Agricultural land cover for the western United States. This dataset was developed from Sagestitch, the Eastern Washington Shrubsteppe Mapping Project, and several state level GAP products (AZ, CA, NM, OR, and WA).

Agricultural land cover in the study area for the conservation assessment of Greater Sage-grouse conducted by the Western Association of Fish and Wildlife Agencies. This dataset was developed from Sagestitch, an Eastern Washington Shrubsteppe Mapping Project, several state-level Gap Analysis Program (GAP) land cover products (AZ, CA, NM, OR, and WA), National Land Cover Data (NLCD) (ND,SD,NE), and the Prairie Farm Rehabilitation Administration (PFRA) Generalized Landcover (Alberta, Saskatchewan).

Agricultural land cover in the study area for the conservation assessment of Greater Sage-grouse conducted by the Western Association of Fish and Wildlife Agencies. This dataset was developed from Sagestitch, an Eastern Washington Shrubsteppe Mapping Project, several state-level Gap Analysis Program (GAP) land cover products (AZ, CA, NM, OR, and WA), National Land Cover Data (NLCD) (ND,SD,NE), and the Prairie Farm Rehabilitation Administration (PFRA) Generalized Landcover (Alberta, Saskatchewan).

The US Department of Interior’s (USDI) Bureau of Land Management (BLM) has a long history of soil and vegetation monitoring of public rangelands it manages. However, historical monitoring data have been stored and managed at the field, district, or state level, making them difficult to compile and analyze. BLM’s Soil Vegetation Inventory Method (hereafter SVIM) program occurred between 1977 and 1983 in Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, and Wyoming. Our objective was to extract and decode vegetation cover data from the SVIM dataset in a georeferenced, digital format. Vegetation cover data are available for 22,578 SVIM site write-up areas in nine states. The wide geographic coverage of the SVIM dataset will make it an outstanding resource for researchers interested in quantifying vegetation change through time in rangelands of the western US. For several studies, the USGS created a crosswalk table to convert SVIM species codes into their modern counterparts for species within the Great Basin, USA. This crosswalk table also converted questionable or incorrect species codes (codes with mistyped codes or species that did not occur in the area) into their most likely species code based on the area in which they were sampled. This conversion was completed using a local expertise along with the USDA Plants Database (https://plants.sc.egov.usda.gov/java/).

The US Department of Interior’s (USDI) Bureau of Land Management (BLM) has a long history of soil and vegetation monitoring of public rangelands it manages. However, historical monitoring data have been stored and managed at the field, district, or state level, making them difficult to compile and analyze. BLM’s Soil Vegetation Inventory Method (hereafter SVIM) program occurred between 1977 and 1983 in Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, and Wyoming. Our objective was to extract and decode vegetation cover data from the SVIM dataset in a georeferenced, digital format. Vegetation cover data are available for 22,578 SVIM site write-up areas in nine states. The wide geographic coverage of the SVIM dataset will make it an outstanding resource for researchers interested in quantifying vegetation change through time in rangelands of the western US. For several studies, the USGS created a crosswalk table to convert SVIM species codes into their modern counterparts for species within the Great Basin, USA. This crosswalk table also converted questionable or incorrect species codes (codes with mistyped codes or species that did not occur in the area) into their most likely species code based on the area in which they were sampled. This conversion was completed using a local expertise along with the USDA Plants Database (https://plants.sc.egov.usda.gov/java/).

Location of agricultural land cover obtained from the LANDFIRE Existing Vegetation Type dataset

Location of agricultural land cover obtained from the LANDFIRE Existing Vegetation Type dataset

Quantifying Western U.S. shrublands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. The USGS NLCD team in collaboration with the BLM has produced the most comprehensive remote sensing-based quantification of Western U.S. shrublands to date. Nine shrubland ecosystem components, including percent shrub, sagebrush (Artemisia spp.), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30-m resolution by mapping region. Each region required extensive ground measurement for model training and validation, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree modeling to create component predictions. In the mapped portion (1,946,100 km²) of the total study area (2,557,556 km²), bare ground averaged 46.8%, shrub 14.4%, sagebrush 4.4%, big sagebrush 3.1%, herbaceous 22.8%, annual herbaceous 4.3% and litter 15.6%. Shrub height averaged 39.8 cm and sagebrush height 10.5 cm. Component accuracies using independent validation averaged R² values of 0.46, RMSE of 10.37 and nRMSE of 0.12, and cross validation averaged R² values of 0.72, RMSE of 5.09 and nRMSE of 0.062. Component composition strongly diverges by level III ecoregions, where 13 of 22 ecoregions are bare ground dominant, 8 are herbaceous dominant, and one is shrub dominant. Sagebrush physically covers 86,219 km², or 4.4%, of our study area, but it is present in 835,507 km², or 42.9%, of the non-masked area of our study area, underscoring its widespread distribution. This version contains some confusion between pinyon-juniper tree cover and shrubs. In a subsequent version, we have applied a more aggressive masking of tree canopy cover to each rangeland component. Specifically, we lowered the tree canopy cover threshold for exclusion from 40 to 25%. For pixels with 1-25% tree canopy cover we ensured that our primary components (shrub, herbaceous, litter, and bare ground) cover summed to 100% when added with the tree canopy. And, for the secondary components (sagebrush, big sagebrush, sagebrush height and shrub height) we reconciled to the primary component (shrub), excluding any pinyon-juniper woodlands. For the updated version with these changes applied, see https://doi.org/10.5066/P9MJVQSQ. This version of data were used as training for the Back-in-Time (BIT) fractional cover time series available at https://doi.org/10.5066/P9C9O66W. Component products can also be downloaded from www.mrlc.gov.

