YU Hao，ZHANG Haolei，ZHANG Ziyan，et al. Multi-source remote sensing identification of underground coal fires based on the fusion of surface temperature and deformation.[J]. Coal Science and Technology，2024，52（7）：139−147
. DOI: 10.12438/cst.2023-1201| Citation: |
YU Hao，ZHANG Haolei，ZHANG Ziyan，et al. Multi-source remote sensing identification of underground coal fires based on the fusion of surface temperature and deformation.[J]. Coal Science and Technology，2024，52（7）：139−147 . DOI: 10.12438/cst.2023-1201
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Underground coal fires have strong concealment and great harm, not only damaging vegetation and ecological environment, causing serious air pollution, but also easily inducing geological disasters, threatening the safety of life and property of surrounding people, as well as the safety of coal production. Therefore, accurate identification and monitoring of underground coal fire disasters is of great significance. To address the issue of difficulty in accurately identifying underground coal fires using a single remote sensing method, multiple Landsat-8 and Sentinel-1 A images from 2017 to 2019 were used. Long term surface temperature and surface deformation of Shuixigou coalfield were obtained using generalized single channel algorithm and DS–InSAR （Distributed Scatterer Inter fabric Synthetic Aperture Radar） technology, respectively. On this basis, research on multi-source remote sensing recognition of underground coal fires by integrating surface temperature and deformation was carried out based on methods such as threshold segmentation and spatiotemporal coupling superposition analysis. The results indicate that the spatiotemporal collaborative analysis of surface long-term temperature and deformation can effectively overcome the impact of complex abnormal signals such as non coal fire high temperature or deformation, and basically accurately restore the response characteristics of underground coal fire signals in the two dimensions of surface temperature and deformation. Moreover, subtle differences were found in the spatial distribution patterns of surface temperature anomalies and deformation anomalies in underground coal fire areas. The deformation anomaly benefits from the resolution of SAR images and fewer external interference factors, which have a stronger indicating effect on underground coal fire identification. However, the range of coal fire areas correctly identified based on temperature anomalies has better spatial consistency with the actual coal fire boundaries. In addition, there is a small deviation between the temperature and deformation peak spatial position of underground coal fire disasters. However, there is a clear consistency in the response between temperature and deformation in the time dimension, which is characterized by stable abnormal high temperatures and continuous subsidence in the coal fire area. It can be seen that compared to a single remote sensing method, the method of integrating temperature and deformation can more accurately identify underground coal fire areas, providing good technical support for the wide area survey and timely prevention and control of underground coal fire disasters.
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