Paper
5 May 2016 Relationship between surface temperature and SAVI using Landsat data in a coal mining area in India
Varinder Saini, Manoj K. Arora, Ravi P. Gupta
Author Affiliations +
Proceedings Volume 9877, Land Surface and Cryosphere Remote Sensing III; 987711 (2016) https://doi.org/10.1117/12.2228094
Event: SPIE Asia-Pacific Remote Sensing, 2016, New Delhi, India
Abstract
The relationship between surface temperature and Soil Adjusted Vegetation Index (SAVI) associated with changing land‐use pattern due to intensive mining and mine fires as discussed in Jharia coalfield, India using data collected by the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Optical land imager (OLI) and thermal infrared sensor (TIRS) from 1991 to 2013. Jharia coalfield is under fire since the last century due to unsustainable mining activities. On visual interpretation of the surface temperature and SAVI images, it was observed that the spatial distribution of SAVI is opposite to that of LST for the whole coalfield. A subset of typical mining area known to have mines under fire was taken for further analysis. Profiles were taken along north-south and east-west directions in the subset in order to disclose variance based on the pixel values of surface temperature and SAVI images. The profiles show that peak SAVI values are in areas having dense vegetation and peak surface temperature values correspond to areas under fire. These two show an obvious negative correlation. Areas with water bodies show low temperature as well as low vegetation index values. Thus, it could be concluded that moderate resolution remote sensing data provides a convenient way to evaluate the impact of mine fires on vegetation over a period of time.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Varinder Saini, Manoj K. Arora, and Ravi P. Gupta "Relationship between surface temperature and SAVI using Landsat data in a coal mining area in India", Proc. SPIE 9877, Land Surface and Cryosphere Remote Sensing III, 987711 (5 May 2016); https://doi.org/10.1117/12.2228094
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Cited by 5 scholarly publications.
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KEYWORDS
Vegetation

Mining

Earth observing sensors

Landsat

Remote sensing

Reflectivity

Near infrared

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