Abstract
The National Land Cover Database (NLCD), developed through the Multi-Resolution Land Characteristics Consortium, was initiated 30 years ago and has continually provided critical, Landsat-based land-cover and land-change information for the United States. Originally launched to address the lack of national-scale, moderate-resolution land-cover data, NLCD has evolved from the pioneering 1992 dataset into a comprehensive, annually updated product suite. Key innovations include the introduction of impervious surface mapping, forest canopy mapping, standardized Landsat mosaics, national-scale accuracy assessments, continual evolution of deep learning and artificial intelligence methodologies, and a transition toward operational, change-focused monitoring. The NLCD has become an essential resource for scientific research, land management, and policy development, with extensive adoption across federal, state, and local agencies; academia; and the private sector. The NLCD data underpin a wide array of applications, including biodiversity conservation, urban planning, hydrology, human health studies, and natural hazard assessment. As new global and high-resolution commercial land-cover products emerge, the NLCD continues to distinguish itself through its temporal depth, federal backing, and thematic consistency. Moving forward, the NLCD will maintain its niche as the leading, moderate-resolution, Delivered by long-term land-cover and land-change dataset IP: 125.17.16.94for the UnitedOn:States,Sun, ensuring continued support Copyright:for broad nationalAmericanapplicationsSocietywhilefor Photcomplementing higher-resolution and global-mapping efforts.
Cite
CITATION STYLE
Sohl, T., Jin, S., Dewitz, J., Wickham, J., Brown, J., Stehman, S., … Deering, C. (2025). Thirty Years of the U.S. National Land Cover Database: Impacts and Future Direction. Photogrammetric Engineering and Remote Sensing, 91(10), 647–659. https://doi.org/10.14358/PERS.25-00121R2
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.