Not just crop or forest: an integrated land cover map for agricultural and natural areas

Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natura...

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Bibliographic Details
Published in:Scientific data 2024-01, Vol.11 (1), p.137-12, Article 137
Main Authors: Kammerer, Melanie, Iverson, Aaron L., Li, Kevin, Goslee, Sarah C.
Format: Article
Language:English
Subjects:
704/172
706/2808
Agriculture
Conservation of Natural Resources
Data Descriptor
Datasets
Forests
Habitats
Humanities and Social Sciences
multidisciplinary
Natural vegetation
Plant reproduction
Science
Science (multidisciplinary)
United States
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Summary:Due to the key role surrounding landscape plays in ecological processes, a detailed characterization of land cover is critical for researchers and conservation practitioners. Unfortunately, in the United States, land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this gap, we merged two datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce integrated ‘Spatial Products for Agriculture and Nature’ (SPAN). Our workflow leveraged strengths of the NVC and the CDL to create detailed rasters comprising both agricultural and natural land-cover classes. We generated SPAN annually from 2012–2021 for the conterminous United States, quantified agreement and accuracy of SPAN, and published the complete computational workflow. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved most conflicts, leaving only 0.6% of agricultural pixels unresolved in SPAN. These ready-to-use rasters characterizing both agricultural and natural land cover will be widely useful in environmental research and management.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-02979-w