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

Land

Qiquan Li¹  Hao Li¹  Ouping Deng¹  Ting Lan¹  Xuesong Gao¹  Min Xiong¹  Min Zeng¹  Qi Wang¹  Rong Huang¹ 

[1] College of Resources, Sichuan Agricultural University, Chengdu 611130, China;

关键词: historical land use;    random forest algorithm;    reconstruction;    spatial distribution;   

DOI  :  10.3390/land10121338

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown