Journal
SUSTAINABILITY
Volume 14, Issue 5, Pages -Publisher
MDPI
DOI: 10.3390/su14052777
Keywords
surface urban heat island (SUHI); remote sensing; GIS; satellite data; LST; EVI; socioeconomic factors; demographic factors; SUHI drivers; urban greenness; Landsat
Funding
- research project: Climate Change and Heatwave Project: Identification of high-risk areas across the City of Greater Geelong (2021)
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This study investigates the spatial patterns of urban heat island (UHI) and its relationships with socioeconomic, demographic, and building factors. Results indicate that these factors significantly contribute to UHI generation.
Despite implementing adaptation strategies and measures to make cities sustainable and resilient, the urban heat island (UHI) has been increasing risks to human health and the urban environment by causing hot spots in city areas. This study investigates the spatial patterns in the surface urban heat island (SUHI) over the study site and develops its relationships to socioeconomic, demographic, and buildings' characteristics. This paper examines the role of building roof types, building roof material, building height, building age, and socioeconomic and demographic factors in driving the SUHI in a city. Numerous studies have focused primarily on the influence of biophysical and meteorological factors on variations in land surface temperatures (LSTs); however, very little attention has been paid to examining the influence of socioeconomic, demographic, and building factors on SUHIs within a city. The analysis has been carried out by processing Landsat based LST data to UHI in the Google Earth Engine (GEE) cloud-based platform. The satellite-based research is further integrated with GIS data acquired from the state government and local city council. Linear regression and multiple regression correlations are further run to examine selected factors' variance on SUHI. Results indicate socioeconomic, demographic, and building factors contribute significantly to SUHI generation; these factors collectively can explain 28% of the variance in SUHI patterns with significant p-values.
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