Name: Landcover FIA County Estimates 2019 - Percent Forest Area
Display Field: STATE_NAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: 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 (http://apps.fs.fed.us/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.
Name: Landcover FIA County Estimates 2018 - Percent Forest Area
Display Field: STATE_NAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: 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 (http://apps.fs.fed.us/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.
Name: Landcover FIA County Estimates 2017 - Percent Forest Area
Display Field: STATE_NAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: 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 (http://apps.fs.fed.us/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.
Name: Landcover FIA County Estimates 2016 - Percent Forest Area
Display Field: STATE_NAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: 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 (http://apps.fs.fed.us/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.
Name: Landcover FIA County Estimates 2015 - Percent Forest Area
Display Field: STATE_NAME
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: 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 (http://apps.fs.fed.us/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.