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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 43-54     DOI: 10.6046/zrzyyg.2020137
Monitoring and interpretation of land subsidence in mining areas in Xuzhou City during 2016—2018
LI Mengmeng(), FAN Xueting, CHEN Chao, LI Qiannan, YANG Jin
Provincial Geomatics Centre of Jiangsu, Nanjing 210013, China
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The time-series interferometric synthetic aperture Radar (InSAR) technology has been widely used since it allows for the safe and efficient obtainment of large-scale high-precision ground subsidence data. It is still a hot topic to efficiently obtain accurate land subsidence data of mining areas at different mining states using this technology to provide data support for the ecological governance of the mining areas. Based on Sentinel-1A images (58 scenes per complete orbit), this paper conducts time series monitoring of six mining areas in Xuzhou City using the multiple master-image coherent target small-baseline interferometric SAR (MCTSB-InSAR) technique and obtains land subsidence results during 2016—2018. Meanwhile, it verifies the accuracy of the obtained subsidence rate using the measured data in a similar period, yielding a difference in root mean square error of 4.0 mm/a. Therefore, the monitoring requirements can be satisfied. The monitoring results are as follows. The Zhangshuanglou and Sanhejian coal mines suffered serious land subsidence, with the maximum average annual subsidence rate exceeding 100 mm/a and the maximum cumulative subsidence exceeding 300 mm. In comparison, the Qishan, Shitun, Quantai, and Zhangji coal mines experienced light subsidence, which all occurred within the mining areas and did not show a notable expansion trend during the monitoring period. Based on these results and the monitoring data of the basic geographical state of Jiangsu Province in 2016, there were 2 844 and 672 high-coherence points falling in houses and roads, respectively for the Sanhejian and Zhangshuanglou coal mines, which accounted for 73.66% and 63.33% of the total high coherence points of the mines, respectively. For the mining areas except for the Quantai coal mine, there was a roughly linear relationship between the subsidence amount and time, which was stronger in the mines under mining than in the mines where mining had stopped. In contrast, the relationship between the subsidence amount in the Quantai coal mine and time presented a nonlinear law. The experiment results show that Sentinel-1A images and the MCTSB-InSAR technique have good application prospects in the monitoring and analysis of land subsidence in mining areas.

Keywords mining area      subsidence monitoring      time-series analysis      InSAR technique     
ZTFLH:  P237P258  
Issue Date: 23 December 2021
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Mengmeng LI
Xueting FAN
Qiannan LI
Cite this article:   
Mengmeng LI,Xueting FAN,Chao CHEN, et al. Monitoring and interpretation of land subsidence in mining areas in Xuzhou City during 2016—2018[J]. Remote Sensing for Natural Resources, 2021, 33(4): 43-54.
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煤矿名称 投产时间 停采时间 生产能力/(万t·a-1)
权台煤矿 1959年12月 2015年12月 110
旗山煤矿 1959年 2016年1月 150
张集煤矿 1979年6月 2016年5月 85
拾屯煤矿 1992年1月 2016年12月 15
三河尖煤矿 1988年8月 2019年 120
张双楼煤矿 1986年 120
Tab.1  Basic information of mining area
Fig.1  Images, bench mark and mining map
Fig.2  Technique flow chart
Fig.3  Spatial and temporal distribution of small baseline pair
Fig.4  Statistics of subsidence rate difference between level point and InSAR point, and point for different difference ranges
Fig.5  Maximum subsidence rate and value of mining area
Fig.6-1  Spatial distribution of land subsidence rate and accumulated land subsidence in mining area
Fig.6-2  Spatial distribution of land subsidence rate and accumulated land subsidence in mining area
Fig.6-3  Spatial distribution of land subsidence rate and accumulated land subsidence in mining area
Fig.7  Statistic of high-coherence points in different subsidence rate ranges in the mining area
Fig.8  Land use classification and high-coherence distribution map of mining area
Fig.9-1  Time-series variation diagram of accumulated subsidence in mining area
Fig.9-2  Time-series variation diagram of accumulated subsidence in mining area
Fig.10  Statistical chart of accumulative subsidence in mining are
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