6a-bed5-efc84d35733a on 26 January 2022.

--- Dataset description provided by original source is as follows ---

BY USING THIS WEBSITE OR THE CONTENT THEREIN, YOU AGREE TO THE TERMS OF USE.
Growers can determine which plants are most likely to thrive at a location. The map is based on the average annual minimum winter temperature, divided into 10-degree F zones.

Plant Hardiness Zones derived the USDA. Data generalized by Oakland County IT/GIS for viewing at larger scales.

From the USDA:

USDA Plant Hardiness Zone Map

The 2012 USDA Plant Hardiness Zone Map is the standard by which gardeners and growers can determine which plants are most likely to thrive at a location. The map is based on the average annual minimum winter temperature, divided into 10-degree F zones.

More information can be found here: http://planthardiness.ars.usda.gov/PHZMWeb/About.aspx

--- Original source retains full ownership of the source dataset ---

In the United States, agroforestry is commonly defined as a suite of land management practices that intentionally integrate woody plants (trees, shrubs, vines, etc.) with crop and/or animal production systems. Understanding agroforestry adoption in the United States is critical to serve as a baseline of existing agroforestry systems and for future planning purposes. There is growing interest in identifying where future systems are most likely to occur. Since 2017, the Census of Agriculture (COA) from the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) has asked whether farm operations have agroforestry. While the COA does not differentiate the type of agroforestry used (e.g., windbreak, silvopasture, forest farming, alley cropping, riparian forest buffer) it does provide county-level numbers of farm operations practicing agroforestry. These raw numbers, available from the NASS website in tabular format, can then be joined to county-level geospatial data to provide thematic maps. This data publication includes vector polygon spatial data in multiple formats that includes the number of farm operations reporting agroforestry, the total number of farms, and the percentage of farm operations reporting agroforestry for each county in the U.S. in 2017 and 2022. The change in the proportion of farms reporting agroforestry from 2017 to 2022 is also included.The raw data were produced by the USDA National Agricultural Statistics Survey (NASS) Census of Agriculture (COA.) The COA is completed every 5 years and is a count of U.S. farms and ranches from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the census year. It also looks at land use, ownership, production practices, income, and other characteristics. The 2017 COA was the first census to ask if producers have any of the five common agroforestry practices (windbreak, silvopasture, forest farming, alley cropping, riparian forest buffer.) NASS included the same agroforestry question in the 2022 COA, allowing for the first national-level trend analysis for agroforestry extent in the United States. The National Agroforestry Center published the first maps depicting the agroforestry results from the COA in 2017 and have now created a new series of maps to reflect newly published agroforestry data from the 2022 COA. In addition, maps showing change in agroforestry at the national scale have been created, using data from the 2017 and 2022 COA. The purpose of this project was to use the raw census numbers to create a spatial layer for visualization, mapping, and analysis purposes.For more information about these data, see Kellerman et al. (2025) and Smith et al. (2022).

The first edition of these data, Kellerman (2023, https://doi.org/10.2737/RDS-2023-0044) contains 2017 data. This second edition includes the same 2017 data, but a different source for county boundaries was used (more details below), as well as the addition to 2022 data.

The USGS, in cooperation with the U.S. Bureau of Land Management (BLM), created a series of geospatial products of the Scotts Creek Watershed in Lake County, California, using National Agriculture Imagery Program (NAIP) imagery from 2022 and Open Street Map (OSM) from 2019. The imagery was downloaded from United States Department of Agriculture (USDA) - Natural Resources Conservation Service (NRCS) Geospatial Data Gateway (https://datagateway.nrcs.usda.gov/) and Geofabrik GmbH - Open Street Map (https://www.geofabrik.de/geofabrik/openstreetmap.html), respectively. An updated trail map for the Upper Scotts Creek Watershed, including the BLM Recreational Area, was created to estimate trail densities in the watershed. A preview image of the roads and trail maps is attached to this data release (see UpperScottsCreek_Roads_and_Trails_Map_2022_USGS2022_CC0.png).

