Abstract
Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field (North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008–2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR (IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014 (181.1 hectares) and 2016 (178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.
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Adler-Golden SM, Berk A, Bernstein LS, et al. (1999) Atmospheric correction for shortwave spectral imagery based on MODTRAN4. In: Imaging Spectrometry V, 1999. International Society for Optics and Photonics. pp 61–70. https://doi.org/10.1117/12.366315
Afify NM, Sheta AAS, Arafat SM, et al. (2017) Land-cover classification for east suez canal region using hyperspectral EO-1 data. European Chemical Bulletin 6:525–530. https://doi.org/10.17628/ecb.2017.6.525-530
Barsi JA, Barker JL, Schott JR (2003) An atmospheric correction parameter calculator for a single thermal band earth-sensing instrument. In: Geoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International, IEEE. pp 3014–3016. https://doi.org/10.1109/IGARSS.2003.1294665
Basommi LP, Guan QF, Cheng DD, et al. (2016) Dynamics of land use change in a mining area: a case study of Nadowli District, Ghana. Journal of mountain science 13: 633–642. https://doi.org/10.1007/s11629-015-3706-4
Chan H-P, Chang CP, Dao PD (2018) Geothermal anomaly mapping using landsat etm+ data in ilan plain, northeastern taiwan. Pure and Applied Geophysics 175: 303–323. https://doi.org/10.1007/s00024-017-1690-z
Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote sensing of environment 113: 893–903. https://doi.org/10.1016/j.rse.2009.01.007
Chatterjee A, Bhattacharya A, Mukherjee A, et al. (2010) Study of coal mine fire in Damodar River basin, India using thermal remote sensing technique. In: 38th COSPAR Scientific Assembly. Held 18–15 July 2010, in Bremen, Germany. p 5. Available on: https://www.cospar-assembly.org/abstractcd/OLD/COSPAR-10/abstracts/data/pdf/abstracts/A31-0086-10.pdf (Accessed on 8 November 2018).
Coll C, Galve JM, Sanchez JM, et al. (2010) Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing 48: 547–555. https://doi.org/10.1109/TGRS.2009.2024934
Dozier J (1981) A method for satellite identification of surface temperature fields of subpixel resolution. Remote Sensing of environment 11: 221–229. https://doi.org/10.1016/0034-4257(81)90021-3
Du X, Bernardes S, Cao D, et al. (2015a) Self-adaptive gradientbased thresholding method for coal fire detection based on ASTER data—Part 2, validation and sensitivity analysis. Remote Sensing 7: 2602–2626. https://doi.org/10.3390/rs70302602
Du X, Cao D, Mishra D, et al. (2015b) Self-adaptive gradient-Based thresholding method for coal fire detection using ASTER thermal infrared data, part I: methodology and decadal change detection. Remote sensing 7: 6576–6610. https://doi.org/10.3390/rs70606576
Duan SB, Li ZL, Tang BH, et al. (2014) Estimation of diurnal cycle of land surface temperature at high temporal and spatial resolution from clear-sky MODIS data. Remote Sensing 6: 3247–3262. https://doi.org/10.3390/rs6043247
Ediriweera S, Pathirana S, Danaher T, et al. (2013) Evaluation of different topographic corrections for Landsat TM data by prediction of foliage projective cover (FPC) in topographically complex landscapes. Remote Sensing 5: 6767–6789. https://doi.org/10.3390/rs5126767
Engle MA, Radke LF, Heffern EL, et al. (2012) Gas emissions, minerals, and tars associated with three coal fires, Powder River Basin, USA. Science of the total environment 420: 146–159. https://doi.org/10.1016/j.scitotenv.2012.01.037
Fisher W, Knuth W (1968) Detection and delineation of subsurface coal fires by aerial infrared scanning. Geological Society America 115: 67–68.
