Spatiotemporal dynamics and spatially explicit drivers of eco-geological coupling coordination in a mountainous region
Abstract
This study developed an integrated framework combining remote sensing assessment, coupling coordination analysis, and interpretable machine learning to investigate the spatiotemporal dynamics of eco-geological coupling coordination and the spatially explicit drivers of its 2020 pattern in a mountainous region. Landsat imagery from 2000 to 2020 was used to derive the Remote Sensing Ecological Index (RSEI), while geological hazard susceptibility was inversely transformed into a Geological Environmental Stability Index (GESI). The coupling coordination degree (CCD) was evaluated under a consistent geological background, and XGBoost–GeoShapley was applied to identify the nonlinear drivers and spatial interaction effects of the 2020 CCD pattern. Mean RSEI increased from 0.54 in 2000 to 0.61 in 2020, indicating an improvement in ecological environmental quality. The CCD exhibited an overall improving trend, with higher values in peripheral mountainous areas and lower values in river valleys and areas with intensive human activity. The proportion of well- and highly coordinated areas increased from 55.6% in 2000 to a peak of 66.7% in 2015 before declining slightly to 64.3% in 2020. Mean annual temperature, population density, and actual evapotranspiration were the three most influential drivers of the 2020 CCD spatial variation. Moreover, GeoShapley revealed pronounced spatial effects and GEO–feature interactions, indicating that the magnitude and direction of driver contributions varied across spatial locations. Strong GEO–feature interaction effects were mainly concentrated in the western–southwestern region, central transition zone, and southern margin. These findings highlight the importance of spatially differentiated ecological restoration and geological hazard mitigation in mountainous eco-geological systems.
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BibTeX
@article{li2026spatiotemporal,
title = {{Spatiotemporal dynamics and spatially explicit drivers of eco-geological coupling coordination in a mountainous region}},
author = {Zhiguang Li and Hongwei Li and Yanwei Chen and Ying Yuan and Aihong Zhou and Shiwen Wang},
journal = {Scientific Reports},
year = {2026},
month = nov,
doi = {10.1038/s41598-026-79113-w},
url = {https://www.nature.com/articles/s41598-026-79113-w},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spatiotemporal dynamics and spatially explicit drivers of eco-geological coupling coordination in a mountainous region
AU - Zhiguang Li
AU - Hongwei Li
AU - Yanwei Chen
AU - Ying Yuan
AU - Aihong Zhou
AU - Shiwen Wang
PY - 2026
DA - 2026/11/03
JO - Scientific Reports
DO - 10.1038/s41598-026-79113-w
UR - https://www.nature.com/articles/s41598-026-79113-w
AB - This study developed an integrated framework combining remote sensing assessment, coupling coordination analysis, and interpretable machine learning to investigate the spatiotemporal dynamics of eco-geological coupling coordination and the spatially explicit drivers of its 2020 pattern in a mountainous region. Landsat imagery from 2000 to 2020 was used to derive the Remote Sensing Ecological Index (RSEI), while geological hazard susceptibility was inversely transformed into a Geological Environmental Stability Index (GESI). The coupling coordination degree (CCD) was evaluated under a consistent geological background, and XGBoost–GeoShapley was applied to identify the nonlinear drivers and spatial interaction effects of the 2020 CCD pattern. Mean RSEI increased from 0.54 in 2000 to 0.61 in 2020, indicating an improvement in ecological environmental quality. The CCD exhibited an overall improving trend, with higher values in peripheral mountainous areas and lower values in river valleys and areas with intensive human activity. The proportion of well- and highly coordinated areas increased from 55.6% in 2000 to a peak of 66.7% in 2015 before declining slightly to 64.3% in 2020. Mean annual temperature, population density, and actual evapotranspiration were the three most influential drivers of the 2020 CCD spatial variation. Moreover, GeoShapley revealed pronounced spatial effects and GEO–feature interactions, indicating that the magnitude and direction of driver contributions varied across spatial locations. Strong GEO–feature interaction effects were mainly concentrated in the western–southwestern region, central transition zone, and southern margin. These findings highlight the importance of spatially differentiated ecological restoration and geological hazard mitigation in mountainous eco-geological systems.
SN - 2045-2322
ER -