Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R² reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.

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.

Mining-induced subsidence boundaries, i.e., the surface areas affected by underground mining, play an important role in surface damage assessment and illegal mining identification. Traditional boundary delineation methods rely on field surveys, which restrict their applicability in regions with limited ground observations. Interferometric Synthetic Aperture Radar (InSAR) technology provides a cost-effective and non-contact solution for delineating subsidence boundaries. However, existing InSAR-based methods for subsidence boundary delineation are susceptible to observation noise and other deformation sources, which reduce the accuracy of boundary identification. To this end, this study proposes a novel method for delineating mining-induced subsidence boundaries by integrating both the magnitude and direction of InSAR-derived deformation gradients, referred to as DMSB-DG. First, time-series line-of-sight (LOS) deformation is obtained based on InSAR technology over mining areas. Then, the Roberts operator is employed to compute the magnitude and direction of the deformation gradients, which serve as the basis for boundary delineation. Finally, the ISODATA clustering algorithm is used, incorporating both the magnitude and direction of the deformation gradients as dual constraints to achieve accurate delineation of mining-affected boundaries. The combination of the two features effectively enhances the completeness and accuracy of boundary delineation. The performance of the proposed DMSB-DG method has been verified by simulation and field data. Specifically, compared with the adaptive mining subsidence boundary delimitation (ASBD) method, the proposed method achieved Kappa coefficients of 91.96% and 87.28%, representing improvements of 21.23% and 27.14% in two field tests, respectively. Furthermore, the influence of ascending and descending SAR images, as well as observational noise, on the performance of the proposed algorithm is also evaluated. The results demonstrate that the proposed method effectively suppresses InSAR noise and other interfering deformations, enabling high-precision delineation of mining impact boundaries.