Spatiotemporal Detection of Ecological Environment Quality Changes in the Lijiang River Basin Using a New Dual Model

Ning Li, Haoyu Wang (), Wen He (), Bin Jia, Bolin Fu, Jianjun Chen, Xinyuan Meng, Ling Yu and Jinye Wang
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Ning Li: School of Airline Services and Tourism Management, Guilin University of Aerospace Technology, Guilin 541004, China
Haoyu Wang: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
Wen He: Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin 541006, China
Bin Jia: School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China
Bolin Fu: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
Jianjun Chen: College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
Xinyuan Meng: School of Airline Services and Tourism Management, Guilin University of Aerospace Technology, Guilin 541004, China
Ling Yu: College of Computer Science and Engineering, Guilin University of Aerospace Technology, Guilin 541004, China
Jinye Wang: College of Tourism and Landscape Architecture, Guilin University of Technology, Guilin 541004, China

Sustainability, 2025, vol. 17, issue 2, 1-18

Abstract: Detecting spatiotemporal changes in ecological environment quality (EEQ) is of great importance for maintaining regional ecological security and supporting sustainable economic and social development. However, research on EEQ detection from a remote sensing perspective is insufficient, especially at the basin scale. Based on two indices, namely, the Ecological Index (EI) and the Remote Sensing Ecological Index (RSEI), we established a dual model, combining the remote sensing ecological comprehensive index (RSECI) and its differential change model, to study the spatiotemporal evolutionary characteristics of EEQ in the Lijiang River Basin (LRB) from 2000 to 2020. The RSECI combines the following five indicators: greenness, wetness, heat, dryness, and aerosol optical depth. The results of this study show that the area of good and excellent EEQ in the LRB decreased from 3676.22 km 2 in 2000 to 2083.89 km 2 in 2020, while the area of poor and fair EEQ increased from 80.81 km 2 in 2000 to 1375.91 km 2 in 2020. From 2000 to 2020, the change curve of the EEQ difference in the LRB first rose, fell, and then rose again. The wetness and greenness indicators had positive effects on promoting EEQ, while the heat, aerosol optical depth, and dryness indicators had restraining effects. The results of stepwise regression analysis showed that, among the selected indicators, wetness and greenness were the key factors for improving the EEQ in the LRB during the study period. The RSECI approach and the difference change model proposed in this study can be used to quantitatively evaluate the EEQ and facilitate the analysis of the spatial and temporal dynamic changes and difference changes in EEQ.

Keywords: detection; ecological environment quality; Lijiang River Basin; remote sensing ecological comprehensive index (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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