Data
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).
Dataset Type
Region
Time Period
Land Cover
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 2019 represents the latest evolution of NLCD land cover products focused on providing innovative land cover and land cover change data for the Nation. NLCD 2019 offers 8 integrated epochs of land cover for years 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019. 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 8 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 2019 does not yet contain updated products for Alaska, Hawaii and Puerto Rico. (Read More)
Tree Canopy
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)
