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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

Author

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  • Jisheng Xia

    (School of Earth Sciences, Yunnan University, Kunming 650091, China)

  • Guoyou Zhang

    (School of Earth Sciences, Yunnan University, Kunming 650091, China)

  • Sunjie Ma

    (School of Earth Sciences, Yunnan University, Kunming 650091, China)

  • Yingying Pan

    (Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
    Yunnan Provincial Archives of Surveying and Mapping (Yunnan Provincial Geomatics Centre), Kunming 650034, China)

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.

Suggested Citation

  • Jisheng Xia & Guoyou Zhang & Sunjie Ma & Yingying Pan, 2025. "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," Land, MDPI, vol. 14(5), pages 1-23, April.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:925-:d:1641427
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    References listed on IDEAS

    as
    1. Jianbo Zhou & Wanqing Liu, 2022. "Monitoring and Evaluation of Eco-Environment Quality Based on Remote Sensing-Based Ecological Index (RSEI) in Taihu Lake Basin, China," Sustainability, MDPI, vol. 14(9), pages 1-21, May.
    2. Qiongrui Zhang & Tao Huang & Songjun Xu, 2023. "Assessment of Urban Ecological Resilience Based on PSR Framework in the Pearl River Delta Urban Agglomeration, China," Land, MDPI, vol. 12(5), pages 1-13, May.
    3. Jian Xu & Liangjun Zheng & Ruiqu Ma & Han Tian, 2023. "Correlation between Distribution of Rural Settlements and Topography in Plateau-Mountain Area: A Study of Yunnan Province, China," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    4. Cheng Cai & Jingye Li & Zhanqi Wang, 2024. "Long-Term Ecological and Environmental Quality Assessment Using an Improved Remote-Sensing Ecological Index (IRSEI): A Case Study of Hangzhou City, China," Land, MDPI, vol. 13(8), pages 1-15, July.
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