The Census of Agriculture, produced by the United States Department of Agriculture (USDA), provides a complete count of America"s farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2022, and provides an in-depth look at the agricultural industry. This layer was produced from data obtained from the USDA National Agriculture Statistics Service (NASS) Large Datasets download page. The data were transformed and prepared for publishing using the Pivot Table geoprocessing tool in ArcGIS Pro and joined to county boundaries. The county boundaries are 2022 vintage and come from Living Atlas ACS 2022 feature layers. Dataset SummaryPhenomenon Mapped: Hay productionGeographic Extent: 48 contiguous United States, Alaska, Hawaii, and Puerto RicoProjection: Web Mercator Auxiliary SphereSource: USDA National Agricultural Statistics ServiceUpdate Frequency: 5 yearsData Vintage: 2022Publication Date: April 2024 AttributesNote that some values are suppressed as "Withheld to avoid disclosing data for individual operations", "Not applicable", or "Less than half the rounding unit". These have been coded in the data as -999, -888, and -777 respectively. You should account for these values when symbolizing or doing any calculations. Commodities included in this layer:Hay - Acres HarvestedHay - Operations with Area HarvestedHay - Production, Measured in TonsHay, (Excl Alfalfa) - Acres HarvestedHay, (Excl Alfalfa) - Operations with Area HarvestedHay, (Excl Alfalfa) - Production, Measured in TonsHay, (Excl Alfalfa), Irrigated - Acres HarvestedHay, (Excl Alfalfa), Irrigated - Operations with Area HarvestedHay, Alfalfa - Acres HarvestedHay, Alfalfa - Operations with Area HarvestedHay, Alfalfa - Production, Measured in TonsHay, Alfalfa, Irrigated - Acres HarvestedHay, Alfalfa, Irrigated - Operations with Area HarvestedHay, Irrigated - Acres HarvestedHay, Irrigated - Operations with Area Harvested Geography NoteIn Alaska, one or more county-equivalent entities (borough, census area, city, municipality) are included in an agriculture census area. What can you do with this layer?This layer is designed for data visualization. Identify features by clicking on the map to reveal the pre-configured pop-up. You may change the field(s) being symbolized. When symbolizing other fields, you will need to update the popup accordingly. Simple summary statistics are supported by this data. Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Snags are a hazard to firefighters that has traditionally been managed at the field level through scouting, rapid assessment, and mitigation by avoidance or by elimination though felling. Widespread wildfires and insect/disease disturbances have resulted in an accumulation of snags across many forested landscapes, raising the risk of injury or death for firefighters and other forest workers. The National Snag Hazard Map (Riley et al. 2022) is intended to provide a landscape level view of current snag hazard to encourage awareness, assessment, and planning to mitigate snag-related risks. The National Snag Hazard Map is based on the estimated density and median height of snags greater than or equal to 7.9-in diameter at breast height and at least 10-ft tall. Snag density and median snag height are classified into hazard levels based on the logic that hazard increases with snag density and height (Dunn et al. 2019). Snag hazard is a 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 on the landscape that may require extra mitigation for safe operation, or that could be avoided to reduce risk to firefighters. The snag hazard map is not meant 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. Maintaining high situational awareness for overhead hazards is recommended regardless of the snag hazard rating. 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

Riley KL, O’Connor CD, Dunn CJ, Haas JR, Stratton RD, Gannon B (2022) A national map of snag hazard to reduce risk to wildland fire responders. Forests 13, 1160. DOI:10.3390/f13081160This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

A digital orthophoto is a georeferenced image prepared from aerial imagery, or other remotely-sensed data in which the displacement within the image due to sensor orientation and terrain relief has been removed. Orthophotos combine the characteristics of an image with the geometric qualities of a map. Orthoimages show ground features such as roads, buildings, and streams in their proper positions, without the distortion characteristic of unrectified aerial imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages, also known as orthomaps, can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols, whereas on an orthoimage all details appear just as in original aerial or satellite imagery.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoServiceFor complete information, please visit https://data.gov.

This cached map layer provides access to the 2022 USDA-NAIP-FSA imagery for the Commonwealth of Kentucky. The imagery was captured during leaf on conditions and was provided at a 60cm (~2ft) resolution.

A digital orthophoto is a georeferenced image prepared from aerial imagery, or other remotely-sensed data in which the displacement within the image due to sensor orientation and terrain relief has been removed. Orthophotos combine the characteristics of an image with the geometric qualities of a map. Orthoimages show ground features such as roads, buildings, and streams in their proper positions, without the distortion characteristic of unrectified aerial imagery. Digital orthoimages produced and used within the Forest Service are developed from imagery acquired through various national and regional image acquisition programs. The resulting orthoimages, also known as orthomaps, can be directly applied in remote sensing, GIS and mapping applications. They serve a variety of purposes, from interim maps to references for earth science investigations and analysis. Because of the orthographic property, an orthoimage can be used like a map for measurement of distances, angles, and areas with scale being constant everywhere. Also, they can be used as map layers in GIS or other computer-based manipulation, overlaying, and analysis. An orthoimage differs from a map in a manner of depiction of detail; on a map only selected detail is shown by conventional symbols, whereas on an orthoimage all details appear just as in original aerial or satellite imagery.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoServiceFor complete information, please visit https://data.gov.

Analysis of ‘Loudoun Soils’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8aed4fa9-2af8-40c3-afad-342a6adc3fa7 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

More Metadata

--- Original source retains full ownership of the source dataset ---

This cached map layer provides access to the 2022 USDA-NAIP-FSA imagery for the Commonwealth of Kentucky. The imagery was captured during leaf on conditions and was provided at a 60cm (~2ft) resolution.

A webmap showing survey flightlines and forest damage polygons mapped during the 2022 aerial detection survey conducted by the USDA-FS Forest Health Protection group in partnership with the State of Alaska Division of Forestry. Aerial surveys are conducted annually in July and August to map recent damage from insects, diseases, declines and abiotic damage. Approximately 15% of the forested area of Alaska is flown each year. Mapping generally occurs at an altitude of 500 ft at 100 mph. Surveyors detect specific types of damage (e.g., defoliation, mortality, dieback) on specific tree and shrub host species or host groups (e.g., hardwoods or conifers). Surveyors draw polygons on an electronic map on a tablet, then attribute the polygons with host, damage type, damage agent and damage severity information. Damage is ground-checked whenever possible to determine the specific damage agents responsible for aerial signatures. Some forms of damage cannot be reliably mapped from the air because they do not have a pronounced aerial signature that can be detected during annual surveys.

This cached map layer provides access to the 2022 USDA-NAIP-FSA imagery for the Commonwealth of Kentucky. The imagery was captured during leaf on conditions and was provided at a 60cm (~2ft) resolution.