Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with N
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ORIGINAL PAPER
Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India Shahfahad 1 & Babita Kumari 1,2 & Mohammad Tayyab 1,3 & Ishita Afreen Ahmed 1 & Mirza Razi Imam Baig 1 & Mohammad Firoz Khan 1 & Atiqur Rahman 1 Received: 21 November 2019 / Accepted: 23 September 2020 # Saudi Society for Geosciences 2020
Abstract This study was designed to compare the pattern of land surface temperature (LST) over four metro cities of India (Mumbai, Chennai, Delhi, and Kolkata) selected on a longitudinal basis in relation to the built-up and vegetation indices. Two different methods were employed for the retrieval of LST, i.e., mono-window algorithm (MWA) and split-window algorithm (SWA) on the Landsat 8 (OLI/TIRS) datasets, to analyze the spatial pattern of LST over selected cities in relation to normalized differential built-up index (NDBI) and normalized differential vegetation index (NDVI). The result shows that the LST was high over the densely built areas while low over the densely vegetated areas. The highest LST, NDBI, and NDVI were found in Mumbai, while Kolkata records the lowest LST and NDVI. Furthermore, the spatial analysis of LST shows that the LST was high in central parts of all cities except in the case of Delhi where some peripheral areas also record high LST. The comparison from in situ LST (field observations) reveals that the SWA has higher accuracy in the retrieval of LST in maritime areas like Mumbai and Chennai because it reduces the atmospheric effects, while the MWA has higher accuracy for inland areas like Delhi. The spatial relationships of LST with NDVI and NDBI show that vegetation cover has more impact on LST in Delhi while low in Chennai and Mumbai, and the built-up surfaces have a higher impact on LST in Chennai and Mumbai than Kolkata and Delhi. Keywords Land surface temperature (LST) . Mono- and split-window algorithms . NDVI . NDBI . Longitudinal analysis . Metro cities—India
Introduction Urbanization is one of the most considerable human activities since the nineteenth century, and about 54% of the world’s population was living in urban areas in 2014 (United Nations 2014). During the last three decades, the urban population has
rapidly increased in India, which led to a fast expansion of the urban areas (Chettry and Surawar 2020; Mandal et al. 2019). One of the most important changes due to urban expansion is the conversion of natural pervious land surfaces into artificial built-up surfaces (Lu et al. 2008). This reduces the surface albedo and significantly changes thermal conductivity and
Responsible Editor: Biswajeet Pradhan * Atiqur Rahman [email protected]
Mirza Razi Imam Baig [email protected]
Shahfahad [email protected]
Mohammad Firoz Khan [email protected]
Babita Kumari [email protected]
1
Mohammad Tayyab [email protected]
Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, India
2
Ishita Afreen Ahmed
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