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A Spatio-Temporal Assessment and Prediction of Surface Urban Heat Island Intensity Using Multiple Linear Regression Techniques Over Ahmedabad City, Gujarat

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Abstract

The present study intends to understand the variability in land surface temperature and urban heat island over Ahmedabad city, Gujarat, from 2003 to 2018 using MODIS thermal data. The spatio-temporal variability of land surface temperature dynamics is understood by pixel to pixel linear regression analysis along with Mann–Kendall and Sen's slope estimator tests. A Gaussian fitting technique is employed to estimate the surface urban heat island (SUHI) signature with respect to the surrounding rural area. An overall increase in the surface urban heat island magnitude is profoundly visible during the winter season. The spatial extent of SUHI shows a uniform distribution of urban heat inside the city and its accumulation within a high dense central urban area in both summer and winter seasons. A multiple linear regression method is used to predict the SUHI magnitude in the next 16 years, i.e. in 2034, based on enhanced vegetation index, white sky albedo, and evapotranspiration as predicting variables considering two different scenarios presuming the present rate of change of predicting variables as first scenario and a double changing rate as compared to present rate in the second scenario.

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Acknowledgements

The authors would like to acknowledge the Department of Earth Sciences, Indian Institute of Technology, Roorkee, India, for providing necessary infrastructure facilities. The authors also acknowledge MHRD and DST NRDMS (Grant Number NRDMS/01/179/2015(C)), New Delhi, for providing the required financial support.

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  1. Department of Earth Sciences, IIT Roorkee, Roorkee, Uttarakhand, 247667, India

    Pir Mohammad & Ajanta Goswami

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Mohammad, P., Goswami, A. A Spatio-Temporal Assessment and Prediction of Surface Urban Heat Island Intensity Using Multiple Linear Regression Techniques Over Ahmedabad City, Gujarat. J Indian Soc Remote Sens 49, 1091–1108 (2021). https://doi.org/10.1007/s12524-020-01299-x

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