Estimation of land surface temperature in response to land use/land cover transformation in Kolkata city and its suburban area, India

Abstract

ABSTRACT The land transformation in Kolkata city and its suburban area is mainly due to intensive population pressure and rapid urban sprawling. Consequently, the land surface temperature (LST) is continuously increasing and gradually intensifying the effects of the urban heat island. The aim of this study is to assess the spatiotemporal variation of LST in response to land use land cover change (LULC) during 1995–2020. The maximum likelihood classifier was used for the supervised classification of LULC and the accuracy assessment was done using the confusion matrix. Quin’s Mono-window algorithms for Landsat TM data of 1995 and 2010 and split-window algorithms for Landsat 8 OLI data of 2020 were applied to retrieve LST. Several spectral indices such as Normalized difference built-up index (NDBI), Normalized difference vegetation index (NDVI), and Modified normalized difference water index (MNDWI) were calibrated and pixel-specific overlay analysis was done for correlation study between spectral indices and LST. This work revealed that the rapid urban sprawling causes massive land transformation in the study area. The land conversions from trees outside forests (TOF) and agricultural land to built-up were significantly contributing to an overall increase in the mean LST during 1995–2020. The mean LST was comparatively high over Kolkata city than its suburban area. During 1995–2020, the mean LST was increased by nearly 8.43°C in the summer season and 4.32°C in the winter season. The increasing rate of LST was found relatively high over the built-up (7.06°C), agricultural land without crop (5.55°C), and open land (5.54°C). However, it was comparatively low over TOF (4.66°C) and water bodies (3.68°C). The LST was positively correlated to NDBI and negatively correlated to NDVI and MNDWI. In order to combat urban warming, this study will promote green city initiatives through sustainable land use planning.