Spatiotemporal Changes and the Drivers of Ecological Environmental Quality Based on the Remote Sensing Ecological Index: A Case Study of Shanxi Province, China

Chi Cheng and Yanqiang Wang ()
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Chi Cheng: College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China
Yanqiang Wang: College of Resources and Environment, Shanxi Agricultural University, Jinzhong 030801, China

Land, 2025, vol. 14, issue 5, 1-26

Abstract: Ecological transition zones spanning semi-humid to semi-arid regions pose distinctive monitoring challenges owing to their climatic vulnerability and geomorphic diversity. This study focuses on Shanxi Province, a typical ecologically fragile area in the Loess Plateau of China. Based on the Google Earth Engine (GEE) platform and Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, we established the Remote Sensing Ecological Index (RSEI) series from 2000 to 2024 for Shanxi Province. The Theil–Sen Median, Mann–Kendall, and Hurst indices were comprehensively applied to systematically analyze the spatiotemporal differentiation patterns of ecological environmental quality. Furthermore, geodetector-based quantification elucidated the synergistic interactions among topographic, climatic, and anthropogenic drivers. The results indicate the following: (1) From 2000 to 2024, ecological restoration initiatives have shaped an “aggregate improvement-localized degradation” paradigm, with medium-quality territories persistently accounting for 30–40% of the total land area. (2) Significant spatial heterogeneity exists, with the Lüliang Mountain area in the west and the Datong Basin in the north being core degradation zones, while the Taihang Mountain area in the east shows remarkable improvement. However, Theil–Sen Median–Hurst index predictions reveal that 60.07% of the improved areas face potential trend reversal risks. (3) The driving mechanisms exhibit spatial heterogeneity, where land use type, temperature, precipitation, elevation, and slope serve as global dominant factors. This research provides scientific support for formulating differentiated ecological restoration strategies, establishing ecological compensation mechanisms, and optimizing territorial spatial planning in Shanxi Province, contributing to the achievement of sustainable development goals.

Keywords: Remote Sensing Ecological Index (RSEI); ecological environmental quality; spatiotemporal changes; geodetector; Google Earth Engine (GEE) (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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