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Catalog datasets represent a fraction of data in use by active WMA projects. The team is working on including more datasets to provide a complete view of data-in-use. Let us know how your experience is!

Datasets


Annual National Land Cover Database (NLCD) Collection 1.1

Domain: Land Cover

Spatial Resolution: 30 meter

Temporal Frequency: annual

Temporal Coverage: 1985 - 2024

Spatial Extent: CONUS

Source: USGS

Update Type: Static

Update Frequency: NA

Update Detail: NA

Access:

Description:

The USGS Land Cover program has combined the tried-and-true methodologies from premier land cover projects, National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP), together with modern innovations in geospatial deep learning technologies to create the next generation of land cover and land change information. The product suite is called, “Annual NLCD” and includes six annual products that represent land cover and surface change characteristics of the U.S.:

  1) Land Cover,
  2) Land Cover Change,
  3) Land Cover Confidence,
  4) Fractional Impervious Surface,
  5) Impervious Descriptor, and
  6) Spectral Change Day of Year.

These land cover science product algorithms harness the remotely sensed Landsat data record to provide state-of-the-art land surface change information needed by scientists, resource managers, and decision-makers. Annual NLCD uses a modernized, integrated approach to map, monitor, synthesize, and understand the complexities of land use, cover, and condition change. With this second release, Annual NLCD, Collection 1.1, the six products are available for the Conterminous U.S. for 1985–2024. 
        
Questions about the Annual NLCD product suite can be directed to the Annual NLCD mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or custserv@usgs.gov . See included spatial metadata for more details.

Citation:

U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products (ver. 1.1, June 2025): U.S. Geological Survey data release, https://doi.org/10.5066/P94UXNTS .

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