Research on Multi-source Remote Sensing Detection of Concealed Fire Sources in Coalfields

WANG Yunjia; YUAN Gang; WANG Teng; LIU Jinglong; ZHAO Feng; FENG Han; DANG Libo; PENG Kai; ZHANG Leixin

doi:10.13203/j.whugis20220184

Volume 47 Issue 10

Oct. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(10): 1651-1661. > DOI: 10.13203/j.whugis20220184

WANG Yunjia, YUAN Gang, WANG Teng, LIU Jinglong, ZHAO Feng, FENG Han, DANG Libo, PENG Kai, ZHANG Leixin. Research on Multi-source Remote Sensing Detection of Concealed Fire Sources in Coalfields[J]. Geomatics and Information Science of Wuhan University, 2022, 47(10): 1651-1661. DOI: 10.13203/j.whugis20220184

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Research on Multi-source Remote Sensing Detection of Concealed Fire Sources in Coalfields

WANG Yunjia^{1, 2,},
YUAN Gang^{1, 2},
WANG Teng^{1, 2},
LIU Jinglong^{1, 2},
ZHAO Feng^{1, 2},
FENG Han^{1, 2},
DANG Libo^{1, 2},
PENG Kai^{1, 2},
ZHANG Leixin^{1, 2}

1.
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2.
Key Laboratory of Land Environment and Disaster Monitoring, Ministry of Natural Resources, China University of Mining and Technology, Xuzhou 221116, China

Funds:

The National Natural Science Foundation of China 41874044

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Author Bio:
WANG Yunjia, PhD, professor, specializes in land environment and disaster monitoring. E-mail: wyj4139@cumt.edu.cn
Received Date: May 21, 2022
Available Online: June 20, 2022
Published Date: October 04, 2022

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Abstract

Abstract

Underground coal fire is widely distributed and repeatedly treated, causing waste of resources and ecological damage. China is the country with the most serious coal spontaneous combustion disaster in the world, 80% of coal seams have the tendency to spontaneous combustion. Rapid, comprehensive, timely and accurate detection of hidden fire sources in coalfields is the basis and prerequisite for fire prevention, extinguishing and ecological management. Multi-source remote sensing has a great potential for the applications, but it needs to penetrate the surface and go deep underground, and there are many bottlenecks to be solved. Firstly, the problem of multi-source remote sensing detection of hidden fires in coalfields is abstracted into the key nodes of same source (same underground spontaneous combustion source), multi-phenomenon (various abnormal phenomena formed on the surface), multi-image (photographed by multi-source remote sensing, including a variety of surface image of abnormal information). Meanwhile, the research chain of multiple phenomena is analyzed, which includes the same source, the phenomenon to image mapping, the transmission from source to phenomenon, and the multiple image recognition source. On these basis, the technical bottleneck of multi-source remote sensing detection of concealed fire sources in coalfields is discussed. Secondly, based on the research examples of concealed fire detection in coal fire areas of Fukang, Miquan and Bao'an in the Xinjiang Uygur Autonomous Regions, China, we give the research progress and effects of polarized time-series interferometric synthetic aperture radar (InSAR) fire area deformation detection, spatio-temporal temperature threshold method fire area delineation, multi-source satellite remote sensing fire area identification, and unmanned aerial vehicle fire area monitoring experiment. Finally, the development direction of integrating multi-source satellite remote sensing images and space-sky-ground-mine cooperative perception cognitions is prospected.
- multi-source remote sensing,
- coalfield spontaneous combustion,
- concealed fire source,
- multiple image recognition source,
- detection

