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).
The National Land Cover Database (NLCD) provides nationwide data on land cover and land cover change at a 30m resolution with a 16-class legend based on a modified Anderson Level II classification system. NLCD 2021 represents the latest evolution of NLCD land cover products focused on providing innovative land cover and land cover change data for the Nation. NLCD 2021 offers 9 integrated epochs of land cover for years 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021. chs (2001 – 2021) and are directly comparable across the full time series and suitable for multi-temporal analysis. The NLCD 2021 release is update based, so the Land Cover and Impervious Surface products released in 2019 are unchanged and can be used directly with NLCD 2021. Science products and the change index will need to be reacquired to contain the additional 2021 change. Specific map products include:Developed classes in these years are directly derived from percent developed impervious surface and include a descriptor label that identifies the type of each impervious surface pixel. The NLCD Land Cover change index combines information from all years of land cover change and provides a simple and comprehensive way to visualize change from all 9 dates of land cover in a single layer. The change index was designed to assist NLCD users to understand complex land cover change with a single product. NLCD 2021 does not yet contain updated products for Alaska, Hawaii and Puerto Rico. (Read More)
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)
RCMAP - Future Projections - 2020s, 2050s, and 2080s
RCMAP projected cover products characterize the fractional (i.e., percentage) cover of shrub, herbaceous, bare ground, litter, sagebrush, tree, and annual herbaceous in each 30-meter pixel in the Western United States. Projected data include three time periods (2020s, 2050s, and 2080s) and two climate scenarios (Representative Concentration Pathways [RCP] 4.5 and 8.5). Component cover were harmonized so that the sum of bare ground, shrub, herbaceous, tree, and litter cover adds to ~100% and secondary components sagebrush and annual herbaceous cover were harmonized to their respective primary components of shrub and herbaceous cover, (so their cover could not be greater than their primary component). 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. Data are packaged by time-period and climate scenarios (six). Additionally, for comparison to reference conditions, a scenario based on 1991-2020 climate normal is available. (Read More)