Tracking cropland transitions: A comparative analysis of U.S. land cover change data.

There are a growing number of land cover data available for the conterminous United States, supporting various applications ranging from biofuel regulatory decisions to habitat conservation assessments. These datasets vary in their source information, frequency of data collection and reporting, land...

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Main Authors: Gray Martin, Kemen Austin, Tyler Lark, Stanley Lee, Christopher M Clark
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0313880
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author Gray Martin
Kemen Austin
Tyler Lark
Stanley Lee
Christopher M Clark
author_facet Gray Martin
Kemen Austin
Tyler Lark
Stanley Lee
Christopher M Clark
author_sort Gray Martin
collection DOAJ
description There are a growing number of land cover data available for the conterminous United States, supporting various applications ranging from biofuel regulatory decisions to habitat conservation assessments. These datasets vary in their source information, frequency of data collection and reporting, land class definitions, categorical detail, and spatial scale and time intervals of representation. These differences limit direct comparison, contribute to disagreements among studies, confuse stakeholders, and hamper our ability to confidently report key land cover trends in the U.S. Here we assess changes in cropland derived from the Land Change Monitoring, Assessment, and Projection (LCMAP) dataset from the U.S. Geological Survey and compare them with analyses of three established land cover datasets across the coterminous U.S. from 2008-2017: (1) the National Resources Inventory (NRI), (2) a dataset Lark et al. 2020 derived from the Cropland Data Layer (CDL), and (3) a dataset from Potapov et al. 2022. LCMAP reports more stable cropland and less stable noncropland in all comparisons, likely due to its more expansive definition of cropland which includes managed grasslands (pasture and hay). Despite these differences, net cropland expansion from all four datasets was comparable (5.18-6.33 million acres), although the geographic extent and type of conversion differed. LCMAP projected the largest cropland expansion in the southern Great Plains, whereas other datasets projected the largest expansion in the northwestern and central Midwest. Most of the pixel-level disagreements (86%) between LCMAP and Lark et al. 2020 were due to definitional differences among datasets, whereas the remainder (14%) were from a variety of causes. Cropland expansion in the LCMAP likely reflects conversions of more natural areas, whereas cropland expansion in other data sources also captures conversion of managed pasture to cropland. The particular research question considered (e.g., habitat versus soil carbon) should influence which data source is more appropriate.
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spelling doaj-art-9e45f73a60fc404ea99fa60af67a85d02025-08-20T01:55:27ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01203e031388010.1371/journal.pone.0313880Tracking cropland transitions: A comparative analysis of U.S. land cover change data.Gray MartinKemen AustinTyler LarkStanley LeeChristopher M ClarkThere are a growing number of land cover data available for the conterminous United States, supporting various applications ranging from biofuel regulatory decisions to habitat conservation assessments. These datasets vary in their source information, frequency of data collection and reporting, land class definitions, categorical detail, and spatial scale and time intervals of representation. These differences limit direct comparison, contribute to disagreements among studies, confuse stakeholders, and hamper our ability to confidently report key land cover trends in the U.S. Here we assess changes in cropland derived from the Land Change Monitoring, Assessment, and Projection (LCMAP) dataset from the U.S. Geological Survey and compare them with analyses of three established land cover datasets across the coterminous U.S. from 2008-2017: (1) the National Resources Inventory (NRI), (2) a dataset Lark et al. 2020 derived from the Cropland Data Layer (CDL), and (3) a dataset from Potapov et al. 2022. LCMAP reports more stable cropland and less stable noncropland in all comparisons, likely due to its more expansive definition of cropland which includes managed grasslands (pasture and hay). Despite these differences, net cropland expansion from all four datasets was comparable (5.18-6.33 million acres), although the geographic extent and type of conversion differed. LCMAP projected the largest cropland expansion in the southern Great Plains, whereas other datasets projected the largest expansion in the northwestern and central Midwest. Most of the pixel-level disagreements (86%) between LCMAP and Lark et al. 2020 were due to definitional differences among datasets, whereas the remainder (14%) were from a variety of causes. Cropland expansion in the LCMAP likely reflects conversions of more natural areas, whereas cropland expansion in other data sources also captures conversion of managed pasture to cropland. The particular research question considered (e.g., habitat versus soil carbon) should influence which data source is more appropriate.https://doi.org/10.1371/journal.pone.0313880
spellingShingle Gray Martin
Kemen Austin
Tyler Lark
Stanley Lee
Christopher M Clark
Tracking cropland transitions: A comparative analysis of U.S. land cover change data.
PLoS ONE
title Tracking cropland transitions: A comparative analysis of U.S. land cover change data.
title_full Tracking cropland transitions: A comparative analysis of U.S. land cover change data.
title_fullStr Tracking cropland transitions: A comparative analysis of U.S. land cover change data.
title_full_unstemmed Tracking cropland transitions: A comparative analysis of U.S. land cover change data.
title_short Tracking cropland transitions: A comparative analysis of U.S. land cover change data.
title_sort tracking cropland transitions a comparative analysis of u s land cover change data
url https://doi.org/10.1371/journal.pone.0313880
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