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
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 ecolo...
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MDPI AG
2025-04-01
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| author | Jisheng Xia Guoyou Zhang Sunjie Ma Yingying Pan |
| author_facet | Jisheng Xia Guoyou Zhang Sunjie Ma Yingying Pan |
| author_sort | Jisheng Xia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-74967fdc0090415ea54a974b18f91fa8 |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-74967fdc0090415ea54a974b18f91fa82025-08-20T03:14:43ZengMDPI AGLand2073-445X2025-04-0114592510.3390/land14050925Spatial Heterogeneity of Driving Factors in Multi-Vegetation Indices RSEI Based on the XGBoost-SHAP Model: A Case Study of the Jinsha River Basin, YunnanJisheng Xia0Guoyou Zhang1Sunjie Ma2Yingying Pan3School of Earth Sciences, Yunnan University, Kunming 650091, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650091, ChinaSchool of Earth Sciences, Yunnan University, Kunming 650091, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, ChinaThe 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.https://www.mdpi.com/2073-445X/14/5/925GEEremote sensing ecologic indexinfluencing factorsexplainable machine learning |
| spellingShingle | Jisheng Xia Guoyou Zhang Sunjie Ma Yingying Pan 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 GEE remote sensing ecologic index influencing factors explainable machine learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | GEE remote sensing ecologic index influencing factors explainable machine learning |
| url | https://www.mdpi.com/2073-445X/14/5/925 |
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