Application of mining collapse recognition technology based on multi-source remote sensing
YANG Xianhua1(), WEI Peng2, LYU Jun3, HAN Lei1, SHI Haolin1, LIU Zhi1
1. Sichuan Key Laboratory of Rare Earth Strategic Resources Evaluation and Utilization, Sichuan Geological Survey Institute, Chengdu 610081, China 2. Sichuan Academy of Land and Space Ecological Restoration and Geological Disaster Prevention and Control, Chengdu 610081, China 3. People’s Government of Baodun Town, Chengdu 611438, China
Mining collapse has caused damage to soil, vegetation, and water resources. With the implementation of the national ecological restoration strategy, it is significant to effectively identify and monitor collapse areas. For this purpose, based on multi-source high-resolution remote sensing images and Sentinel-1 SAR radar images, this study identified and monitored the mining collapses of a coal mine in Baiyin City, Gansu Province using the two technologies, namely the Stacking-InSAR method for extracting ground subsidence data and the human-computer interactive interpretation of optical images of mining collapse. Moreover, this study comprehensively compared the characteristics of both techniques and explored the application prospects of both techniques in the deployment of ecological restoration engineering. The results are as follows: ① The Stacking-InSAR radar monitoring technology can better reflect the deformation during the monitoring period and can effectively identify the mining collapse areas in shallow, middle, and deep coal seams. ② The high-resolution optical image technology can better identify the mining collapse areas in shallow and middle coal seams, more accurately identify the damaged land, and can well identify the historically formed mining collapse areas and damaged land whose collapse deformation has stopped. ③ The collapse deformation and land damage of various stages can be obtained by combining the InSAR monitoring technology and the recognition method base on high-resolution remote sensing images, thus providing detailed and reliable basic data for ecological restoration engineering.
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