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

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

References

[1] Agustan (2010). Ground deformation detection based on ALOS-PALSAR data utilizing DInSAR technique in Indonesia. Dissertation for the Doctoral Degree. Nagoya: Nagoya University

[2] Allievi J, Ambrosi C, Ceriani M, Ceriani M, Colesanti C, Crosta G B, Ferretti A, Fossati D (2003). Monitoring slow mass movements with the permanent scatterers technique. IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France

[3] Berardino P, Fornaro G, Lanari R, Sansosti E (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans Geosci Remote Sens, 40(11): 2375–2383

[4] Colesanti C, Ferretti A, Novali F, Prati C, Rocca F (2003). SAR monitoring of progressive and seasonal ground deformation using the permanent scatterers technique. IEEE Trans Geosci Remote Sens, 41(7): 1685–1701

[5] Deng J, Li B, Wang K, Wang C P (2016). Research status and outlook on prevention and control technology of coal fire disaster in China. Coal Science and Technology, 44(10):1–7+101 (in Chinese)

[6] Deng W, Wan Y Q, Zhao R C (2001). Detecting coal fires with a neural network to reduce the effect of solar radiation on landsat thematic mapper thermal infrared images. Int J Remote Sens, 22(6): 933–944

[7] Filatov A, Evtyushkin A, Bryksin V (2013). Some results of long term geodynamic monitoring of oil and gas fields and power engineering infrastructure using ENVISAT and ALOS SAR data. Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Tsukuba, Japan: 181–184

[8] Fitra I, James D (2012). Applications of DInSAR for measuring mine-induced subsidence and constraining ground deformation model. GeoCongres, Oakland, California, United States

[9] Gabriel A K, Goldstein R M, Zebker H A (1989). Mapping small elevation changes over large areas: differential radar interferometry. J Geophys Res-Sol Ea, 94 (B7): 9183–9191

[10] Ji H L, Tashpolat T, Cai Z Y, Shi Q D, Wei J, Xia J (2012a). The inversion and verification of land surface temperature for coal fire areas based on TM data. Remote Sens for Land and Resources, (04): 101–106 (in Chinese)

[11] Ji H L (2012b). Application of multi-scale thermal infrared remote sensing data in coalfield fire area information extraction. Dissertation for the Master’s Degree. Urumchi: Xinjiang University (in Chinese)

[12] Jiang L M, Lin H, Ma J W, Kong B, Wang Y (2011a). Potential of small-baseline SAR interferometry for monitoring land subsidence related to underground coal fires: Wuda (Northern China) case study. Remote Sens Environ, 115(2): 257–268

[13] Jiang W G, Wu J J, Gu L, Yang B, Chen Q, Liu X C (2011b). Monitoring method of underground coal fire based on night thermal infrared remote sensing technology. Spectrosc Spect Anal, 31(2): 357–361 (in Chinese)

[14] Jiang W G, Zhu X H, Wu J J, Gu L, Ma G B, Liu X C (2011c). Retrieval and analysis of coal fire temperature in Wuda Coalfield, Inner Mongolia, China. Chin Geogr Sci, 21(2): 159–166

[15] Jiménez-Muñoz J C, Sobrino J A, Skokovic D, Mattar C, Cristobal J (2014). Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data. IEEE Geosci Remote Sens Lett, 11(10): 1840–1843

[16] Kuenzer C, Stracher G B (2012). Geomorphology of coal seam fires. Geomorphology, 138(1): 209–222

[17] Lagios E, Sakkas V, Novali F, Fumagalli A, Del Conte S (2011). Ground deformation studies in Cephallonia island (Western Greece) based on DGPS & PS interferometry. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC Dissertation for the Doctoral Degree. Nagoya:

[18] Li F, Liang H D, Zhao X P, Bai J W, Cui Y K (2017). Remote sensing monitoring and assessment of fire-fighting effects in Wuda coal field, Inner Mongolia. Remote Sens for Land and Resources, 29(3): 217–223 (in Chinese)

