Abstract
The urban heat island (UHI) phenomenon occurring in the urban areas or city-clusters is increasingly becoming a severe problem in the urbanization process. Previous research mainly rely on grid analysis techniques to study temperature data from images recorded at fixed time instants. The evolutionary process of UHI in both time and space has not been investigated yet. This research designs an object-oriented spatiotemporal model to reconstruct the evolution of UHI and provide a qualitative interpretation. Each UHI is modeled as a spatiotemporal field object with it own life cycle. Dynamic behavior of an UHI is defined by sequences of spatial changes (e.g. contraction or expansion) and topological transformations (e.g. merge or split). The model is implemented in an object-relational database and applied to air temperature data collected from weather stations every hour over three days. UHIs with their behavior were extracted from the data. Results suggest that the model can effectively track and provide a qualitative description of the UHI evolution.
Highlights
Urban heat island is a phenomenon where temperature in urban areas is obviously higher than in surrounding rural areas
With the increasing rate of urbanization process, many rural areas have gradually become urbanized areas, small and middle-sized cities expand to metropolises, and mega cities grow to spatial contiguous city clusters, which cause the urban heat island (UHI) occurring in the cities or city clusters to have a measurable influence on the weather and even regional climate evolution
The threshold temperature is the average value of three sample points located in the rural area closely to the urban area (Figure 5)
Summary
Urban heat island is a phenomenon where temperature in urban areas is obviously higher than in surrounding rural areas. Much work has been done to study the causative factors and adverse effects of UHI from thermal intensity images derived from satellite images and from meteorological station records. Work in this direction consisted mainly in correlating thermal intensity from static surface temperature images with environmental (Lo et al 1997, Dousset and Gourmelon 2003, Stathopoulou and Cartalis 2006) or social indicators (Buyantuyev and Wu 2010). (Keramitsoglou et al 2011) proposed an object-based image analysis to reveal thermal pattern that thermal intensities of hotspots are strongly correlated to their extent
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