The Xinjiang is an important coal production base in China and also a serious coal fire disaster area. Coal fires not only waste resources, but also cause air pollution and damage to the ecological environment. Hence, it is very important to identify and monitor the underground coal fire areas accurately and efficiently for the control of coal fires. Interferometric synthetic aperture radar (InSAR) technology identifies and monitors coal fire areas by monitoring surface subsidence caused by burned out area. Compared with traditional coal fire monitoring technology, InSAR technology has the advantages of allweather and high efficiency. But the fire areas are often distributed in wild areas, this factor significantly limits the application of the traditional Persistent Scatterer interferometry (PSI) technology. In addition, Xinjiang coal fires are mostly located in historical goafs, so it is necessary to distinguish the subsidence caused by mining and coal fires. Therefore, distributed scatterer interferometry (DSI) technology is used to monitor the Miquan fire area in Xinjiang in this paper. The results show that compared with PSI technology, DSI technology can expand the number of effective monitoring points 124 times. On this basis, spatiotemporal analysis of surface subsidence in the study area suggests that the subsidence caused by mining and coal fires exhibits significantly different space-time evolution rules. Therefore, in the future, the coal fire area and mining area can be separated and identified according to these rules. The final identified coal fire area contains all measured coal fire points, and accurately monitors the fire extinguishing area. INDEX TERMS Coal fire monitoring, subsidence information, InSAR, spatio-temporal analysis.
Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.
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