Spatial Heterogeneity of Driving Factors in Multi-Vegetation Indices RSEI Based on the XGBoost-SHAP Model: A Case Study of the Jinsha River Basin, Yunnan
Jisheng Xia, Guoyou Zhang, Shiping Ma, Yingying Pan
Abstract
The Jinsha River Basin in Yunnan serves as a crucial ecological barrier in southwestern China. Objective ecological assessment and identification of key driving factors are essential for the region’s sustainable development. The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological assessments. In recent years, interpretable machine learning (IML) has introduced novel approaches for understanding complex ecological driving mechanisms. This study employed Google Earth Engine (GEE) to calculate three vegetation indices—NDVI, SAVI, and kNDVI—for the study area from 2000 to 2022, along with their corresponding RSEI models (NDVI-RSEI, SAVI-RSEI, and kNDVI-RSEI). Additionally, it analyzed the spatiotemporal variations of these RSEI models and their relationship with vegetation indices. Furthermore, an IML model (XGBoost-SHAP) was employed to interpret the driving factors of RSEI. The results indicate that (1) the RSEI levels in the study area from 2000 to 2022 were primarily moderate; (2) compared to NDVI-RSEI, SAVI-RSEI is more susceptible to soil factors, while kNDVI-RSEI exhibits a lower saturation tendency; and (3) potential evapotranspiration, land cover, and elevation are key drivers of RSEI variations, primarily affecting the ecological environment in the western, southeastern, and northeastern parts of the study area. The XGBoost-SHAP approach provides valuable insights for promoting regional sustainable development.