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- Title
大型煤炭基地生态系统服务功能遥感评估及演变分析.
- Authors
孙, 灏; 高, 金华; 崔, 希民; 王, 国瑞; 李, 培现
- Abstract
Ecosystem Service Function (ESF) is the direct benefit obtained by human beings from the ecosystem. Thus, it is significant to monitor the ecosystem status from the perspective of ESF. Taking a large coalmining base Ningdong, China as the study area, we designed remote sensing-based methods to calculate five important ESFs: Climate Regulation (CR), Wind prevention and Sand fixation (WS), Carbon sequestration and Oxygen Release (CO), Water Conservation (WC), and Soil Conservation (SC). Subsequently, we capitalized on long-term data to evaluate the spatiotemporal variation of those ESFs and evaluate the effect of coal-mining on ecosystem from the perspectives of ESF. Results demonstrated that: (1) From 2001 to 2019, the ESF of the study area showed an overall improvement trend, where the CR, WS, and CO have a very significant increasing trend. The WC decreased slightly, and the SC were found basically unchanged. (2) With regard to the spatial distribution, the ESF is relatively high for areas far away from the coal-mining face, otherwise it is relatively low. (3) Contrast analysis between coalmining area and non-coalmining area indicated that coalmining impedes the increase of ESF in the study area. The increasing rate of CR in non-coalmining area are about twice that in coalmining area. The increasing rate of CO and WS in non-coalmining area is about 1.5 times that of the coalmining area. The overall improvement of ESF in the study area may be the comprehensive result of climatic environment change and artificial restoration activities. However, the overall improvement rate is significantly lower than that of non-mining areas, which implies that the coal mining activities have caused a certain negative impact on ESF and it is still necessary to strengthen the protection and restoration of the ecosystem.
- Subjects
RESTORATION ecology; CARBON sequestration; WATER conservation; COAL mining; CLIMATE change; SOIL conservation; ECOSYSTEMS
- Publication
Journal of Remote Sensing, 2024, Vol 28, Issue 4, p926
- ISSN
1007-4619
- Publication type
Article
- DOI
10.11834/jrs.20231590