underground coal fires
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2021 ◽  
Vol 10 (7) ◽  
pp. 449
Author(s):  
Yanyan Gao ◽  
Ming Hao ◽  
Yunjia Wang ◽  
Libo Dang ◽  
Yuecheng Guo

Underground coal fires can increase surface temperature, cause surface cracks and collapse, and release poisonous and harmful gases, which significantly harm the ecological environment and humans. Traditional methods of extracting coal fires, such as global threshold, K-mean and active contour model, usually produce many false alarms. Therefore, this paper proposes an improved active contour model by introducing the distinguishing energies of coal fires and others into the traditional active contour model. Taking Urumqi, Xinjiang, China as the research area, coal fires are detected from Landsat-8 satellite and unmanned aerial vehicle (UAV) data. The results show that the proposed method can eliminate many false alarms compared with some traditional methods, and achieve detection of small-area coal fires by referring field survey data. More importantly, the results obtained from UAV data can help identify not only burning coal fires but also potential underground coal fires. This paper provides an efficient method for high-precision coal fire detection and strong technical support for reducing environmental pollution and coal energy use.



2021 ◽  
Vol 13 (6) ◽  
pp. 1141
Author(s):  
Jinglong Liu ◽  
Yunjia Wang ◽  
Shiyong Yan ◽  
Feng Zhao ◽  
Yi Li ◽  
...  

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.



Author(s):  
Jun Li ◽  
Yinan Yang ◽  
Jinsong Li ◽  
Yandong Mao ◽  
Varinder Saini ◽  
...  


2020 ◽  
Vol 29 (6) ◽  
pp. 3973-3985 ◽  
Author(s):  
Haiyan Wang ◽  
Junpeng Zhang ◽  
Lei Zhang ◽  
Jiali Wang ◽  
Zuohui Xu


Geothermics ◽  
2020 ◽  
Vol 85 ◽  
pp. 101768
Author(s):  
M.M. Plakunov ◽  
C.C. Yavuzturk ◽  
A.D. Chiasson


2020 ◽  
Vol 136 ◽  
pp. 136-147 ◽  
Author(s):  
Zeyang Song ◽  
Xinyan Huang ◽  
Claudia Kuenzer ◽  
Hongqing Zhu ◽  
Juncheng Jiang ◽  
...  


2020 ◽  
Vol 219 ◽  
pp. 103382 ◽  
Author(s):  
Zeyang Song ◽  
Xinyan Huang ◽  
Juncheng Jiang ◽  
Xuhai Pan


2019 ◽  
Vol 332 (2) ◽  
pp. 022008
Author(s):  
Dongxue Zhang ◽  
Yong Pan ◽  
Zeyang Song ◽  
Xueliang Zhu ◽  
Sikai Han ◽  
...  


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