Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang
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...
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doaj-fbad94490251404392c161036c738a052021-03-18T00:04:01ZengMDPI AGRemote Sensing2072-42922021-03-01131141114110.3390/rs13061141Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiangJinglong Liu0Yunjia Wang1Shiyong Yan2Feng Zhao3Yi Li4Libo Dang5Xixi Liu6Yaqin Shao7Bin Peng8Key Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Land Environment and Disaster Monitoring, MNR, China University of Mining and Technology, Xuzhou 221116, ChinaCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of Mines and Coals, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaXinjiang Coalfield Fire Extinguishing Engineering Bureau, Urumqi 830000, ChinaUnderground 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 (R<sup>2</sup> reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.https://www.mdpi.com/2072-4292/13/6/1141underground coal fire recognitionthermal infrared technologyDS-InSARmulti-source remote sensing datafire identification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jinglong Liu Yunjia Wang Shiyong Yan Feng Zhao Yi Li Libo Dang Xixi Liu Yaqin Shao Bin Peng |
spellingShingle |
Jinglong Liu Yunjia Wang Shiyong Yan Feng Zhao Yi Li Libo Dang Xixi Liu Yaqin Shao Bin Peng Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang Remote Sensing underground coal fire recognition thermal infrared technology DS-InSAR multi-source remote sensing data fire identification |
author_facet |
Jinglong Liu Yunjia Wang Shiyong Yan Feng Zhao Yi Li Libo Dang Xixi Liu Yaqin Shao Bin Peng |
author_sort |
Jinglong Liu |
title |
Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang |
title_short |
Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang |
title_full |
Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang |
title_fullStr |
Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang |
title_full_unstemmed |
Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, XinJiang |
title_sort |
underground coal fire detection and monitoring based on landsat-8 and sentinel-1 data sets in miquan fire area, xinjiang |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-03-01 |
description |
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 (R<sup>2</sup> reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires. |
topic |
underground coal fire recognition thermal infrared technology DS-InSAR multi-source remote sensing data fire identification |
url |
https://www.mdpi.com/2072-4292/13/6/1141 |
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