Evaluation of survey and remote sensing data products used to estimate land use change in the United States: Evolving issues and emerging opportunities. Issue 129 (March 2022)

• Record Type: Journal Article
• Authors: Wang, Minzi; Wander, Michelle; Mueller, Steffen; Martin, Nico; Dunn, Jennifer B.
• Is Part Of: Environmental science & policy. Issue 129(2022)
• Page Start: 68
• Page End: 78
• Publication Date: 2022-03
• Subjects: Land use change -- Survey data -- Thematic maps -- Remote sensing -- Land use classification -- Error -- Uncertainty -- Biofuel policy; Environmental policy -- Periodicals; Environmental sciences -- Periodicals; Environnement -- Politique gouvernementale -- Périodiques; Sciences de l'environnement -- Périodiques; Environmental policy; Environmental sciences; Periodicals; Electronic journals; 363.70561
• DOI: 10.1016/j.envsci.2021.12.021
• Languages: English
• ISSNs: 1462-9011

Abstract

Abstract: Transparent, consistent, and statistically reliable land use/ land cover area estimates are needed to assess land use change and greenhouse gas emissions associated with biofuel production and other land uses that are influenced by policy. As relevant studies have increased rapidly during past decades, the methods used to combine data extracted from land use land cover (LULC) surveys and remote sensing-based products and track or report sources of uncertainty vary notably. This paper reviews six data sources that are most commonly used to investigate LULC and change in the contiguous U.S. by highlighting the main characteristics, strengths and weaknesses and considering how uncertainty is assessed by the June Area Survey (JAS), the Census of Agriculture (COA), the Farm Survey Agency (FSA) acreage, the National Resources Inventory (NRI), the National Wetlands Inventory (NWI), and the Forest Inventory and Analysis (FIA); and two remote sensing-based data products, the Cropland Data Layer (CDL) and the National Land Cover Database (NLCD). The summary and conclusion identify important research gaps or challenges limiting current land use/land cover and change studies (e.g., lack of high-quality reference data and uncertainty quantification, etc.) and opportunities and emerging techniques (data fusion and machine learning) that will improve reliability of land use/land cover assessments and associated policies. Blended approaches that marry high quality ground truth data that are more finely resolved than data supplied by government surveys with multitemporal imagery are needed track use of non-agricultural lands vulnerable to agricultural expansion. These considerations are notably important as the U.S. considers the renewal and possibly revision of its Renewable Fuel Standard, which includes provisions that require monitoring of agricultural land expansion. Highlights: Producers and consumers of land use change information rely on key data sources that are being continually updated and improved. Strengths, weaknesses and data gaps associated with key survey and remote sensing products are reviewed. Trust in land use and biofuels policies that are reliant on accurate land use allocation will increase with transparency and consistency in methods used.