Gangopadhyay PK, Lahiri-Dutt K, Saha K (2006) Application of remote sensing to identify coalfires in the Raniganj Coalbelt, India. International Journal of Applied Earth Observation and Geoinformation 8: 188–195. https://doi.org/10.1016/j.jag.2005.09.001
Gao M-L, Zhao W-J, Gong Z-N, et al. (2014) Topographic correction of ZY-3 satellite images and its effects on estimation of shrub leaf biomass in mountainous areas. Remote Sensing 6: 2745–2764. https://doi.org/10.3390/rs6042745
Gao M, Gong H, Zhao W, et al. (2016) An improved topographic correction model based on Minnaert. GIScience & Remote Sensing 53: 247–264. https://doi.org/10.1080/15481603.2015.1118976
Garrison T, Hower JC, Fryar AE, et al. (2016) Water and soil quality at two eastern-Kentucky (USA) coal fires. Environmental Earth Sciences 75: 574. https://doi.org/10.1007/s12665-016-5380-4
Gautam R, Singh D, Mittal A, et al. (2008) Application of SVM on satellite images to detect hotspots in Jharia coal field region of India. Advances in space research 41: 1784–1792. https://doi.org/10.1016/j.asr.2007.05.011
Gielisch H, Kropp C (2018) Coal Fires a major Source of Greenhouse Gases-a forgotten problem. Environmental Risk Assessment and Remediation 2(1): 5–8. https://doi.org/10.4066/2529-8046.100030
Gillespie A, Rokugawa S, Matsunaga T, et al. (1998) A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE transactions on geoscience and remote sensing 36: 1113–1126. https://doi.org/10.1109/36.700995
Gillespie AR (1986) Lithologic mapping of silicate rocks usingTIMS. In: The TIMS Data User’s Workshop, June 18–19 1985. JPL Pub. pp 29–44. Available on: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19870007685.pdf (Accessed on 8 November 2018)
Huo H, Jiang X, Song X, et al. (2014a) Detection of coal fire dynamics and propagation direction from multi-temporal nighttime Landsat SWIR and TIR data: A case study on the Rujigou coalfield, Northwest (NW) China. Remote sensing 6: 1234–1259. https://doi.org/10.3390/rs6021234
Huo H, Jiang X, Song X, et al. (2014) Coal fires dynamics detection over Rujigou coalfield, Ningxia, NW China. In: Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International, 2014b. IEEE. pp 4512–4515. https://doi.org/10.1109/IGARSS.2014.6947495
Huo H, Ni Z, Gao C, et al. (2015) A study of coal fire propagation with remotely sensed thermal infrared data. Remote Sensing 7: 3088–3113. https://doi.org/10.3390/rs70303088
Jiang W, Jia K, Chen Z, et al. (2017) Using spatiotemporal remote sensing data to assess the status and effectiveness of the underground coal fire suppression efforts during 2000–2015 in Wuda, China. Journal of Cleaner Production 142: 565–577. https://doi.org/10.1016/j.jclepro.2016.03.082
Karan SK, Samadder SR (2016) Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas. Environmental monitoring and assessment 188: 486. https://doi.org/10.1007/s10661-016-5494-x
Karan SK, Samadder SR, Maiti SK (2016) Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands. Journal of environmental management 182: 272–283. https://doi.org/10.1016/j.jenvman.2016.07.070
Kato S, Kouyama T, Nakamura R, et al. (2018) Simultaneous retrieval of temperature and area according to sub-pixel hotspots from nighttime Landsat 8 OLI data. Remote Sensing of Environment 204: 276–286. https://doi.org/10.1016/j.rse.2017.10.025
Kealy PS, Hook SJ (1993) Separating temperature and emissivity in thermal infrared multispectral scanner data: Implications for recovering land surface temperatures. IEEE Transactions on Geoscience and Remote Sensing 31: 1155–1164. https://doi.org/10.1109/36.317447
Kresse W, Danko DM (2012) Springer handbook of geographic information. Springer Science & Business Media, Berlin, Germany. pp 75–94. https://doi.org/10.1007/978-3-540-72680-7
Kuenzer C, Dech S (2014) Thermal Infrared Remote Sensing: Sensor, Methods, Applications. Springer, Dordrecht, The Netherlands; Heidelberg, Germany. pp 429–451. https://doi.org/10.1080/01431161.2014.928448
Kuenzer C, Stracher GB (2012) Geomorphology of coal seam fires. Geomorphology 138: 209–222. https://doi.org/10.1016/j.geomorph.2011.09.004
Lantzanakis G, Mitraka Z, Chrysoulakis N (2017) Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery. In: Perspectives on Atmospheric Sciences. Springer, Switzerland. pp 255–261. https://doi.org/10.1007/978-3-319-35095-0_36
