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...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/14/2414 |
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| author | Xueling Wu Jiaxin Lu Chaojie Lv Liuting Qin Rongrui Liu Yanjuan Zheng |
| author_facet | Xueling Wu Jiaxin Lu Chaojie Lv Liuting Qin Rongrui Liu Yanjuan Zheng |
| author_sort | Xueling Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-98ffbeb1fea24d39a370b635dbc01bd3 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-98ffbeb1fea24d39a370b635dbc01bd32025-08-20T03:32:15ZengMDPI AGRemote Sensing2072-42922025-07-011714241410.3390/rs17142414Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, ChinaXueling Wu0Jiaxin Lu1Chaojie Lv2Liuting Qin3Rongrui Liu4Yanjuan Zheng5School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaSchool of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaSchool of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaSchool of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaSchool of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaCollege of Economics and Management, Huazhong Agricultural University, Wuhan 430070, ChinaThe 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.https://www.mdpi.com/2072-4292/17/14/2414eco-geological environmentAHP-CVRAGA-PPspatiotemporal evolution |
| spellingShingle | Xueling Wu Jiaxin Lu Chaojie Lv Liuting Qin Rongrui Liu Yanjuan Zheng Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China Remote Sensing eco-geological environment AHP-CV RAGA-PP spatiotemporal evolution |
| title | Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China |
| title_full | Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China |
| title_fullStr | Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China |
| title_full_unstemmed | Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China |
| title_short | Spatiotemporal Analysis of Eco-Geological Environment Using the RAGA-PP Model in Zigui County, China |
| title_sort | spatiotemporal analysis of eco geological environment using the raga pp model in zigui county china |
| topic | eco-geological environment AHP-CV RAGA-PP spatiotemporal evolution |
| url | https://www.mdpi.com/2072-4292/17/14/2414 |
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