Abstract

Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the strength of the thermal intensity of the surface of urban heat island (SUHI) and to see how hot the surface of the Earth would be in a particular location. In this respect, the most developed urban city like Dhaka Metropolitan Area (DMA), Bangladesh is considered for estimation of LST, and Normalized Difference Vegetation Index (NDVI) changes trend in more developed and growing developing areas. The focus of this study is to find out the critical hotspot zones for further instantaneous analysis between these two types of areas. The trends of long-term spatial and temporal LST and NDVI are estimated applying Landsat images-Landsat 5-TM and Landsat OLI_TIRS-8 for the period of 1988 to 2018 for DMA and for developed and growing developing areas during the summer season like for the month of March. The supervised classification was used to estimate the land cover categories and to generate the LST trends maps of the different percentiles of LSTs over time using the emissivity and effective at sensor brightness temperature. The study found the change in land cover patterns by different LST groups based on 50th, 75th, and 90th percentile where the maximum LST for the whole DMA went up by 2.48°C, 1.01°C, and 3.76°C for the months of March, April, and May, respectively for the period of 1988 to 2018. The highest difference in LST was found for the most recently developed area. The moderate change of LST increased in the built-up areas where LST was found more sensitive to climate change than the growing developed areas. The vegetation coverage area decreased by 6.74% in the growing, developing areas compared to the developed areas from 1988 to 2018. The findings of the study might be helpful for urban planners and researchers to take up appropriate measures to mitigate the thermal effect on urban environment.

Full Text
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