Law K, Nichol J (2004) Topographic correction for differential illumination effects on IKONOS satellite imagery Int Arch Photogramm. Remote Sens Spat Inform Sci 35: 641–646. Available on: http://ira.lib.polyu.edu.hk/handle/10397/38933 (Accessed on 8 November 2018)
Li ZL, Tang BH, Wu H, et al. (2013a) Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment 131: 14–37. https://doi.org/10.1016/j.rse.2012.12.008
Li ZL, Wu H, Wang N, et al. (2013b) Land surface emissivity retrieval from satellite data. International Journal of Remote Sensing 34: 3084–3127. https://doi.org/10.1080/01431161.2012.716540
Majumdar T, Pal S, Bhattacharya AK (2012) Generation of emissivity and land surface temperature maps using MODIS TIR data for lithological mapping over the Singhbhum-Orissa Craton. Journal of the Geological Society of India 80: 685–699. https://doi.org/10.1007/s12594-012-0194-9
Matson M, Dozier J (1981) Identification of subresolution high temperature sources using a thermal IR sensor. Photogrammetric Engineering and Remote Sensing 47: 1311–1318. Available on: https://www.researchgate.net/profile/Jeff_Dozier/publication/233531133_Identification_of_subresolution_high_temperature_sources_using_thermal_IR_sensor/links/02e7e52ab314c0affe000000/Identification-ofsubresolution-high-temperature-sources-using-thermal-IRsensor.pdf (Accessed on 8 November 2018)
Maya M, Musekiwa C, Mthembi P, et al. (2015) Remote sensing and geochemistry techniques for the assessment of coal mining pollution, Emalahleni (Witbank), Mpumalanga. South African Journal of Geomatics 4: 174–188. https://doi.org/10.4314/sajg.v4i2.9
Melody S, Johnston F (2015) Coal mine fires and human health: What do we know? International Journal of Coal Geology 152: 1–14. https://doi.org/10.1016/j.coal.2015.11.001
Meyer P, Itten KI, Kellenberger T, et al. (1993) Radiometric corrections of topographically induced effects on Landsat TM data in an alpine environment. ISPRS Journal of Photogrammetry and Remote Sensing 48: 17–28. https://doi.org/10.1016/0924-2716(93)90028-L
Negm A, Mesbah S, Abdelaziz T, et al. (2017) Nile River Bathymetry by Satellite Remote Sensing Case Study: Rosetta Branch. In: The Nile River, The Handbook of Environmental Chemistry. Springer, Cham, Switzerland. pp 259–274. https://doi.org/10.1007/698_2017_17
Nguyen HL (2017a) Research on geological and topographical characteristics, and solutions for the optimation of coal discharge sites in Khanh Hoa mining area, Phu Luong district, Thai Nguyen province. Bachelor's thesis, Hanoi University of Natural Resources and Environment, Ha Noi, Viet Nam (In Vietnamese). Available online at: lib.hunre.edu.vn/Download.aspx?file=Nguy?n%20Huy%20L i?u.pdf&id=13038 (Accessed on 8 November 2018)
Nguyen TT (2017b) Use of Moran's I and robust statistics to separate geochemical anomalies in Jiurui area (Southeast China) Bulletin Of The Mineral Research and Exploration: 22–44. https://doi.org/10.19111/bulletinofmre.351376
Nguyen TT, Vu DT, Trinh LH, et al. (2016) Spatial Cluster and Outlier Identification of Geochemical Association of Elements: A Case Study in Juirui Copper Mining Area. Bulletin Of The Mineral Research and Exploration: 159–167. https://doi.org/10.19111/bmre.01695
Pal S, Vaish J, Kumar S, et al. (2016) Coal fire mapping of East Basuria Colliery, Jharia coalfield using vertical derivative technique of magnetic data. Journal of Earth System Science 125: 165–178. https://doi.org/10.1007/s12040-016-0655-4
Pandey J, Kumar D, Panigrahi D, et al. (2017) Temporal transition analysis of coal mine fire of Jharia coalfield, India, using Landsat satellite imageries. Environmental Earth Sciences 76: 439. https://doi.org/10.1007/s12665-017-6765-8
Poon LL, Song T, Rosenfeld R, et al. (2016) Quantifying influenza virus diversity and transmission in humans. Nature Genetics 48: 195. https://doi.org/10.1038/ng.3479
Prakash A, Gupta R, Saraf A (1997) A Landsat TM based comparative study of surface and subsurface fires in the Jharia coalfield, India. International journal of remote sensing 18: 2463–2469. https://doi.org/10.1080/014311697217738
Qi G, Wang D, Zheng K, et al. (2015) Kinetics characteristics of coal low-temperature oxidation in oxygen-depleted air. Journal of Loss Prevention in the Process Industries 35: 224–231. https://doi.org/10.1016/j.jlp.2015.05.011