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References

[1]	张建民, 管海晏, 曹代勇. 中国地下煤火研究与治理[M]. 北京: 煤炭工业出版社, 2008 Zhang Jianmin, Guan Haiyan, Cao Daiyong. Research and Control of Underground Coal Fire in China[M]. Beijing: China Coal Industry Publishing House, 2008
[2]	汪云甲. 矿区生态扰动监测研究进展与展望[J]. 测绘学报, 2017, 46 (10): 1705-1716 doi: 10.11947/j.AGCS.2017.20170358 Wang Yunjia. Research Progress and Prospect on Ecological Disturbance Monitoring in Mining Area [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1705-1716 doi: 10.11947/j.AGCS.2017.20170358
[3]	Song Z Y, Kuenzer C. Coal Fires in China over the last Decade: A Comprehensive Review[J]. International Journal of Coal Geology, 2014, 133: 72-99 doi: 10.1016/j.coal.2014.09.004
[4]	张学庆, 张渝, 于明达, 等. 新疆煤田火区的危害与治理挑战[J]. 中国矿业, 2014, 23 (S2): 93-95 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGKA2014S2024.htm Zhang Xueqing, Zhang Yu, Yu Mingda, et al. Damages and Cellenge for Governing of Coalfield Fires in Xinjiang[J]. China Mining Magazine, 2014, 23 (S2): 93-95 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGKA2014S2024.htm
[5]	熊盛青, 陈斌, 于长春, 等. 地下煤层自燃遥感与地球物理探测技术[M]. 北京: 地质出版社, 2006 Xiong Shengqing, Chen Bin, Yu Changchun, et al. Remote Sensing and Geophysical Detection Technology for Spontaneous Combustion of Underground Coal Seams[M]. Beijing: Geological Publishing House, 2006
[6]	武建军, 蒋卫国, 刘晓晨, 等. 地下煤火探测、监测与灭火技术研究进展[J]. 煤炭学报, 2009, 34(12): 1669-1674 doi: 10.3321/j.issn:0253-9993.2009.12.016 Wu Jianjun, Jiang Weiguo, Liu Xiaochen, et al. Innovative Technologies for Exploration, Monitoring and Extinction of Underground Coal Fires[J]. Journal of China Coal Society, 2009, 34(12): 1669-1674 doi: 10.3321/j.issn:0253-9993.2009.12.016
[7]	钟茂华, 符泰然, 胡忠斌. 新疆煤田火区现状调查研究: 小尺度区域热辐射信息分析[J]. 中国工程科学, 2010, 12(1): 12-17 doi: 10.3969/j.issn.1009-1742.2010.01.002 Zhong Maohua, Fu Tairan, Hu Zhongbin. Small-Scale Area Survey and Analyses of Xinjiang's Coal Field Fire in China[J]. Engineering Sciences, 2010, 12(1): 12-17 doi: 10.3969/j.issn.1009-1742.2010.01.002
[8]	邓军, 文虎, 张辛亥. 煤田火灾防治理论与技术[M]. 徐州: 中国矿业大学出版社, 2014 Deng Jun, Wen Hu, Zhang Xinhai. Coal Field Fire Prevention Theory and Technology[M]. Xuzhou: China University of Mining and Technology Press, 2014
[9]	Shao Z L, Jia X Y, Zhong X X, et al. Detection, Extinguishing, and Monitoring of a Coal Fire in Xinjiang, China[J]. Environmental Science and Pollution Research, 2018, 25(26): 26603-26616 doi: 10.1007/s11356-018-2715-6
[10]	汪云甲, 黄翌, 邵亚琴, 等. 矿区生态扰动监测与评价[M]. 北京: 科学出版社, 2021 Wang Yunjia, Huang Yi, Shao Yaqin, et al. Monitoring and Evaluation of Ecological Disturbance in Mining Area[M]. Beijing: Science Press, 2021
[11]	Xu Y, Fan H D, Dang L B. Monitoring Coal Seam Fires in Xinjiang Using Comprehensive Thermal Infrared and Time Series InSAR Detection[J]. International Journal of Remote Sensing, 2021, 42(6): 2220-2245 doi: 10.1080/01431161.2020.1823045
[12]	Riyas M J, Syed T H, Kumar H, et al. Detecting and Analyzing the Evolution of Subsidence Due to Coal Fires in Jharia Coalfield, India Using Sentinel-1 SAR Data[J]. Remote Sensing, 2021, 13(8): 1521 doi: 10.3390/rs13081521
[13]	Kim J, Lin S Y, Singh R P, et al. Underground Burning of Jharia Coal Mine(India)and Associated Surface Deformation Using InSAR Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 103: 102524 doi: 10.1016/j.jag.2021.102524
[14]	黄昭权, 张登荣, 王帆, 等. 基于差分干涉SAR的煤田火区地表形变监测[J]. 国土资源遥感, 2010, 22 (4): 85-90 https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG201004019.htm Huang Zhaoquan, Zhang Dengrong, Wang Fan, et al. Differential SAR Interferometry for the Monitoring of Underground Coal Spontaneous Combustion Zone Surface Deformation[J]. Remote Sensing for Land & Resources, 2010, 22(4): 85-90 https://www.cnki.com.cn/Article/CJFDTOTAL-GTYG201004019.htm
[15]	Gupta N, Syed T H, Athiphro A. Monitoring Subsurface Coal Fires in Jharia Coalfield Using Observations of Land Subsidence from Differential Interferometric Synthetic Aperture Radar (DInSAR)[J]. Journal of Earth System Science, 2013, 122(5): 1249-1258 doi: 10.1007/s12040-013-0355-2
[16]	Jiang L M, Lin H, Ma J W, et al. Potential of Small-Baseline SAR Interferometry for Monitoring Land Subsidence Related to Underground Coal Fires: Wuda(Northern China)Case Study[J]. Remote Sensing of Environment, 2011, 115(2): 257-268 doi: 10.1016/j.rse.2010.08.008
