Paper Info
Title
Study on the Landscape Space of Typical Mining Areas in Xuzhou City from 2000 to 2020 and Optimization Strategies for Carbon Sink Enhancement
Abstract
The continuous extraction of mining resources has led to the destruction of landscape space, which has had a great impact on the human living environment and pristine ecosystems. Optimizing the ecological spatial networks of mining areas can restore and enhance the damaged ecological environment. However, there are few cases of ecological spatial network optimization in mining areas, and there are still some shortcomings. Therefore, in this study, we propose an ecological spatial network theory and a synergistic enhancement of ecological functions and carbon sink optimization model (SEEC) for urban restoration in mining areas, emphasizing the functional and carbon sink nature of ecological sources. We selected a typical mining area in Xuzhou City as the study area, explored the changes in the nature and function of the ecological spatial network from 2000 to 2020, and selected the ecological spatial network in the mining area of Xuzhou City in 2020 as the optimization study case, adding 27 ecological stepping stones and 72 ecological corridors. Through the comparison of robustness before and after optimization, we found that the optimized ecological spatial network has a stronger stability and ecological restoration ability. This study provides strategies and methods for ecological restoration projects in national mining cities and also provides references and lessons for ecological restoration in other mining areas in the future.
Year
DOI
Venue
2022
10.3390/rs14174185
REMOTE SENSING
Keywords
DocType
Volume
spatiotemporal ecological spatial network, sustainable urban development, ecological restoration, complex networks, boosting carbon sinks, robustness
Journal
14
Issue
ISSN
Citations 
17
2072-4292
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Shi Qiu100.34
Qiang Yu201.35
Teng Niu300.34
Minzhe Fang400.34
Hongqiong Guo500.34
Hongjun Liu611.39
Song Li7117.33