Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures

• Published in: International journal of applied earth observation and geoinformation Vol. 67; pp. 30 - 42
• Main Authors: Deilami, Kaveh, Kamruzzaman, Md, Liu, Yan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2018
• Subjects: Contributory factors; heat; heat island; issues and policy; land cover; Land surface temperature; Landsat; Modelling; population density; Satellite images; spatial data; Spatio-temporal; surface water; systematic review; temperature; Urban heat island; vegetation cover; Spatio-temporal; Modelling; Land surface temperature; Satellite images; Urban heat island; Contributory factors
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2017.12.009

•A systematic review on 75 literatures focused on spatial and temporal factors affecting the urban heat island effect.•The findings show the necessity of employing advance spatial statistical techniques in investigating urban heat island.•Highlights the need for three-dimensional study of urban heat island effect. Despite research on urban heat island (UHI) effect has increased exponentially over the last few decades, a systematic review of factors contributing to UHI effect has scarcely been reported in the literature. This paper provides a systematic and overarching review of different spatial and temporal factors affecting the UHI effect. UHI is a phenomenon when urban areas experience a higher temperature than their surrounding non-urban areas and is considered as a critical factor contributing to global warming, heat related mortalities, and unpredictable climatic changes. Therefore, there is a pressing need to identify the spatio-temporal factors that contribute to (or mitigate) the UHI effect in order to develop a thorough understanding of their causal mechanism so that these are addressed through urban planning policies. This paper systematically identified 75 eligible studies on UHI effect and reviews the nature and type of satellite images used, the techniques applied to classify land cover/use changes, the models to assess the link between spatio-temporal factors and UHI effect, and the effects of these factors on UHI. The review results show that: a) 54% of the studies used Landsat TM images for modelling the UHI effect followed by Landsat ETM (34%), and MODIS (28%); b) land cover indices (46%), followed by supervised classification (17%) were the dominant methods to derive land cover/use changes associated with UHI effect; c) ordinary least square regression is the most commonly applied method (68%) to investigate the link between different spatio-temporal factors and the UHI effect followed by comparative analysis (33%); and d) the most common factors affecting the UHI effect as reported in the reviewed studies, include vegetation cover (44%), season (33%), built-up area (28%), day/night (25%), population density (14%), water body (12%) together with others. This research discusses the findings in policy terms and provides directions for future research.