Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang

Jinglong Liu,Yi Li,Libo Dang,Shiyong Yan,Yaqin Shao,Yunjia Wang,Feng Zhao,Xixi Liu,Bin Peng

doi:10.3390/rs13061141

Abstract

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.

Highlights

Underground coal fire disasters refer to the interaction of coal seams with oxygen under the interference of the natural environment or human factors [1]
The study area is snow-prone from January to March, which could affect the remote sensing of coal fire signal
Combining experimental analysis and local weather data, we found that October to December is a suitable period using thermal remote sensing technology detect fire period for usingfor thermal infraredinfrared remote sensing technology to detecttocoal firecoal areas in areas in theregion

Summary

Introduction

Underground coal fire disasters refer to the interaction of coal seams with oxygen under the interference of the natural environment or human factors [1]. Nearly 1 billion tons of coal is destroyed by underground coal fires every year [5]. Several toxic and harmful gases and a large amount of dust, sulfide, and other pollutants are produced during the combustion of coal, causing severe environmental pollution [6]. The combustion space area created by coal fires can change the stress balance of the overlying rocks, causing cracks, fissures, and collapses, which seriously endanger the local residents’ lives and property [7]. It is crucial to identify, monitor, and control the underground coal fires

Methods

Results

Discussion

Conclusion