An Investigation to Identify the Effectiveness of Socioeconomic, Demographic, and Buildings’ Characteristics on Surface Urban Heat Island Patterns

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

Spatio-temporal mapping and monitoring of Urban Heat Island patterns over Sydney, Australia using MODIS and Landsat-8

© 2016 IEEE. Most cities have become net sources of heat with well-documented examples of anthropogenic climate modification in urban areas driving the Urban Heat Island (UHI) effect. This is defined as having higher temperatures (air /surface) in the built environment of cities compared with the surrounding and in rural areas. In this study we integrated remotely sensed satellite data to map and monitor the UHI effect over the Sydney region in Australia. Terra/Aqua MODIS Land Surface Temperature (LST) time series data for 2003 to 2015 were analysed to determine the spatio-temporal dynamics of UHI intensity. Land cover data from the MODIS were used to delineate the urban, rural and water class for the Sydney region. The UHI intensities were extracted from LST images by normalising rural LST patterns for each date. A Gaussian approximation was then applied in order to quantify spatial extent, centre and magnitude of UHI intensities. The temporal analysis on seasonal and interannual variations of UHI, revealed maximum intensities in daytime periods, particularly during the summer season. The daytime UHI intensity in Sydney could be as large as 7 - 8 °C in summer days. However, relatively weak UHI intensities were observed in night-time periods during all seasons. It was observed from the time series data that there were slight non-significant increasing trend in daytime UHI magnitudes for Sydney. However, pixel based UHI intensities at dense urban suburbs showed significant increasing trends for daytime and no defined trend for night time observations. To better characterise the locational nature of UHI, Landsat-8 land surface temperature data were analysed for summer period. Landsat data were helpful in extracting the information on hot spots in urban area more precisely. Satellite data of MODIS and Landsat provided efficient results for monitoring, mapping and characterizing UHI patterns over time and space. Future work involves classification of urban areas using thermal and vegetation index combined analyses in order to better understand the drivers of the UHI