Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China

The Three Gorges Reservoir Area in China presents a critical conflict between industrial development and ecological conservation. It functions as a key hub for water management, energy production, and shipping, while also serving as a vital zone for ecological and environmental protection. Focusing on Zigui County, this study developed a 16-indicator evaluation system integrating geological, ecological, and socioeconomic factors. It utilized the Analytic Hierarchy Process (AHP), coefficient of variation (CV), and the Real-Coded Accelerating Genetic Algorithm-Projection Pursuit (RAGA-PP) model for evaluation, the latter of which optimizes the projection direction and utilizes PP to transform high-dimensional data into a low-dimensional space, thereby obtaining the values of the projection indices. The findings indicate the following: (1) The RAGA-PP model outperforms conventional AHP-CV methods in assessing Zigui County’s eco-geological environment, showing superior accuracy (higher Moran’s I) and spatial consistency. (2) Hotspot analysis confirms these results, revealing distinct spatial patterns. (3) From 2000 to 2020, “bad” quality areas decreased from 17.31% to 12.33%, while “moderate” or “better” zones expanded. (4) This improvement reflects favorable natural conditions and reduced human impacts. These trends underscore the effectiveness of China’s ecological civilization policies, which have prioritized sustainable development through targeted environmental governance, afforestation initiatives, and stringent regulations on industrial activities.

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Publication Details

Journal
Remote Sensing
Published
2025-07-12
DOI
https://doi.org/10.3390/rs17142414
Citations
1
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
2.48

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article

Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China

Rongrui Liu, Liuting Qin, Jiaxin Lu, Yuejie Zheng et al.
1 citations
Remote Sensing
Geochemistry and Geologic Mapping
2.48
article

Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China

Rongrui Liu, Liuting Qin, Jiaxin Lu, Yuejie Zheng, Xueling Wu, Chaoyan Lv
article en
1 citations

Abstract

The Three Gorges Reservoir Area in China presents a critical conflict between industrial development and ecological conservation. It functions as a key hub for water management, energy production, and shipping, while also serving as a vital zone for ecological and environmental protection. Focusing on Zigui County, this study developed a 16-indicator evaluation system integrating geological, ecological, and socioeconomic factors. It utilized the Analytic Hierarchy Process (AHP), coefficient of variation (CV), and the Real-Coded Accelerating Genetic Algorithm-Projection Pursuit (RAGA-PP) model for evaluation, the latter of which optimizes the projection direction and utilizes PP to transform high-dimensional data into a low-dimensional space, thereby obtaining the values of the projection indices. The findings indicate the following: (1) The RAGA-PP model outperforms conventional AHP-CV methods in assessing Zigui County’s eco-geological environment, showing superior accuracy (higher Moran’s I) and spatial consistency. (2) Hotspot analysis confirms these results, revealing distinct spatial patterns. (3) From 2000 to 2020, “bad” quality areas decreased from 17.31% to 12.33%, while “moderate” or “better” zones expanded. (4) This improvement reflects favorable natural conditions and reduced human impacts. These trends underscore the effectiveness of China’s ecological civilization policies, which have prioritized sustainable development through targeted environmental governance, afforestation initiatives, and stringent regulations on industrial activities.

Remote SensingVol. 17(14)
Huazhong Agricultural University (CN), Ministry of Natural Resources (CN), China University of Geosciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 10%
Geochemistry and Geologic Mapping
2.48
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