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Conference paper

Effect of 3D morphological distribution of cities on urban heat island formation: A GIS approach

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1Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, NG7 2RD, University Park, Nottingham, UNITED KINGDOM

2Department of Architecture and Built Environment, Faculty of Engineering, University of Nottingham, NG7 2RD, University Park, UNITED KINGDOM

Published
10 February 2020
Pages
127-137
Received
10 February 2020
DOI
10.65582/set.2019.136
Access
Open access, © 2019 Copyright WSSET
Download PDF11 pages · 1.4 MB

Abstract

The impact of the urban heat island (UHI) on the health of urban residents, energy consumption, as well as outdoor air quality has become increasingly significant in cities. Despite multiple recent studies on developing UHI predictive models, the impact of vertical morphological parameters on the UHI formation is barely investigated. The current work investigates the extent of vertical morphological components’ impact on the UHI intensity in urban areas and proposes a novel vertical urban morphological parameter defined as elevation index. Using remote-sensing satellite images, and a Geographical Information System (GIS) based approach, both horizontal and vertical morphological components of San-Francisco, as the case study city, were studied for 20 of its representative built up areas. High-resolution geometrical data of these areas were then correlated with the land surface temperature (LST) of those areas. Linear and nonlinear regression analysis revealed that the newly proposed elevation index has a significant impact on representing of the UHI behaviour with a coefficient of determination factor (R²) of 0.88. The model was then successfully validated against the UHI prediction of five new areas not being used for the training purposes. The impact of elevation morphology on UHI intensity was seen to be significant, especially at areas with a significant elevation variation.

Keywords. urban heat island; predictive tool; regression; remote-sensing; GIS

© 2019 Copyright WSSET

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