Quantifying Western U.S. shrublands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. The USGS NLCD team in collaboration with the BLM has produced the most comprehensive remote sensing-based quantification of Western U.S. shrublands to date. Nine shrubland ecosystem components, including percent shrub, sagebrush (Artemisia spp.), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30-m resolution by mapping region. Each region required extensive ground measurement for model training and validation, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree modeling to create component predictions. In the mapped portion (1,946,100 km²) of the total study area (2,557,556 km²), bare ground averaged 46.8%, shrub 14.4%, sagebrush 4.4%, big sagebrush 3.1%, herbaceous 22.8%, annual herbaceous 4.3% and litter 15.6%. Shrub height averaged 39.8 cm and sagebrush height 10.5 cm. Component accuracies using independent validation averaged R² values of 0.46, RMSE of 10.37 and nRMSE of 0.12, and cross validation averaged R² values of 0.72, RMSE of 5.09 and nRMSE of 0.062. Component composition strongly diverges by level III ecoregions, where 13 of 22 ecoregions are bare ground dominant, 8 are herbaceous dominant, and one is shrub dominant. Sagebrush physically covers 86,219 km², or 4.4%, of our study area, but it is present in 835,507 km², or 42.9%, of the non-masked area of our study area, underscoring its widespread distribution. This version contains some confusion between pinyon-juniper tree cover and shrubs. In a subsequent version, we have applied a more aggressive masking of tree canopy cover to each rangeland component. Specifically, we lowered the tree canopy cover threshold for exclusion from 40 to 25%. For pixels with 1-25% tree canopy cover we ensured that our primary components (shrub, herbaceous, litter, and bare ground) cover summed to 100% when added with the tree canopy. And, for the secondary components (sagebrush, big sagebrush, sagebrush height and shrub height) we reconciled to the primary component (shrub), excluding any pinyon-juniper woodlands. For the updated version with these changes applied, see https://doi.org/10.5066/P9MJVQSQ. This version of data were used as training for the Back-in-Time (BIT) fractional cover time series available at https://doi.org/10.5066/P9C9O66W. Component products can also be downloaded from www.mrlc.gov.

Quantifying Western U.S. shrublands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. The USGS NLCD team in collaboration with the BLM has produced the most comprehensive remote sensing-based quantification of Western U.S. shrublands to date. Nine shrubland ecosystem components, including percent shrub, sagebrush (Artemisia spp.), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30-m resolution by mapping region. Each region required extensive ground measurement for model training and validation, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree modeling to create component predictions. In the mapped portion (1,946,100 km²) of the total study area (2,557,556 km²), bare ground averaged 46.8%, shrub 14.4%, sagebrush 4.4%, big sagebrush 3.1%, herbaceous 22.8%, annual herbaceous 4.3% and litter 15.6%. Shrub height averaged 39.8 cm and sagebrush height 10.5 cm. Component accuracies using independent validation averaged R² values of 0.46, RMSE of 10.37 and nRMSE of 0.12, and cross validation averaged R² values of 0.72, RMSE of 5.09 and nRMSE of 0.062. Component composition strongly diverges by level III ecoregions, where 13 of 22 ecoregions are bare ground dominant, 8 are herbaceous dominant, and one is shrub dominant. Sagebrush physically covers 86,219 km², or 4.4%, of our study area, but it is present in 835,507 km², or 42.9%, of the non-masked area of our study area, underscoring its widespread distribution. This version contains some confusion between pinyon-juniper tree cover and shrubs. In a subsequent version, we have applied a more aggressive masking of tree canopy cover to each rangeland component. Specifically, we lowered the tree canopy cover threshold for exclusion from 40 to 25%. For pixels with 1-25% tree canopy cover we ensured that our primary components (shrub, herbaceous, litter, and bare ground) cover summed to 100% when added with the tree canopy. And, for the secondary components (sagebrush, big sagebrush, sagebrush height and shrub height) we reconciled to the primary component (shrub), excluding any pinyon-juniper woodlands. For the updated version with these changes applied, see https://doi.org/10.5066/P9MJVQSQ. This version of data were used as training for the Back-in-Time (BIT) fractional cover time series available at https://doi.org/10.5066/P9C9O66W. Component products can also be downloaded from www.mrlc.gov.