Monitoring coal seam fires in Xinjiang using comprehensive thermal infrared and time series InSAR detection

ABSTRACT

The combustion of underground coal seams has important effects on the national economy and strategic energy security of countries, and damages the environment, ecology, vegetation, and water resources. Xinjiang, China, is one of the most serious areas of coalfield fires in the world, and it is difficult for any single remote sensing detection method to meet the practical requirements for accurate detection of fire areas. In order to detect coalfield areas in Xinjiang more accurately and reliably, a detection method that integrates thermal infrared and radar remote sensing anomaly is proposed: The mono-window algorithm is used to retrieve the Land Surface Temperature (LST) using images from the Land Remote-Sensing Satellite System (Landsat) 8, the Adaptive Edge Threshold Algorithm (AETA) is used to extract the data characterized by LST anomalies, and Small Baseline Subsets Interferometric Synthetic Aperture Radar (SBAS-InSAR) is used to obtain the land surface subsidence information from Sentinel-1A images. Through the overlay analysis of the above-mentioned LST and subsidence information, the extraction of possible coal fire areas in the Jiangjun Gobi area of Xinjiang is realized. As well as according to the characteristic that underground coal fires melt snow in winter, areas where coal fires are unlikely to occur are excluded using the natural, true-colour images of Landsat 8. Finally, the proposed method is verified using the characteristics of the time series cumulative subsidence of the land surface in areas where coal fires are likely to occur, and by examining the coupling between subsidence velocity and anomalies in LST data. The results show that the proposed method can overcome the shortcomings of single-factor remote sensing detection to a certain extent, and has the ability to detect fire areas more accurately.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities under Grant [2019XKQYMS23].