Spatio-Temporal Changes and Driving Mechanisms of the Ecological Quality in the Mountain–River–Sea Regional System: A Case Study of the Southwest Guangxi Karst–Beibu Gulf

Jinrui Ren, Baoqing Hu (), Jinsong Gao, Chunlian Gao, Zhanhao Dang and Shaoqiang Wen
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Jinrui Ren: Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education/Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China
Baoqing Hu: Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education/Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China
Jinsong Gao: Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education/Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China
Chunlian Gao: Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education/Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China
Zhanhao Dang: Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education/Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China
Shaoqiang Wen: Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education/Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Nanning Normal University, Nanning 530001, China

Sustainability, 2025, vol. 17, issue 16, 1-21

Abstract: This study investigates the spatio-temporal characteristics and driving mechanisms of ecological quality in the mountain–river–sea regional system using the Remote Sensing Ecological Index (RSEI) model, moderate-resolution imaging spectroradiometer (MODIS) data, and the Google Earth Engine (GEE) platform. The analysis, conducted at both the grid and county scales using spatial autocorrelation and geodetector, showed a notable improvement in ecological quality, with the average RSEI value rising from 0.549 in 2000 to 0.627 in 2022. The distribution pattern reveals superior quality in the northwest and inferior quality in central urban cores and coastal zones. Ecological quality exhibited significant spatial clustering, with high–high clusters in karst mountains and low–low clusters in urban and industrial zones. Geodetector analysis identified GDP and population density as dominant factors at the grid scale, and GDP and elevation at the county scale. By quantifying spatio-temporal variations and driving mechanisms of ecological quality across scales, this study provides a solid scientific foundation for regional ecological conservation and sustainable development.

Keywords: ecological quality; remote sensing ecological index (RSEI); Google Earth Engine (GEE); scale effects; spatio-temporal change; driving factors; spatial autocorrelation; geodetector; Mountain–River–Sea (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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