Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China

doi:10.1007/s11707-019-0757-9

Front. Earth Sci.

2020, Vol. 14

Issue (1) : 1-12 https://doi.org/10.1007/s11707-019-0757-9

RESEARCH ARTICLE

Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China

Shiyong YAN^1,²(

), Ke SHI^1,²(

), Yi LI^1,², Jinglong LIU^1,², Hongfeng ZHAO³

¹. Ministy of Natural Resource Key Lab of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou 221116, China
². School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
³. Comprehensive Geological Exploration Team in Coal Geology Bureau of Xinjiang Uygur Autonomous Region, Urumchi 830009, China

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Abstract

Xinjiang in China is one of the areas worst affected by coal fires. Coal fires cannot only waste a large amount of natural resources and cause serious economic losses, but they also cause huge damage to the atmosphere, the soil, the surrounding geology, and the environment. Therefore, there is an urgent need to effectively explore remote sensing based detection of coal fires for timely understanding of their latest development trend. In this study, in order to investigate the distribution of coal fires in an accurate and reliable manner, we exploited both Landsat-8 optical data and Sentinel-1A synthetic aperture radar (SAR) images, using the generalized single-channel algorithm and the InSAR time-series analysis approach, respectively, for coal fire detection in the southern part of the Fukang region of Xinjiang, China. The generalized single-channel algorithm was used for land surface temperature information extraction. Meanwhile, the time-series InSAR analysis technology was employed for estimating the surface micro deformation information, which was then used for building a band-pass filter. The suspected coal fire locations could then be established by a band-pass filtering operation on the obtained surface temperature map. Finally, the locations of the suspected coal fires were validated by the use of field survey data. The results indicate that the integration of thermal infrared remote sensing and radar interferometry technologies is an efficient investigation approach for coal fire detection in a large-scale region, which would provide the necessary spatial information support for the survey and control of coal fires.

Keywords land surface temperature generalized single-channel algorithm surface deformation time-series InSAR analysis filtering operation coal fire detection

Corresponding Author(s): Shiyong YAN,Ke SHI

Just Accepted Date: 24 July 2019 Online First Date: 23 September 2019 Issue Date: 24 March 2020

Cite this article:

Shiyong YAN,Ke SHI,Yi LI, et al. Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China[J]. Front. Earth Sci., 2020, 14(1): 1-12.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-019-0757-9
https://academic.hep.com.cn/fesci/EN/Y2020/V14/I1/1

Fig.1 True-color image of Landsat-8 bands 4, 3, and 2 in the research region.

Tab.1 Landsat-8 OLI and TIRS data acquired between 2015 and 2017.

Tab.2 Sentinel-1A SAR SLC data acquired between 2015 and 2017.

Fig.2 The technical procedure of coal fire detection.

Tab.3 Coefficient calculation table for the atmospheric parameters in Landsat-8 TIRS band 1 (^{Wang, 2017}).

Fig.3 Schematic diagram of the time-series processing of the land surface temperature information.

Fig.4 Schematic diagram of the land surface temperature anomaly areas in the research region.

Fig.5 The rate of surface deformation in the research region (a), and the SBAS-InSAR spatio-temporal baselines (b).

Fig.6 Schematic diagram of the extraction of suspected coal fire locations by a spatial filter.

Fig.7 Overlay sketch of the suspected coal fire areas in the research region and the coal fire locations identified by field survey.

Fig.8 Profile information of the surface temperature and subsidence at typical coal fire locations: (a), (b), (c), (d) represent the profile information of the No. 1, No. 2, No. 6, and No. 7 coal fire locations, respectively.

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