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10.3390/land10121338
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Article

Spatially Explicit Reconstruction of Cropland Using the Random Forest: A Case Study of the Tuojiang River Basin, China from 1911 to 2010

by
Qi Wang
,
Min Xiong
,
Qiquan Li
,
Hao Li
,
Ting Lan
,
Ouping Deng
,
Rong Huang
,
Min Zeng
and
Xuesong Gao
*
College of Resources, Sichuan Agricultural University, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
Land 2021, 10(12), 1338; https://doi.org/10.3390/land10121338
Submission received: 16 November 2021 / Revised: 30 November 2021 / Accepted: 2 December 2021 / Published: 4 December 2021
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Abstract

A long-term, high-resolution cropland dataset plays an essential part in accurately and systematically understanding the mechanisms that drive cropland change and its effect on biogeochemical processes. However, current widely used spatially explicit cropland databases are developed according to a simple downscaling model and are associated with low resolution. By combining historical county-level cropland archive data with natural and anthropogenic variables, we developed a random forest model to spatialize the cropland distribution in the Tuojiang River Basin (TRB) during 1911–2010, using a resolution of 30 m. The reconstruction results showed that the cropland in the TRB increased from 1.13 × 104 km2 in 1911 to 1.81 × 104 km2. In comparison with satellite-based data for 1980, the reconstructed dataset approximated the remotely sensed cropland distribution. Our cropland map could capture cropland distribution details better than three widely used public cropland datasets, due to its high spatial heterogeneity and improved spatial resolution. The most critical factors driving the distribution of TRB cropland include nearby-cropland, elevation, and climatic conditions. This newly reconstructed cropland dataset can be used for long-term, accurate regional ecological simulation, and future policymaking. This novel reconstruction approach has the potential to be applied to other land use and cover types via its flexible framework and modifiable parameters.
Keywords: historical land use; random forest algorithm; reconstruction; spatial distribution historical land use; random forest algorithm; reconstruction; spatial distribution

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MDPI and ACS Style

Wang, Q.; Xiong, M.; Li, Q.; Li, H.; Lan, T.; Deng, O.; Huang, R.; Zeng, M.; Gao, X. Spatially Explicit Reconstruction of Cropland Using the Random Forest: A Case Study of the Tuojiang River Basin, China from 1911 to 2010. Land 2021, 10, 1338. https://doi.org/10.3390/land10121338

AMA Style

Wang Q, Xiong M, Li Q, Li H, Lan T, Deng O, Huang R, Zeng M, Gao X. Spatially Explicit Reconstruction of Cropland Using the Random Forest: A Case Study of the Tuojiang River Basin, China from 1911 to 2010. Land. 2021; 10(12):1338. https://doi.org/10.3390/land10121338

Chicago/Turabian Style

Wang, Qi, Min Xiong, Qiquan Li, Hao Li, Ting Lan, Ouping Deng, Rong Huang, Min Zeng, and Xuesong Gao. 2021. "Spatially Explicit Reconstruction of Cropland Using the Random Forest: A Case Study of the Tuojiang River Basin, China from 1911 to 2010" Land 10, no. 12: 1338. https://doi.org/10.3390/land10121338

APA Style

Wang, Q., Xiong, M., Li, Q., Li, H., Lan, T., Deng, O., Huang, R., Zeng, M., & Gao, X. (2021). Spatially Explicit Reconstruction of Cropland Using the Random Forest: A Case Study of the Tuojiang River Basin, China from 1911 to 2010. Land, 10(12), 1338. https://doi.org/10.3390/land10121338

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