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).
NLCD imperviousness products represent urban impervious surfaces as a percentage of developed surface over every 30-meter pixel in the United States. NLCD 2019 updates all previously released versions of impervious products for CONUS and provides integrated analysis for all Land Cover dates. It also includes a matching impervious surface descriptor layer. This descriptor layer identifies types of roads, wind tower sites, building locations, and energy production sites to allow a deeper analysis of developed features. No new imperviousness products for Alaska, Hawaii and Puerto Rico are available from NLCD 2019. (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)
Rangeland Ecological Potential - Component Cover, Cover Departure, and Vegetation States. Ecological Potential rangeland fractional cover data products represent the potential cover given the most productive, least disturbed, portion of the 1985-2020 Landsat archive. Models used to predict Ecological Potential cover were trained on ecologically intact sites where annual herbaceous cover is low, no known disturbance or land treatment has occurred over the study period, and bare ground cover is relatively lower than expectations (Read More)