[19] Li J (2015). Research of information extraction in the hazard area of coal spontaneous combustion based on remote sensing. China Mining Magazine, 24(06):138–141+146 (in Chinese)

[20] Li R R, Ben Z Q, Li P, Qin W Q (2016). Study on the method of coal fire monitoring based on Landsat-8 data. Journal of China Coal Society, 41(7): 1735–1740 (in Chinese)

[21] Liang T (2014). Monitoring the surface deformation of mining area based on short-baseset InSAR technology. Bulletin of Surveying and Mapping, (S2):82–84 (in Chinese)

[22] Liu Y L (2016). Land subsidence monitoring and correlation analysis with ground fluid exploitation using time series In SAR technique over the Yellow River delta (China). Institute of Oceanology, Chinese Academy of Sciences (in Chinese)

[23] Mishra R K, Bahuguna P P, Singh V K (2011). Detection of coal mine fire in Jharia Coal Field using Landsat-7 ETM+ data. Int J Coal Geol, 86(1): 73–78

[24] Qin Z H, Li W J, Xu B, Chen Z X, Liu J (2004). The estimation of land surface emissivity for Landsat TM6. Remote Sens for Land and Resources, (3): 28–32, 36, 41 (in Chinese)

[25] Qiu C J, Wang J, Liu L C, Bi F C (2012). Remote sensing technology applied to fire disaster monitoring and measuring of Wuda Coalfield. Coal Engineering, (08):130–133 (in Chinese)

[26] Samsonov S (2010). Topographic correction for ALOS PALSAR interferometry. IEEE Trans Geosci Remote Sens, 48(7): 3020–3027

[27] Samsonov S, Oreye N D (2012). Multidimensional time-series analysis of ground deformation from multiple InSAR data sets applied to Virunga Volcanic Province. Geophys J Int, 191(3): 1095–1108

[28] Slavecki R J (1964). Detection and location of subsurface coal fires. Proceeding of thirds symposium on remote sensing of environment. University of Michinggan: 537–549

[29] Soenen S A, Peddle D R, Coburn C A (2005). SCS+C: A modified sun-canopy-sensor topographic correction in forested terrain. IEEE Trans Geosci Remote Sens , 43(9): 2148–2159

[30] Song Z Y, Kuenzer C (2014). Coal fires in China over the last decade: a comprehensive review. Int J Coal Geol, 133: 72–99

[31] Song Z Y, Kuenzer C (2017). Spectral reflectance (400–2500 nm) properties of coals, adjacent sediments, metamorphic and pyrometamorphic rocks in coal-fire areas: a case study of Wuda coalfield and its surrounding areas, northern China. Int J Coal Geol, 171: 142–152

[32] Song Z Y, Kuenzer C, Zhu H Q, Zhang Z, Jia Y R, Sun Y L, Zhang J Z (2015). Analysis of coal fire dynamics in the Wuda syncline impacted by fire-fighting activities based on in-situ observations and Landsat-8 remote sensing data. Int J Coal Geol, 141–142: 91–102

[33] Tao L, Zhang H, Wang C, Tang Y X (2012). A Review on new multi-baseline DInSAR methods for surface deformation monitoring. Remote Sens Techno and App, 27(6): 805–811 (in Chinese)

[34] Voigt S, Tetzlaff A, Zhang J, Künzer C, Zhukov B, Strunz G, Oertel D, Roth A, van Dijk P, Mehl H (2004). Integrating satellite remote sensing techniques for detection and analysis of uncontrolled coal seam fires in North China. Int J Coal Geol, 59(1–2): 121–136

[35] Wang M M (2017). Methodology development for retrieving land surface temperature and near surface air temperature based on thermal infrared Remote Sensing. Institute of Remote Sensing and Digital Earth (in Chinese)