[17]	Voigt S, Tetzlaff A, Zhang J Z, et al. Integrating Satellite Remote Sensing Techniques for Detection and Analysis of Uncontrolled Coal Seam Fires in North China[J]. International Journal of Coal Geology, 2004, 59(1/2): 121-136
[18]	Zhou L F, Zhang D R, Wang J, et al. Mapping Land Subsidence Related to Underground Coal Fires in the Wuda Coalfield (Northern China) Using a Small Stack of ALOS PALSAR Differential Interferograms[J]. Remote Sensing, 2013, 5(3): 1152-1176 doi: 10.3390/rs5031152
[19]	许怡, 范洪冬, 党立波. 基于TIRS和TCP-InSAR的新疆广域煤田火区探测方法[J]. 金属矿山, 2019(10): 164-171 https://www.cnki.com.cn/Article/CJFDTOTAL-JSKS201910027.htm Xu Yi, Fan Hongdong, Dang Libo. Detection Method of Fire Area in Xinjiang Wide Area Coalfield Based on TIRS and TCP-InSAR[J]. Metal Mine, 2019(10): 164-171 https://www.cnki.com.cn/Article/CJFDTOTAL-JSKS201910027.htm
[20]	Liu J L, Wang Y J, Li Y, et al. Underground Coal Fires Identification and Monitoring Using Time-Series InSAR with Persistent and Distributed Scatterers: A Case Study of Miquan Coal Fire Zone in Xinjiang, China[J]. IEEE Access, 7: 164492-164506
[21]	刘竞龙, 汪云甲, 闫世勇, 等. 乌鲁木齐东侧煤火多源遥感融合探测[J]. 煤矿安全, 2019, 50(8): 158-161 https://www.cnki.com.cn/Article/CJFDTOTAL-MKAQ201908039.htm Liu Jinglong, Wang Yunjia, Yan Shiyong, et al. Multi-Source Remote Sensing Fusion Detection of Coal Fire in Eastern Urumchi[J]. Safety in Coal Mines, 2019, 50(8): 158-161 https://www.cnki.com.cn/Article/CJFDTOTAL-MKAQ201908039.htm
[22]	赵峰, 王腾, 彭锴, 等. 基于同质像元时序相位矩阵分解的DS目标相位优化方法: CN113687353A[P]. 2021-11-23 Zhao Feng, Wang Teng, Peng Kai, et al. DS Target Phase Optimization Method Based on Homogeneous Pixel Time Sequence Phase Matrix Decomposition: CN113687353A[P]. 2021-11-23
[23]	Wang T, Wang Y J, Zhao F, et al. A Spatio-Temporal Temperature-Based Thresholding Algorithm for Underground Coal Fire Detection with Satellite Thermal Infrared and Radar Remote Sensing[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 110: 102805 doi: 10.1016/j.jag.2022.102805
[24]	Liu J L, Wang Y J, Yan S Y, et al. Underground Coal Fire Detection and Monitoring Based on Landsat-8 and Sentinel-1 Data Sets in Miquan Fire Area, Xinjiang[J]. Remote Sensing, 2021, 13(6): 1141 doi: 10.3390/rs13061141
[25]	Wang Y J, Tian F, Huang Y, et al. Monitoring Coal Fires in Datong Coalfield Using Multi-Source Remote Sensing Data[J]. Transactions of Nonferrous Metals Society of China, 2015, 25(10): 3421-3428 doi: 10.1016/S1003-6326(15)63977-2
[26]	Yuan G, Wang Y J, Zhao F, et al. Accuracy Assessment and Scale Effect Investigation of UAV Thermography for Underground Coal Fire Surface Temperature Monitoring[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102426 doi: 10.1016/j.jag.2021.102426
[27]	Gao Y Y, Hao M, Wang Y J, et al. Multi-Scale Coal Fire Detection Based on an Improved Active Contour Model from Landsat-8 Satellite and UAV Images[J]. ISPRS International Journal of Geo-Information, 2021, 10(7): 449 doi: 10.3390/ijgi10070449
[28]	张雷昕, 汪云甲, 赵峰, 等. 基于无人机热红外遥感的新疆宝安煤矿火区探测[J]. 煤炭工程, 2021, 53(6): 162-166 https://www.cnki.com.cn/Article/CJFDTOTAL-MKSJ202106033.htm Zhang Leixin, Wang Yunjia, Zhao Feng, et al. Detection and Analysis of Fire Area in Xinjiang Baoan Coal Mine Based on UAV Thermal Infrared Remote Sensing[J]. Coal Engineering, 2021, 53(6): 162-166 https://www.cnki.com.cn/Article/CJFDTOTAL-MKSJ202106033.htm
[29]	龚健雅. 人工智能时代测绘遥感技术的发展机遇与挑战[J]. 武汉大学学报·信息科学版, 2018, 43(12): 1788-1796 doi: 10.13203/j.whugis20180082 Gong Jianya. Chances and Challenges for Development of Surveying and Remote Sensing in the Age of Artificial Intelligence[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 1788-1796 doi: 10.13203/j.whugis20180082
[30]	张兵. 遥感大数据时代与智能信息提取[J]. 武汉大学学报·信息科学版, 2018, 43 (12): 1861-1871 doi: 10.13203/j.whugis20180172 Zhang Bing. Remotely Sensed Big Data Era and Intelligent Information Extraction[J]. Geomatics and Information Science of Wuhan University, 2018, 43 (12): 1861-1871 doi: 10.13203/j.whugis20180172

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Research on Multi-source Remote Sensing Detection of Concealed Fire Sources in Coalfields

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