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Combining Tabular and Satellite-Based Datasets to Better Understand Cropland Change

Author

Listed:
  • Kenneth Lee Copenhaver

    (CropGrower LLC, Tampa, FL 33606, USA)

Abstract

In recent years, regulatory agencies in the USA and Europe have begun to require documentation that land used to produce crops and biofuels has not been converted from carbon-capturing grasslands or forests. Precise measurement of these land cover changes, however, has proven difficult. Analysis to date has focused primarily on moderate resolution (30 m) satellite imagery, which has not provided the land cover granularity or accuracy needed. These studies have estimated large-scale land conversion to crops in the USA. This study analyzed the satellite datasets but included tabular datasets and aerial imagery of the USA to determine whether the combination of datasets, focusing on more detailed analysis in these locations, could more accurately identify potential locations of land use change. Analyses of satellite imagery data from 1985 to 2020 found that much of the land that 2008 to 2020 satellite datasets classified as natural-to-crop land change was idle cropland. The results indicate a dynamic landscape of marginal land moving in and out of cropland. Approximately as much land was allowed to go fallow (6145 hectares) as land going into crop (7901 hectares) from 1985 to 2020. The results from this study indicate regulatory agencies could more accurately measure the impacts of conversion of natural lands to crop if long-term historical land cover/land use was also analyzed.

Suggested Citation

  • Kenneth Lee Copenhaver, 2022. "Combining Tabular and Satellite-Based Datasets to Better Understand Cropland Change," Land, MDPI, vol. 11(5), pages 1-20, May.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:714-:d:812028
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    References listed on IDEAS

    as
    1. Woubet G. Alemu & Geoffrey M. Henebry & Assefa M. Melesse, 2020. "Land Cover and Land Use Change in the US Prairie Pothole Region Using the USDA Cropland Data Layer," Land, MDPI, vol. 9(5), pages 1-20, May.
    2. Larsen, Ashley E. & Hendrickson, Brandon T. & Dedeic, Nicholas & MacDonald, Andrew J., 2015. "Taken as a given: Evaluating the accuracy of remotely sensed crop data in the USA," Agricultural Systems, Elsevier, vol. 141(C), pages 121-125.
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    Citations

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    Cited by:

    1. Chuanrong Zhang & Xinba Li, 2022. "Land Use and Land Cover Mapping in the Era of Big Data," Land, MDPI, vol. 11(10), pages 1-22, September.

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