Coincident maps of changing land cover, land use, and forest condition in the United States, 1985-present

Maps of land cover class are more common, and generally more accurate, than maps of land use because "use" implies management intent that may not be directly sensible by earth-observing satellites. However, many monitoring frameworks related to sustainability require land use and land cover to be explicitly differentiated. This is particularly true for forests, where natural and human-caused dynamics in tree cover often occur independently of long-term land use changes that signal deforestation. We used an extensive multi-temporal, multi-variate sample of reference points across the United States to calibrate and validate 30 m mapped time series (1985-present) of land cover, land use, and vegetation condition change. These maps comprise the Landscape Change Monitoring System (LCMS) and are served through: an interactive, open-access app; Google Earth Engine; image services; and the FSGeodata Clearinghouse. Here, we provide methods, validation metrics, and a usage example highlighting the value of differentiating use from cover in the context of model-assisted estimation of forest area using U.S. Department of Agriculture, Forest Service inventory data.

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Publication Details

Journal
Scientific Data
Published
2026-03-03
DOI
https://doi.org/10.1038/s41597-026-06743-0
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

Coincident maps of changing land cover, land use, and forest condition in the United States, 1985-present

Sean P. Healey, Ian W. Housman, Joshua P. Heyer, Jennifer Ross et al.
Scientific Data
Remote Sensing in Agriculture
article

Coincident maps of changing land cover, land use, and forest condition in the United States, 1985-present

Sean P. Healey, Ian W. Housman, Joshua P. Heyer, Jennifer Ross, Kevin Megown, Zhiqiang Yang, Elizabeth Hardwick
article en

Abstract

Maps of land cover class are more common, and generally more accurate, than maps of land use because "use" implies management intent that may not be directly sensible by earth-observing satellites. However, many monitoring frameworks related to sustainability require land use and land cover to be explicitly differentiated. This is particularly true for forests, where natural and human-caused dynamics in tree cover often occur independently of long-term land use changes that signal deforestation. We used an extensive multi-temporal, multi-variate sample of reference points across the United States to calibrate and validate 30 m mapped time series (1985-present) of land cover, land use, and vegetation condition change. These maps comprise the Landscape Change Monitoring System (LCMS) and are served through: an interactive, open-access app; Google Earth Engine; image services; and the FSGeodata Clearinghouse. Here, we provide methods, validation metrics, and a usage example highlighting the value of differentiating use from cover in the context of model-assisted estimation of forest area using U.S. Department of Agriculture, Forest Service inventory data.

Scientific Data
Natural Resources Canada (CA), Geospatial Research (United Kingdom) (GB), Rocky Mountain Research Station (US), Rocky Mountain Research (United States) (US)
U.S. Department of Agriculture
Zero hunger
Openalex Percentile: Top 45%
Remote Sensing in Agriculture
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