Estimating the seasonal relationship between land surface temperature and normalized difference bareness index using Landsat data series

Year 2022, Volume: 7 Issue: 1, 9 - 16, 15.02.2022

Subhanil Guha , Himanshu Govil

https://doi.org/10.26833/ijeg.833260

Cited By: 7

Abstract

The present study analyzes the seasonal variability of the relationship between the land surface temperature (LST) and normalized difference bareness index (NDBaI) on different land use/land cover (LULC) in Raipur City, India by using sixty-five Landsat images of four seasons (pre-monsoon, monsoon, post-monsoon, and winter) of 1991-1992, 1995-1996, 1999-2000, 2004-2005, 2009-2010, 2014-2015, and 2018-2019. The mono-window algorithm was used to retrieve LST and Pearson's correlation coefficient was used to generate the LST-NDBaI relationship. The post-monsoon season builds the best correlation (0.59) among the four seasons. The water bodies builds a moderate to strong positive correlation (>0.50) in all the four seasons. On green vegetation, this correlation is moderate to strong positive (>0.54) in the three seasons, except the pre-monsoon season. The built-up area and bare land generate a moderate positive correlation (>0.34) in all the four seasons. Among the four seasons, the post-monsoon season builds the best correlation for all LULC types, whereas the pre-monsoon season has the least correlation. This research work is useful for environmental planning of other citieswith similar climatic conditions.

Keywords

Landsat, LST, LULC, NDBal, Raipur

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