Raju A, Singh A, Kumar S, et al. (2016) Temporal monitoring of coal fires in Jharia Coalfield, India. Environmental Earth Sciences 75: 989. https://doi.org/10.1007/s12665-016-5799-7
Reddy C, Srivastav S, Bhattacharya A (1993) Application of Thematic Mapper short wavelength infrared data for the detection and monitoring of high temperature related geoenvironmental features. International Journal of Remote Sensing 14: 3125–3132. https://doi.org/10.1016/j.jag.2005.09.001
Rhee J, Im J (2014) Estimating high spatial resolution air temperature for regions with limited in situ data using MODIS products. Remote Sensing 6: 7360–7378. https://doi.org/10.3390/rs6087360
Richards JA (2013) Remote sensing digital image analysis -An Introduction. Springer, Heidelberg New York Dordrecht London. pp 247–318. https://doi.org/10.1007/978-3-642-30062-2
Roy P, Guha A, Kumar KV (2015a) An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field, India. International Journal of Applied Earth Observation and Geoinformation 39: 120–127. https://doi.org/10.1016/j.jag.2015.03.009
Roy P, Guha A, Kumar KV (2015b) Structural control on occurrence and dynamics of Coalmine fires in Jharia Coalfield: a remote sensing based analysis. Journal of the Indian Society of Remote Sensing 43: 779–786. https://doi.org/10.1007/s12524-015-0451-7
Saini V, Gupta RP, Arora MK (2016) Environmental impact studies in coalfields in India: A case study from Jharia coalfield Renewable and Sustainable. Energy Reviews 53: 1222–1239. https://doi.org/10.1016/j.rser.2015.09.072
Shuguang Z, Kefa Z, Yao C, et al. (2015) Exploratory data analysis and singularity mapping in geochemical anomaly identification in Karamay, Xinjiang, China. Journal of Geochemical Exploration 154: 171–179. https://doi.org/10.1016/j.gexplo.2014.12.007
Singh A, Raju A, Pati P, et al. (2017) Mapping of Coal Fire in Jharia Coalfield, India: a Remote Sensing Based Approach. Journal of the Indian Society of Remote Sensing 45:369–376. https://doi.org/10.1007/s12524-016-0590-5
Sobrino JA, Jimenez-Munoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of environment 90: 434–440. https://doi.org/10.1016/j.rse.2004.02.003
Sobrino JA, Jiménez-Muñoz JC, Sòria G, et al. (2008) Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing 46: 316–327. https://doi.org/10.1109/TGRS.2007.904834
Sobrino JA, Oltra-Carrió R, Sòria G, et al. (2013) Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing. International Journal of Remote Sensing 34: 3177–3192. https://doi.org/10.1080/01431161.2012.716548
Sokol E, Kudinov E, Kiriltseva N, et al. (2010) Geological Prereguisites of Late Cenozoic Coal Fires in the Kuznetsk Coal Basin, West Siberia, Russia. In: Latest Developements in Coal Fire Research: Bridging the Science, Economics and Politics of a Global Disaster. Proceedings of ICCFR 2 Second International Conference on Coal Fire Research (19-21 May 2010 dbb forum Berlin, Germany), 2010. Elsevier, Berlin, Germany.
Song Z, 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. International Journal of Coal Geology 171: 142–152. https://doi.org/10.1016/j.coal.2017.01.008
Song Z, Kuenzer C, Zhu H, et al. (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. International Journal of Coal Geology 141: 91–102. https://doi.org/10.1016/j.coal.2015.03.008
Srivastava1 VK, Agarwal1 S, Majumdar TJ (2017) Subsidence mapping related to coal mine fires in the Jharia coal field (India) Using Aster Tir and ERS SAR data. Journal of Earthquake Science and Engineering 4: 17–34. Available online at: https://www.joes.org.in/openaccess/P3-%20vol4.pdf (Accessed on 24 October 2018)
Su H, Zhou F, Qi H, et al. (2017) Design for thermoelectric power generation using subsurface coal fires. Energy 140: 929–940. https://doi.org/10.1016/j.energy.2017.09.029
Tang H, Li ZL (2013) Quantitative remote sensing in thermal infrared: theory and applications. Springer Science & Business Media, Berlin, Germany. https://doi.org/10.1007/978-3-642-42027-6
Tran VT, Tran DK, Phan QV (2010) Exploitation and management of burning coal seams in underground mines of Vietnam. In: Proceedings of the Second International Conference on Coal Fire Research (ICCFR 2), Germany, 2010. Elsevier, Berlin, Germany. pp 59–63.
