Quantitative assessment of the importance of bio-physical drivers of land cover change based on a random forest method

The spatial distribution patterns of land cover greatly influence the ecological balance of the Loess Plateau. Understanding the bio-physical drivers of land cover change is important for ecological restoration in the context of climate change. However, in the analysis of the drivers of land cover change in the Loess Plateau, the role of bio-physical drivers has not been quantitatively evaluated. Using remote sensing data, machine learning, and statistical methods, this study analyzed the spatial and temporal patterns of land cover from 2001 to 2018 in the Loess Plateau of China. We used a random forest (RF) model to quantify the importance of bio-physical drivers of land cover. Our results demonstrated that the RF model has good performance and high reliability (model ac-curacy score 0.8). Our simulation experiment revealed that evapotranspiration was the most important driver (importance score, IS >0.2), temperature and precipitation had regional heterogeneity, and slope was the least important (IS <0.05). We suggest that evapotranspiration can be regulated by properly allocating the type of land cover, so as to rationally allocate water resources on the Loess Plateau. This study provides a new foun-dation for quantitatively evaluating the drivers of land cover change and regulating the distribution of water resources on the Loess Plateau, China.

Automated summary

The study analyzed the spatial and temporal patterns of land cover in the Loess Plateau of China from 2001 to 2018 using remote sensing data and machine learning, highlighting the importance of evapotranspiration as a key driver of land cover change. Suggestions were made for regulating water resources allocation by properly allocating land cover types.