Integration of remote sensing, county-level census, and machine learning for century-long regional cropland distribution data reconstruction

  • Authors: Yang, Jia; Tao, Bo; Shi, Hao; Ouyang, Ying; Pan, Shufen; Ren, Wei; Lu, Chaoqun
  • Publication Year: 2020
  • Publication Series: Scientific Journal (JRNL)
  • Source: International Journal of Applied Earth Observation and Geoinformation
  • DOI: 10.1016/j.jag.2020.102151

Abstract

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellitebased cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78×104 km2 in 1850 to 6.64×104 km2 in 1980 and then decreased to 6.16×104 km2 in 2018. Cropland expansion rate was the largest in the 1960s (749 km2 yr−1) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km2 yr−1 in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machinelearning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem

  • Citation: Yang, Jia; Tao, Bo; Shi, Hao; Ouyang, Ying; Pan, Shufen; Ren, Wei; Lu, Chaoqun. 2020. Integration of remote sensing, county-level census, and machine learning for century-long regional cropland distribution data reconstruction. International Journal of Applied Earth Observation and Geoinformation. 91: 102151-. https://doi.org/10.1016/j.jag.2020.102151.
  • Keywords: Land use change, Cropland expansion, Afforestation, Ecosystem services, Logistic regression, Marginal croplands
  • Posted Date: July 23, 2020
  • Modified Date: August 4, 2020
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