Construction of landscape eco-geological risk assessment framework in coal mining area using multi-source remote sensing data

被引:4
|
作者
Zhu, Xiaoya [1 ,2 ]
Li, Peixian [1 ,3 ]
Wang, Bing [1 ]
Zhao, Sihai [1 ]
Zhang, Tao [1 ]
Yao, Qingyue [1 ]
机构
[1] China Univ Min & Technol Beijing, Coll Geosci & Surveying Engn, Beijing 100083, Peoples R China
[2] Minist Nat Resources, Key Lab Mine Geol Hazards Mech & Control, Xian 710054, Peoples R China
[3] China Univ Min & Technol Beijing, Res Ctr Ecogeol Environm & Remote Sensing Big Data, Inner Mongolia Res Inst, Beijing 010300, Peoples R China
关键词
Landscape eco-geological risk; Landscape loss index; Multiscale geographically weighted regression; Shenfu mining area; ECOLOGICAL RISK; REGRESSION;
D O I
10.1016/j.ecoinf.2024.102635
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
High-intensity and large-scale mining activities have aggravated regional eco-geological risk. Therefore, it is significantly essential to conduct an assessment of the eco-geological risk of mining areas. Although some progress has been achieved in ecological risk assessment studies, existing approaches are not entirely suitable for coal bases with high landscape fragmentation and dense coal mining activities. Here, we developed a novel landscape ecological and geological risk (LEGR) assessment framework based on theories that include landscape ecological risk and eco-geological risk. The framework selected 10 indicators, including slope, fluctuation, lithological hardness, soil type, FVC, RSEI, precipitation, biological abundance, distance to road and subsidence rate, and calculated the weights of indicators by introducing the AHP-CRITIC coupled weighting model. Then, the impact of landscape disturbances on eco-geological risk is quantified by measuring landscape losses. This framework was applied to the Shenfu mining area (SFMA), a typical coal base in northwest China. The results indicated the LEGR was moderate in the SFMA whose spatial distribution exhibited an increasing trend from southwest to northeast. Besides, the high LEGR was mainly in the aggregated mining area with high subsidence. For the eco-geological environment monitoring at the mine scale, a multiscale geographically weighted regression (MGWR) model was utilized for analyzing the relationship between indicators and LEGR within the disturbed range of coal mining. It provided valuable insights for the formulation of environmental protection policies in the mining area.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] Assessment of agricultural drought based on multi-source remote sensing data in a major grain producing area of Northwest China
    Cai, Siyang
    Zuo, Depeng
    Wang, Huixiao
    Xu, Zongxue
    Wang, GuoQing
    Yang, Hong
    AGRICULTURAL WATER MANAGEMENT, 2023, 278
  • [32] Construction of Multi-Source Remote Sensing Data Geodatabase Based on Urban Establishment up at Mountains
    Wang, Beibei
    Yang, Kun
    Yuan, Lei
    Zhu, Yanhui
    2015 23RD INTERNATIONAL CONFERENCE ON GEOINFORMATICS, 2015,
  • [33] Estimation of Leaf Area Index for Dendrocalamus giganteus Based on Multi-Source Remote Sensing Data
    Qin, Zhen
    Yang, Huanfen
    Shu, Qingtai
    Yu, Jinge
    Xu, Li
    Wang, Mingxing
    Xia, Cuifen
    Duan, Dandan
    FORESTS, 2024, 15 (07):
  • [34] Progress and prospects of multi-source remote sensing monitoring technology for coal mining subsidence in mining areas of the western Loess Plateau
    Tang F.
    Yang Q.
    Meitan Kexue Jishu/Coal Science and Technology (Peking), 2023, 51 (12): : 9 - 26
  • [35] WILDFIRE RISK ASSESSMENT USING MULTI-SOURCE REMOTE SENSE DERIVED VARIABLES
    Wen, Chongbo
    He, Binbin
    Quan, Xingwen
    Liu, Xiangzhuo
    Liu, Xiaofang
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 7644 - 7647
  • [36] Dynamic monitoring of land subsidence in mining area from multi-source remote-sensing data - a case study at Yanzhou, China
    Hu, Zhenqi
    Xu, Xianlei
    Zhao, Yanling
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2012, 33 (17) : 5528 - 5545
  • [37] Soil moisture content inversion research using multi-source remote sensing data
    Zhang Chengcai
    Zhu Zule
    LAND SURFACE REMOTE SENSING II, 2014, 9260
  • [38] Multi-source Remote Sensing Images Data Integration and Sharing Using Agent Service
    Cui, Binge
    Chen, Xin
    Song, Pingjian
    Liu, Rongjie
    WEB INFORMATION SYSTEMS ENGINEERING - WISE 2009, PROCEEDINGS, 2009, 5802 : 235 - +
  • [39] Spatial Scaling of Forest Aboveground Biomass Using Multi-Source Remote Sensing Data
    Wang, Xinchuang
    Jiao, Haiming
    IEEE ACCESS, 2020, 8 : 178870 - 178885
  • [40] A Study on Urban Thermal Field of Shanghai Using Multi-source Remote Sensing Data
    Li, Cheng-Fan
    Yin, Jing-Yuan
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2013, 41 (04) : 1009 - 1019