Izvestiya, Atmospheric and Oceanic Physics

, Volume 53, Issue 9, pp 1081–1087 | Cite as

The Application of LANDSAT Multi-Temporal Thermal Infrared Data to Identify Coal Fire in the Khanh Hoa Coal Mine, Thai Nguyen province, Vietnam

The Use of Space Information about the Earth


The Khanh Hoa coal mine is a surface coal mine in the Thai Nguyen province, which is one of the largest deposits of coal in the Vietnam. Numerous reasons such as improper mining techniques and policy, as well as unauthorized mining caused surface and subsurface coal fire in this area. Coal fire is a dangerous phenomenon which affects the environment seriously by releasing toxic fumes which causes forest fires, and subsidence of infrastructure surface. This article presents study on the application of LANDSAT multi-temporal thermal infrared images, which help to detect coal fire. The results obtained in this study can be used to monitor fire zones so as to give warnings and solutions to prevent coal fire.


coal fire remote sensing thermal infrared LANDSAT land surface temperature 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bondur, V.G., Importance of aerospace remote sensing approach to the monitoring of nature fire in Russia, Int. Forest Fire News, 2010, no. 40, pp. 43–57.Google Scholar
  2. Bondur, V.G., Satellite monitoring of wildfires during the anomalous heat wave of 2010 in Russia, Izv., Atmos. Ocean. Phys., 2011, vol. 47, no. 9, pp. 1039–1048.CrossRefGoogle Scholar
  3. Bondur, V.G., Satellite Monitoring of Trace Gas and Aerosol Emissions during Wildfires in Russia, Izv., Atmos. Ocean. Phys., 2016, vol. 52, no. 9, pp. 1078–1091.CrossRefGoogle Scholar
  4. Bondur, V.G. and Ginzburg, A.S., Emission of carbonbearing gases and aerosols from natural fires on the territory of Russia based on space monitoring, Dokl. Earth Sci., 2016, vol. 466, no. 2, pp. 148–152.CrossRefGoogle Scholar
  5. Carlson, T.N. and Ripley, D.A., On the relation between NDVI, fractional vegetation cover and leaf area index, Remote Sens. Environ., 1997, vol. 62, pp. 241–252.Google Scholar
  6. Chen, Y., Li, J., Yang, B., and Zhang, S., Detection of coal fire location and change based on multi-temporal thermal remotely sensed data and field measurements, Int. J. Remote Sens., 2007, vol. 28, no. 15, pp. 3173–3179.CrossRefGoogle Scholar
  7. Cracknell, A.P. and Mansor, S.B., Detection of sub-surface coal fires using Landsat thematic mapper data, Int. Arch. Photogram. Remote Sens., 1992, vol. 29, pp. 750–753.Google Scholar
  8. Gautam, R.S., Singh, V.K., and Mittal, D., An efficient contextual algorithm to detect subsurface fires with NOAA/AVHRR data, IEEE Trans. Geosci. Remote Sens., 2008, vol. 46, no. 7, pp. 2005–2015.CrossRefGoogle Scholar
  9. Huo H., Jiang X., Song, X., Li, Zh.-L., Ni, Zh., and Gao, C., 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 China, Remote Sens., 2014, vol. 6, no. 2, pp. 1234–1259. doi 10.3390/rs6021234CrossRefGoogle Scholar
  10. Jiang, Z., Huete, A.R., Chen, J., Chen, Y., Li, J., Yan, G., et al., Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction, Remote Sens. Environ., 2006, vol. 101, no. 3, pp. 366–378. doi 10.1016/j.rse.2006.01.003CrossRefGoogle Scholar
  11. Landsat-7 Science Data User’s Handbook, Greenbelt, MD: NASA/Goddard Space Flight Center.Google Scholar
  12. Luu, D.H. and Nguyen, T.H.L., Renewable energy policies for sustainable development in Vietnam, VNU J. Sci.: Earth Sci., 2009, vol. 25, no. 3, pp. 133–142.Google Scholar
  13. Mishra, R.K., Pandey, J., Chaudhary, S.K., Khalkho, A., and Singh, V.K., Estimation of air pollution concentration over Jharia coalfield based on satellite imagery of atmospheric aerosol, Int. J. Geomatics Geosci., 2013, vol. 4, no. 1, pp. 30–35.Google Scholar
  14. Mishra, R.K., Roy, P.N.S., Pandey, J., Khalkho, A., and Singh, V.K., Study of coal fire dynamics of Jharia coalfield using satellite data, Int. J. Geomatics Geosci., 2014, vol. 4, no. 3, pp. 477–484.Google Scholar
  15. Prakash, A. and Gupta, R.P., Surface fires in Jharia coalfield, India—their distribution and estimation of area and temperature from TM data, Int. J. Remote Sens., 1999, vol. 20, pp. 1935–1946.Google Scholar
  16. Prasun, K., Kuntala, L., and Kanika, S., Application of remote sensing to identify coalfires in the Raniganj Coalbelt, India, Int. J. Appl. Earth Obs. Geoinform., 2006, vol. 8, no. 3, pp. 188–195. doi 10.1016/j.jag.2005.09.001CrossRefGoogle Scholar
  17. Valor, E. and Caselles, V., Mapping land surface emissivity from NDVI. Application to European, African and South American areas, Remote Sens. Environ., 1996, vol. 57, pp. 167–184.Google Scholar
  18. Van de Griend, A.A. and Owen, M., On the relationship between thermal emissivity and the normalized difference vegetation index for natural surface, Int. J. Remote Sens., 1993, vol. 14, pp. 1119–1131.CrossRefGoogle Scholar
  19. Voigt, S., Tetzlaff, A., Zhang, J., Kunzer, C., Zhukov, B., and Strunz, G., Integrating satellite remote sensing techniques for detection and analysis of uncontrolled coal seam fire in North China, Int. J. Coal Geol., 2004, vol. 59, nos. 1–2, pp. 121–136. doi 10.1016/j.coal.2003.12.013CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  1. 1.Le Quy Don Technical UniversityHanoiVietnam
  2. 2.Moscow State University of Geodesy and CartographyMoscowRussia

Personalised recommendations