Multispectral-TIR Data Analysis by Split Window Algorithm for Coal Fire Detection and Monitoring
Journal Title: International Journal of Humanities and Social Science Invention - Year 2017, Vol 6, Issue 5
Abstract
Coal fires are common and serious phenomena in most coal producing countries in the world. Coal fires not only burn valuable non-renewable coal reserves and severely affect the environment, but also sometimes cause loss of life and property. Thus, it is very important to monitor coal fire. Remote sensing provides a useful technique for investigating environmental degradation in general and detecting and monitoring of coal fires in particular. Jharia coal field is an exclusive store house of prime coking coal in the country. In this study, an attempt has been made to retrieve land surface temperature (LST) over Jharia coal field from ASTER multi-spectral TIR data sets acquired in 2006 and 2009 by split window algorithm. Land surface temperature (LST) is an important geophysical parameter used for precisely detecting thermal anomalous pixels. The water vapor content of the atmosphere and atmospheric transmittance in TIR region was determined from MODIS data. Thermal emissivity of the land surface was determined by a hybrid technique adopting NDVI based model and knowledge based approach. LST was determined by split window algorithm using ASTER TIR channels 13 and 14 where atmospheric transmittance and thermal emissivity of the land surface were used as essential inputs. Finally, histogram based separation of thermally anomalous pixels facilitates for detection and monitoring of coal fire affected areas. It is observed that the coal fire affected area has been increased substantially during the observation period (2006-2009).
Authors and Affiliations
Bushra Praveen, Deepmala Gupta
Keywords
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