Coupled Assessment of Land Use Changes and Ecological Benefits Using Multi-Source Remote Sensing Data

Jin Guo, Xiaojian Wei (), Fuqing Zhang and Yubo Ding
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Jin Guo: Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China
Xiaojian Wei: Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China
Fuqing Zhang: Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China
Yubo Ding: Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China

Agriculture, 2025, vol. 15, issue 13, 1-27

Abstract: The Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR), serving as a pivotal hub for coordinated economic and ecological development in central China, is characterized by marked ecological fragility and climate sensitivity. Investigating the land use dynamics and ecological benefit changes within this region holds critical strategic significance for balancing regional development with the construction of ecological security barriers. This study systematically analyzed the spatiotemporal variations in land use/land cover (LULC) across the UAMRYR, using multi-source remote sensing data, climatic factors, land conditions, and anthropogenic influences. By integrating the four-quadrant model and the coupling degree model, we developed a remote sensing ecological index (RSEI)–ecological service index (ESI) coupling evaluation framework to assess the spatiotemporal evolution patterns of changes in ecological benefits in the region. Furthermore, we employed Geodetector analysis to identify the key influencing factors driving the RSEI–ESI coupling relationship and their interactive mechanisms. The research findings are as follows: (1) The ecological regional pattern has changed. The area of Quadrant I (RSEI > 0.5 and ESI > 0.5) decreased by 13,800 km 2 , whereas Quadrants II (RSEI < 0.5 and ESI > 0.5) and IV (RSEI > 0.5 and ESI < 0.5) increased by 14,900 km 2 and 3500 km 2 , respectively. Quadrant III (RSEI < 0.5 and ESI < 0.5) remained relatively stable. This indicates that the imbalance in ecological functional spaces has intensified, affecting key ecological processes. (2) The quantitative analysis of the spatiotemporal evolution characteristics of the RSEI and ESI revealed contrasting trends: the RSEI decreased by 0.006, whereas the ESI showed a slight increase of 0.001. (3) The ranking of the driving factors indicated that the Normalized Difference Vegetation Index (NDVI) and the mean annual rainfall (MAP) were the primary factors driving ecological evolution, while the influence of economic driving factors was relatively weak. This study establishes a three-pillar framework (quadrant-based diagnosis, Geodetector-driven analysis, and RSEI–ESI coupled interventions) to guide precision-based ecological restoration and spatial governance.

Keywords: remote sensing ecological index; ecosystem services index; four-quadrant model; coupling index; driving factors (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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