Abstract
Land surface temperature (LST) is a key factor in numerous areas such as climate change, land use/land cover in the urban areas, and heat balance and is also a significant participant in the creation of climate models. Landsat data has given numerous possibilities to understand the land processes by means of remote sensing. The present study has been performed to identify the LST of the study region using Landsat 8 OLI/TIRS satellite images for two time periods in order to compare the data. The study also attempted to identify and predict the role and importance of NDVI, NDBI, and the slope of the region on LST. The study concludes that the maximum and minimum temperatures of 40.44 C and 20.78 C were recorded during the November month whereas the maximum and minimum LST for month March has increased to 42.44 C and 24.57 C respectively. The result indicates that LST is inversely proportional to NDVI (−6.369) and slope (−0.077) whereas LST is directly proportional to NDBI (+14.74). Multiple linear regression model has been applied to calculate the extents of NDVI, NDBI, and slope on the LST. It concludes that the increase in vegetation and slope would result in slight decrease in temperature whereas the increase in built-up will result in a huge increase in temperature.
Highlights
Land surface temperature (LST) is an indispensable factor in the physics of land surface processes: it plays the most significant role in the transfer of energy and water from the ground to the atmosphere [1]
For the purpose of extraction of LST can be done with the help of remote sensing which has ample range of sensors, like Landsat 4 and 5 (TM), 7 (ETM+), 8 (TIRS 1 and 2), Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Spaceborne ermal Emission and Reflection (ASTER), and Advanced Very High Resolution Radiometer (AVHRR) [14], and the study confirms that the remote sensing provides accurate temperature value than ground station [15]
For finding the land surface temperature (LST) of the study region, Landsat 8 Operational Land Imagers (OLI) and ermal Infrared Sensor (TIRS) images with 143 paths and 51 rows have been downloaded from USGS (United States Geological survey) [25] which provides 11 bands with different wavelengths
Summary
Land surface temperature (LST) is an indispensable factor in the physics of land surface processes: it plays the most significant role in the transfer of energy and water from the ground to the atmosphere [1]. A detailed study of the spatial and temporal changes of LST is essential to different research fields which include surface energy budgeting [3, 4], urban climate, vegetation [5, 6], and hydrology [7, 8]. For finding the land surface temperature (LST) of the study region, Landsat 8 Operational Land Imagers (OLI) and ermal Infrared Sensor (TIRS) images with 143 paths and 51 rows have been downloaded from USGS (United States Geological survey) [25] which provides 11 bands with different wavelengths. The study area of the Vellore region and its corresponding satellite images are obtained for the data processing and analysis. E following algorithm has been utilized to retrieve the land surface temperature of the study region. 12°12′0′′N 12°26′0′′N 12°40′0′′N 12°54′0′′N 13°8′0′′N 13°22′0′′N e Scientific World Journal
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