Use the interface below to filter and download available NLCD products. Click (here) for NLCD Science Research Products which offer more comprehensive delineation of shrub and grass classes and information about change disturbance. For access to dynamic MRLC viewer applications and tools, click (here).
Exotic Annual Grass
The Exotic Annual Grass (EAG) abundance dataset provides early season percent cover estimate of the exotic grass species in 30m spatial resolution for a mapped year in rangeland ecosystems of western United States. We plan to release these EAG estimates multiple times each year in early growing season. EAG is a continuous field consisting of abundance of non-native grass species whose life history is complete in one growing season. Cheatgrass (Bromus tectorum) is a dominant species, but this dataset also includes Bromus arvensis L., Bromus briziformis Fisch. & C.A. Mey. Bromus catharticus Vahl, Bromus commutatus Schrad, Bromus diandrus Roth, Bromus hordeaceus L., Bromus hordeaceus spp. Hordeaceus, Bromus japonicus Thunb, Bromus madritensis L., Bromus madritensis L. ssp. rubens (L.) Duvin, Bromus racemosus L., Bromus rubens L., Bromus secalinus L., Bromus texensis (Shear) Hitchc, and medusahead (Taeniatherum caput-medusae (L.) Nevski). A main objective of releasing these maps is to provide a tool for better monitoring EAG dynamics and informing conservation and management efforts at local to regional scales. (Read More)
NLCD tree canopy cover is a 30 m raster geospatial dataset that is available for the conterminous United States. They are generated by the USDA Forest Service. These data contain percent tree canopy estimates, as a continuous variable, for each pixel across all land covers and types. Tree canopy cover is derived from multi-spectral satellite imagery and other available ground and ancillary information. 2011 through 2021 Forest canopy products are available for the conterminous United States, and will be available for coastal Alaska, Hawaii, Puerto Rico, and American Virgin Islands in the summer of 2023. (Read More)
RCMAP – Basemap (2016)
RCMAP base component products characterize the percentage of each 30-meter pixel in the Western United States covered by shrub, herbaceous, bare ground, litter, sagebrush, big sagebrush and annual herbaceous, along with estimating shrub height and sagebrush height. These products have been produced by USGS in collaboration with the Bureau of Land Management. Component products are designed to be used individually or combined to support a broad variety of applications. Please note these new Revised (071520) rangeland products will differ from the first generation of circa 2016 fractional cover maps, a more aggressive masking of tree canopy cover was applied to each rangeland component. Specifically, we have 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. (Read More)
RCMAP - Time-Series - Trends
Currently available trends statistics are for the 1985-2021 time-series. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, non-sagebrush shrub, perennial herbaceous, tree, sagebrush and shrub, rule-based error maps, and the temporal trends of each. Data characterize the percentage of each 30-meter pixel in the Western United States covered by each component for each year from 1985-2021 - providing change information for 35 years (imagery for 2012 was unavailable). Because of file size limitations, individual component products are packaged in three historic intervals including 1985-1995, 1996-2006, and 2007-2021. Trend analysis for each component is also available as a zip file for the full 1985-2021 period to help users understand the magnitude of change. (Read More)