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, Journal Year: 2025, Volume and Issue: 14(5), P. 925 - 925

Published: April 24, 2025

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

Language: Английский

Study on the coupling coordination characteristics and influencing factors of ecological environmental civilization and resident public health in China—based on a modified coupling coordination model

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(12), P. e0315373 - e0315373

Published: Dec. 6, 2024

As industrial technologies advance, climate change and environmental pollution increasingly pose threats to human health. This study examines the coupling coordination characteristics between ecological civilization (EEC) resident public health (RPH) promote both higher standards enhanced societal sustainability. Utilizing panel data from 31 provinces in China spanning 2010 2022, this paper constructs evaluation indices for EEC RPH. Initially, entropy method is employed determine development levels of each domain. Subsequently, a modified degree (CCD) model applied assess CCD research further investigates spatiotemporal evolution trends using methods such as Dagum Gini coefficient, kernel density estimation (KDE), Markov chains. Finally, Tobit utilized analyze factors influencing CCD. Findings reveal that during period, RPH exhibited stable upward trend, although overall level remained relatively low. The showed consistent growth nationally across three major regions. Overall inequality coordination, measured by has decreased, with coefficient reducing 0.0316 0.0199 2022. KDE results indicate rightward shift curve CCD, suggesting significant reduction absolute disparities. Panel regression analysis shows economic development, urbanization, education significantly positively influence on national scale, urbanization having most substantial impact, followed levels.

Language: Английский