Article Dans Une Revue International Journal of Remote Sensing Année : 2021

Land subsidence induced by underground coal mining at Quang Ninh, Vietnam: persistent scatterer interferometric synthetic aperture radar observation using Sentinel-1 data

Résumé

Land subsidence occurs in Ha Long and Cam Pha cities due to human activities in which the underground coal mining exploitation plays a crucial responsibility. For mapping the spatial distribution of this phenomenon and monitoring its evolution over the time, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) approach was applied to 56 Sentinel-1A images, captured from June 2015 to June 2019. This approach allows monitoring ground displacement by continuously measuring the position of objects that persistently scatter radar beams. This interferometric synthetic aperture radar (InSAR) technique extracts deformation signals and it can be applied to areas where the discontinuous land subsidence is occurring. The mean land subsidence velocity map shows that two principal areas in Ha Long city including Ha Lam, Ha Trung, Ha Khanh, and Ha Tu wards, and Cam Pha city, specifically Mong Duong ward have been subsided in recent years with the maximum velocity of 14.5 mm year(-1) and a cumulative displacement ranging from 30 to 50 mm in the Line Of Sight (LOS) direction over 4 years. Field investigation recognized the direct impact of this phenomenon on houses, infrastructures, and natural features located above the underground coal mining utilities. This hazard has a close relationship with the exploiting process of coal seams, which alternately lay inside the sedimentary layers of the Hon Gai formation.

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Dates et versions

hal-04792737 , version 1 (20-11-2024)

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Citer

Vu Khac Dang, Thanh Dong Nguyen, Ngoc Hung Dao, Thi Loi Duong, Xuan Vinh Dinh, et al.. Land subsidence induced by underground coal mining at Quang Ninh, Vietnam: persistent scatterer interferometric synthetic aperture radar observation using Sentinel-1 data. International Journal of Remote Sensing, 2021, 42 (9), pp.3563-3582. ⟨10.1080/01431161.2021.1875513⟩. ⟨hal-04792737⟩
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