[36] Wang Q (2014). A Study of Ground Deformation over Taiyuan City Using PS-InSAR Technique. Bulletin of Surveying and Mapping, (5):71–75 (in Chinese)

[37] Wang Y J, Tian F, Huang Y, Wang J, Wei C J (2015). Monitoring coal fires in Datong coalfield using multi-source remote sensing data. Trans Nonferrous Met Soc China, 25(10): 3421–3428

[38] Wu D W (2001). Remote sensing prospecting information extraction method and application research in Qinghai Mangya Area. China University of Geosciences (in Chinese)

[39] Yang J K, Chen B Q, Deng K Z, He Q, Zhao W Y (2015). Monitoring and prediction of mining subsidence based on D-InSAR and probability integral method. Metal Mine, 44(4): 195–200 (in Chinese)

[40] Yu C, Li Z, Penna N T (2018a). Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model. Remote Sens Environ, 204: 109–121

[41] Yu C, Li Z, Penna N T, Crippa P (2018b). Generic atmospheric correction model for Interferometric Synthetic Aperture Radar observations. J Geophys Res-Sol Ea, 123(10): 9202–9222

[42] Yu C, Penna N T, Li Z (2017). Generation of real-time mode high-resolution water vapor fields from GPS observations. J Geophys Res Atmos, 122(3): 2008–2025

[43] Zeng Q Y, Zhang L S (2012). Discussion on drilling technology in coal seam burning area of Fukang Area in Xinjiang. Guangdong Sci and Techno, 21(07): 139–146 (in Chinese)

[44] Zhang Q S (2014). Fire detection technology of coal spontaneous combustion scope location based on compound mining depth coal seam. Coal Techno, 33(07): 270–271 (in Chinese)

[45] Zhang X, Van Genderen J V, Guan H Y, Kroonenberg S (2003). Spatial analysis of thermal anomalies from airborne multi-spectral data. Int J Remote Sens, 24(19): 3727–3742

[46] Zhang X, Van Genderen J V, Kroonenberg S B (1997). A method to evaluate the capability of Landsat-5 TM band 6 data for sub-pixel coal fire detection. Int J Remote Sens, 18(15): 3279–3288

[47] Zhang X, Zhang J, Kuenzer C, Voigt S, Wagner W (2004). Capability evaluation of 3–5 mm and 8–12.5 mm airborne thermal data for underground coal fire detection. Int J Remote Sens, 25(17): 3047–3058

[48] Zhang X Q, Zhang Y, Yu M D, Li X F, Wang T, Hu S R (2014). Damages and challenge for governing of coalfield fires in Xinjiang. China Min Mag, 23(S2): 93–95 (in Chinese)

[49] Zhang Z M, Jing L M, Liu L, Wang H S (2018). Detecting the underground coal fire by using landsat thermal infrared imagery:Taking Wuda Coalfield as an example. Bulletin of Surveying and Mapping, (03): 93–97 (in Chinese)

[50] Zhou H Y, Wang Y J, Yan S Y, Liu X X (2017). Land subsidence monitoring and analyzing of Cangzhou Area Sentinel-1A/B based time series InSAR. Bulletin of Surveying and Mapping, (07):89–93 (in Chinese)

[51] Zhou J B, Zeng X J, Fan T (2005). Geological characteristics of the coal resource zone at Bukang, Xinjiang. Xinjiang Geology, (02):146–151 (in Chinese)

[52] Zhu C G, Fan H D, Deng K Z (2014). Inversion of subsidence based on temporal InSAR Method in Tanggu Area. Geomatics and Information Science of Wuhan University, 39(2): 248–252 (in Chinese)

Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 41874044) and the Jiangsu Province Foundation of Brand Specialty Construction Projects in College and University (No. PPZY2015B144). The authors also gratefully acknowledge the European Space Agency for providing the Sentinel-1A SAR data and the US Geological Survey for providing the Landsat-8 data and the DEM data. The authors would also like to thank NASA for the auxiliary atmospheric data.

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