A spatio-temporal temperature-based thresholding algorithm for underground coal fire detection with satellite thermal infrared and radar remote sensing

Abstract

Coal fire is a global catastrophe that causes resource waste, economic loss, and environmental pollution. Accurate detection of surface abnormalities induced by coal fires (such as thermal anomalies and land deformation) is critical for coal fire monitoring and extinguishment. However, the conventional thermal anomaly detection methods do not consider the temporal variation of land surface temperature (LST). Moreover, previous studies have not applied the polarimetric persistent scatterer interferometry(PolPSI) technique for coal-fire-related ground deformation monitoring, which is supposed to have better performance than conventional persistent scatterer interferometry (PSI) approaches. To this end, in this study, a spatio-temporal temperature-based thresholding (STTBT) algorithm is proposed to detect thermal anomalies, firstly. Then, the ground deformation obtained by the PolPSI method is combined with the detected thermal anomalies for the coal fire detection. Besides, a two-stage bandpass filter is employed to assist in detecting the suspected coal fire. The results in the Fukang coal fire area, China, demonstrate that, compared with the conventional methods, the proposed STTBT mothed achieves higher accuracy of thermal anomalies detection, thereby can able to detect coal fire areas more effectively. In addition, PolPSI has better performance than PSI in ground deformation monitoring over coal fire areas. The proposed STTBT combined with PolPSI can serve as a powerful tool for detecting and monitoring underground coal fires.