Abstract
Landsat data (L8, L7 and L5) of ten Indian cities were selected for spatial estimation of land surface temperature (LST) to understand the urban heat island (UHI) phenomenon. LST was estimated using two methods (a) the spectral radiance model; and (b) an automatic mapping algorithm developed in the ERDAS Imagine platform. LST values were normalized between 0 and 1 using an UHIindex for understanding the UHI scenario of cities during 1995–2017. The estimated average UHIindex values of nine cities were > 0.5, with the highest being 0.89, which forced in identifying the parameter influencing the tremendous rise in LST. To study this behavior, the role of different land cover changes (built-up, water bodies, or barren land) was considered and satellite-derived indices were adopted for classification instead of supervised classification looking at medium/low resolution freely available spatial data. The decision of which index mostly affected LST was solved by assigning a grade to each index generated from using the grey relational approach. With a highest grade of 5.8 and other two being 4.8 and 4.7, the built-up satellite index suggested that human activities severely altered the surface temperature in the cities. A realistic and practical "Living index (LI)" approach was implemented for ranking the cities in the study area based on the numeric of built-up index and UHIindex values encountered in day-to-day life.
Highlights
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Land surface temperature (LST) and remote sensing indices (NDBI, MNDWI, and NDBaI) were mapped using Landsat data.
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On normalizing LST using an UHIindex, it was observed that the estimated average values of nine cities were > 0.5 and with highest being 0.89.
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Grey Relational Analysis (GRA) was adopted for assigning weights to indices that influenced rise in LST. NDBI with a highest weight of 5.8 influenced LST the most.
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A Living index (LI) approach ranked the ten cities based on their estimated NDBI and UHIindex values.
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The authors would like to acknowledge Science and Engineering Research Board (SERB), India, for providing the financial support for this research program vide project no. ECR/2016/000057.
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Sovan Sankalp: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Roles/Writing - original draft. Sanat Nalini Sahoo: Visualization, Writing - review and editing.
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Sankalp, S., Sahoo, S.N. Grey Relational Modelling of Land Surface Temperature (LST) for Ranking Indian Urban Cities. Environ. Process. 9, 32 (2022). https://doi.org/10.1007/s40710-022-00588-6
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DOI: https://doi.org/10.1007/s40710-022-00588-6