Abstract

Land Surface Temperature (LST) is an important phenomenon in global climate change. As the green house gases in the atmosphere increases, the LST will also increase. This will result in melting of glaciers and ice sheets and affects the vegetation of that area. Its impact will be more in the monsoon areas, because the rainfall is unpredictable, failure of monsoon and there will be heavy down pour of rainfall. LST can be estimated through many algorithms viz., Split-Window (SW), Dual-Angle (DA), Single-Channel (SC), Sobrino and Mao. With the advent of satellite images and digital image processing software, now it is possible to calculate LST. In this study, LST for Dindigul District, Tamil Nadu, India, was derived using SW algorithm with the use of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution. SW algorithm needs spectral radiance and emissivity of two TIR bands as input for deriving LST. The spectral radiance was estimated using TIR bands 10 and 11. Emissivity was derived with the help of NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were used. The output revealed that LST was high in the barren regions whereas it was low in the hilly regions because of vegetative cover. As the SW algorithm uses both the TIR bands (10 and 11) and OLI bands 2, 3, 4 and 5, the LST generated using them were more reliable and accurate.

Full Text
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