Trinh LH, Zablotskii V (2017) The Application of LANDSAT Multi-Temporal Thermal Infrared Data to Identify Coal Fire in the Khanh Hoa Coal Mine, Thai Nguyen province, Vietnam. Izvestiya, Atmospheric and Oceanic Physics 53(9): 1081–1087. https://doi.org/10.1134/S0001433817090183
Tuyen LT, Tuan NV, Ohga K, et al. (2016) Characteristics of Spontaneous Combustion of Anthracite in Vietnamese Coal Mines. Journal of MMIJ 132: 167–174. (In Japanese) https://doi.org/10.2473/journalofmmij.132.167
Vázquez-Jiménez R, Romero-Calcerrada R, Ramos-Bernal RN, et al. (2017) Topographic Correction to Landsat Imagery through Slope Classification by Applying the SCS+ C Method in Mountainous Forest Areas ISPRS. International Journal of Geo-Information 6: 287. https://doi.org/10.3390/ijgi6090287
Veraverbeke S, Verstraeten WW, Lhermitte S, et al. (2010) Illumination effects on the differenced Normalized Burn Ratio's optimality for assessing fire severity. International Journal of Applied Earth Observation and Geoinformation 12: 60–70. https://doi.org/10.1016/j.jag.2009.10.004
Vu HN (2013) Assessment of the environmental status in Khanh Hoa coal field, Thai Nguyen province. Master's thesis, Thai Nguyen University of Agriculture and Forestry, Thai Nguyen University. (In Vietnamese) Available online at: https://text.123doc.org/document/2421902-danh-gia-hien-trang-moi-truong-tai-mothan-khanh-hoa-tinh-thai-nguyen.htm (Accessed on 8 November 2018)
Yang B, Chen Y, Li J, et al. (2005) Simple normalization of multi-temporal thermal IR data and applied research, on the monitoring of typical coal fires in northern China. In: Proceedings of the Geoscience and Remote Sensing Symposium, IGARSS, Vol. 8, pp 5725–5728, Seoul, South Korea. Available online at: http://www.dtic.mil/dtic/tr/fulltext/u2/a449933.pdf (Accessed on 8 November 2018)
Yu X, Guo X, Wu Z (2014) Land surface temperature retrieval from Landsat 8 TIRS—Comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing 6: 9829–9852. https://doi.org/10.3390/rs6109829
Zanter K (2015) Landsat 8 (L8) data users handbook Survey. Department of the Interior US Geological. Available online at: https://landsat.usgs.gov/sites/default/files/documents/Landsat8DataUsersHandbook.pdf (Accessed on 8 November 2018)
Acknowledgements
This study was funded by the Ministry-level Scientific and Technological Key Programs of Ministry of Natural Resources and Environment of Viet Nam “Application of thermal infrared remote sensing and GIS for mapping underground coal fires in Quang Ninh coal basin” (Grant No. TNMT.2017.08.06). The authors thank the editor, three anonymous reviewers and Ms. Cliona Langley (National University of Ireland Galway) for assistance with suggested improvements to this manuscript, Mr. Van Anh Do (Viet Nam Meteorological and Hydrological Administration, Ministry of Natural Resources and Environment) for providing meteorological air temperature data and the people who lived near the Khanh Hoa coal mining sites for their help during the field survey.
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Vu, T.D., Nguyen, T.T. Spatio-temporal changes of underground coal fires during 2008–2016 in Khanh Hoa coal field (North-east of Viet Nam) using Landsat time-series data. J. Mt. Sci. 15, 2703–2720 (2018). https://doi.org/10.1007/s11629-018-4997-z
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DOI: https://doi.org/10.1007/s11629-018-4997-z