Integrating mining district data into ecological security pattern identification: a case study of Chenzhou
Abstract Resource-intensive cities face significant ecological challenges due to mining activities, which degrade landscapes, pollute ecosystems, and disrupt ecological security patterns. This study proposes a process for identifying ecological security patterns (ESP) in mining cities, integrating l...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-00883-w |
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| author | Jiawei Hui Yongsheng Cheng |
| author_facet | Jiawei Hui Yongsheng Cheng |
| author_sort | Jiawei Hui |
| collection | DOAJ |
| description | Abstract Resource-intensive cities face significant ecological challenges due to mining activities, which degrade landscapes, pollute ecosystems, and disrupt ecological security patterns. This study proposes a process for identifying ecological security patterns (ESP) in mining cities, integrating landscape risk assessment, remote sensing ecological quality evaluation, and mining district spatial data. We introduce the ecological source index (ECSI) to identify ecological sources in Chenzhou and construct an ecological resistance surface (ERS) by incorporating mining district locations. Using circuit theory, we map key ecological corridors and nodes, establishing the ecological security framework for Chenzhou. Our findings show 2,903 km² of primary ecological sources, 1,735 km² of secondary ES, and 2,124 km² of tertiary ES, along with 90 ecological corridors (1,183.66 km), 22 inactive corridors (983.37 km), 3 major river corridors, 68 pinch points, and 80 barriers. The ecological sources are organized in a “dominant source with multiple subsidiary cores” structure, connected by a “three horizontal and four vertical” corridor network. Ecological sources are primarily located in the east, while corridors, pinch points, and barriers are concentrated in the west. Barriers are mainly urban areas, mining zones, and farmland, while pinch points occur in narrow corridor sections, especially near towns and mining areas. Mining activities cause localized shifts and fragmentation of ecological corridors. We propose recommendations for mining management, such as implementing strict mining approval processes, constructing artificial ecological corridors, and expanding ecological channel boundaries in pinch point clusters. These findings provide essential guidance for ecological restoration and sustainable development in resource-dependent cities. |
| format | Article |
| id | doaj-art-64219b3fc8614a39b1e154dabc2bff9f |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-64219b3fc8614a39b1e154dabc2bff9f2025-08-20T02:15:02ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-00883-wIntegrating mining district data into ecological security pattern identification: a case study of ChenzhouJiawei Hui0Yongsheng Cheng1School of Geosciences and Info-Physics, Central South UniversitySchool of Geosciences and Info-Physics, Central South UniversityAbstract Resource-intensive cities face significant ecological challenges due to mining activities, which degrade landscapes, pollute ecosystems, and disrupt ecological security patterns. This study proposes a process for identifying ecological security patterns (ESP) in mining cities, integrating landscape risk assessment, remote sensing ecological quality evaluation, and mining district spatial data. We introduce the ecological source index (ECSI) to identify ecological sources in Chenzhou and construct an ecological resistance surface (ERS) by incorporating mining district locations. Using circuit theory, we map key ecological corridors and nodes, establishing the ecological security framework for Chenzhou. Our findings show 2,903 km² of primary ecological sources, 1,735 km² of secondary ES, and 2,124 km² of tertiary ES, along with 90 ecological corridors (1,183.66 km), 22 inactive corridors (983.37 km), 3 major river corridors, 68 pinch points, and 80 barriers. The ecological sources are organized in a “dominant source with multiple subsidiary cores” structure, connected by a “three horizontal and four vertical” corridor network. Ecological sources are primarily located in the east, while corridors, pinch points, and barriers are concentrated in the west. Barriers are mainly urban areas, mining zones, and farmland, while pinch points occur in narrow corridor sections, especially near towns and mining areas. Mining activities cause localized shifts and fragmentation of ecological corridors. We propose recommendations for mining management, such as implementing strict mining approval processes, constructing artificial ecological corridors, and expanding ecological channel boundaries in pinch point clusters. These findings provide essential guidance for ecological restoration and sustainable development in resource-dependent cities.https://doi.org/10.1038/s41598-025-00883-w |
| spellingShingle | Jiawei Hui Yongsheng Cheng Integrating mining district data into ecological security pattern identification: a case study of Chenzhou Scientific Reports |
| title | Integrating mining district data into ecological security pattern identification: a case study of Chenzhou |
| title_full | Integrating mining district data into ecological security pattern identification: a case study of Chenzhou |
| title_fullStr | Integrating mining district data into ecological security pattern identification: a case study of Chenzhou |
| title_full_unstemmed | Integrating mining district data into ecological security pattern identification: a case study of Chenzhou |
| title_short | Integrating mining district data into ecological security pattern identification: a case study of Chenzhou |
| title_sort | integrating mining district data into ecological security pattern identification a case study of chenzhou |
| url | https://doi.org/10.1038/s41598-025-00883-w |
| work_keys_str_mv | AT jiaweihui integratingminingdistrictdataintoecologicalsecuritypatternidentificationacasestudyofchenzhou AT yongshengcheng integratingminingdistrictdataintoecologicalsecuritypatternidentificationacasestudyofchenzhou |