Multi-Resolution Land Characteristics (MRLC) Consortium

The Multi-Resolution Land Characteristics (MRLC) consortium is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. The creation of this consortium has resulted in the mapping of the lower 48 United States, Hawaii, Alaska and Puerto Rico into a comprehensive land cover product termed, the National Land Cover Database (NLCD), from decadal Landsat satellite imagery and other supplementary datasets.

NLCD 2019 Now Available

The U.S. Geological Survey (USGS) has released a new generation of National Land Cover Database (NLCD) products named NLCD 2019 for the conterminous U.S. NLCD 2019 contains 34 different land cover products characterizing land cover and land cover change across 8 epochs from 2001-2019. Products include urban imperviousness and urban imperviousness change updated to match all landcover epochs; tree canopy and tree canopy change across 2 epochs from 2011-2016, with a 2019 and 2021 canopy suite set to be released in the next year; and RCMAP rangeland fractional component data including a 1985-2020 time-series, projections of future component cover through the 2080s, and Ecological Potential component cover. Data are available on this website either as prepackaged products or custom product areas can be interactively chosen using the viewer. NLCD 2019 represents the most comprehensive land cover database ever produced by the USGS and was specifically developed to meet the rapidly growing demand for land cover change data. NLCD is coordinated through the 10-member Multi Resolution Land Characteristics Consortium (MRLC), a two decades-long interagency federal government collaboration that has proved an exemplary model of cooperation among federal agencies to combine resources to provide digital land cover information for the Nation.

NLCD 2019 now offers land cover for years 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and impervious surface and impervious descriptor products now updated to match each date of land cover. These products update all previously released versions of landcover and impervious products for CONUS (NLCD 2001, NLCD 2006, NLCD 2011, NLCD 2016) and are not directly comparable to previous products. NLCD 2019 land cover and impervious surface product versions of previous dates must be downloaded for proper comparison. Also included with thelLand cover is the NLCD Land Cover Change Index. This index 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 also offers an impervious surface descriptor product that identifies the type of each impervious surface pixel. This product identifies types of roads, wind tower sites, building locations, and energy production sites to allow deeper analysis of developed features.

RCMAP 1985-2020 Fractional Component Time-Series Now Available

The U.S. Geological Survey (USGS), in collaboration with the MRLC consortium and Bureau of Land Management (BLM), is pleased to announce the availability of a new generation of Rangeland, Condition, Monitoring, Assessment, and Projection (RCMAP) fractional component data spanning a 1985-2020 time-series. The new time-series includes yearly cover predictions for 8 components: shrub, sagebrush, non-sagebrush shrub, herbaceous, annual herbaceous, perennial herbaceous, litter, and bare ground cover. Additionally, we produce error maps for each component, which reflect model uncertainty.

Data are available for download and on the rangelands viewer. The new generation of data update previously released 1985-2018 RCMAP (referred to as BIT) data and are not designed to be backwards compatible (1985-2018 cover predictions are different between versions). While users are encouraged to use the new generation of data, the previous version of the time-series data is archived.