Coal Seam Fire Area Determination Using Pixel Values of the Satellite Data
A large amount of coal is locked up in coal seams worldwide, every year, due to uncontrolled coal seam fires (CSF). China and India are most affected due to these fires. Mapping an quantification of these fires requires the spectral characterisation of land use and land cover (LULC) from the satellite imagery. Remote sensing (RS) and Geographic information system (GIS) are useful tools for the assessment of the underground CSF. This paper reports the experiences obtained from the experiments on spectral band of satellite imageries in delineating the thermal regimes of CSF in a coalfield in India. The steps involved in the coal fire data analysis are preprocessing, processing and post processing. The data analyses have been carried out on the satellite data. The changes in LULC have been detected by visual interpretation, image differencing, band rationing and level slicing. The field observations were incorporated in analysis to identify the scope for further improvement in LULC simulations for the reliable modelling, delineation, mapping and monitoring of the CSF. The results obtained from the study depict that shallow depth workings, contiguous panel multi seam workings and the thick seam mining had created directly or indirectly very complex situations in thermal regimes of CSF. The province ratification of the CSF surveillance by LULC depicts that the propagation of the fire is higher in the lateral direction i:e in perpendicular direction of the fire heading.
Chatterjee, R. S., Bannerjee, D., Roy, J. and Bhattacharya, A. K. (1994): “Landsat TM data processing techniques for identifying and delineating environmental impacts of coal mining.” IT C Journal, 1992, 155-162.
Sabins, F. F. (1998): Remote Sensing Principles and Interpretation, (2nd edn.), (San Francisco: W.H. Freeman).
Prakash, A., Gupta, R. P. and Saraf, A. K. (1997): “A Landsat TM based comparative study of surface and subsurface fires in the Jharia Coalfield, India.” International Journal of Remote Sensing, 18 (11), 2463-2469.
Liu, Xin and Yetik, I. S. (2010): A maximum likelihood classification method for
a. Image segmentation considering subject variability Medical Imaging Research Center,
b. Illinois Institute of Technology, Chicago, IL, USA.
Reddy, C. S. S., Srivastava, S. K. and Bhattacharya, A. (1993): “Application of Thematic Mapper short wavelength infra red data for the detection and monitoring of high temperature related geo environment features,” International Journal of Remote Sensing, 14 (17): 3125 - 31.
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