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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. doi: 10.13203/j.whugis20220184
Citation: 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. doi: 10.13203/j.whugis20220184

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

doi: 10.13203/j.whugis20220184
Funds:

The National Natural Science Foundation of China (41874044).

  • Received Date: 2022-05-22
    Available Online: 2022-06-21
  • 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 from the same source——phenomenon to image mapping——transmission from source to phenomenon——multiple image recognition source is analyzed. 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 Baoan in the Xinjiang Uygur Autonomous Regions, China,the authors give the research progress and effects of polarized time-series InSAR fire area deformation detection, spatio-temporal temperature threshold method fire area delineation, combined thermal infrared + radar + optical 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.
  • [1] 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
    [2] 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 International, 2018, 25(26):26603-26616
    [3] Xu Y., Fan HD, Dang LB. 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.
    [4] 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
    [5] 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
    [6] 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
    [7] 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
    [8] 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
    [9] 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
    [10] 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
    [11] 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
    [12] 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
    [13] Wang Y J, Tian F, Huang Y, et al. Monitoring Coal Fires in Datong Coalfield Using MultiSource Remote Sensing Data[J]. Transactions of Nonferrous Metals Society of China, 2015, 25(10):3421-3428
    [14] 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
    [15] 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 GeoInformation, 2021, 10(7):449
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Research on Multi-source Remote Sensing Detection of Concealed Fire Sources in Coalfields

doi: 10.13203/j.whugis20220184
Funds:

The National Natural Science Foundation of China (41874044).

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 from the same source——phenomenon to image mapping——transmission from source to phenomenon——multiple image recognition source is analyzed. 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 Baoan in the Xinjiang Uygur Autonomous Regions, China,the authors give the research progress and effects of polarized time-series InSAR fire area deformation detection, spatio-temporal temperature threshold method fire area delineation, combined thermal infrared + radar + optical 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.

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. doi: 10.13203/j.whugis20220184
Citation: 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. doi: 10.13203/j.whugis20220184
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