Abstract
The urban heat island (UHI) phenomenon caused by rapid urbanization has become an important global ecological and environmental problem that cannot be ignored. In this study, the UHI effect was quantified using Landsat 8 image inversion land surface temperatures (LSTs). With the spatial scale of street units in Fuzhou City, China, using ordinary least squares (OLS) regression, geographically weighted regression (GWR) models, and multi-scale geographically weighted regression (MGWR), we explored the spatial heterogeneities of the influencing factors and LST. The results indicated that, compared with traditional OLS models, GWR improved the model fit by considering spatial heterogeneity, whereas MGWR outperformed OLS and GWR in terms of goodness of fit by considering the effects of different bandwidths on LST. Building density (BD), normalized difference impervious surface index (NDISI), and the sky view factor (SVF) were important influences on elevated LST, while building height (BH), forest land percentage (Forest_per), and waterbody percentage (Water_per) were negatively correlated with LST. In addition, built-up percentage (Built_per) and population density (Pop_Den) showed significant spatial non-stationary characteristics. These findings suggest the need to consider spatial heterogeneity in analyses of impact factors. This study can be used to provide guidance on mitigation strategies for UHIs in different regions.
Highlights
According to the seventh population census, the urbanization rate of China’s resident population will be 63.89% by 2020
The local indicators of spatial association (LISA) map shows that the H-H high land surface temperatures (LSTs) clusters were mainly located around Gao Gai Mountain in the southern part of the industrial area, with a smaller number of scattered clusters in the central and northern parts of the city (Figure 2b)
This study used the ordinary least squares (OLS), geographically weighted regression (GWR), and multi-scale geographically weighted regression (MGWR) models to quantify the spatial relationships between LST and its driving factors in Fuzhou, China
Summary
According to the seventh population census, the urbanization rate of China’s resident population will be 63.89% by 2020. In the process of urbanization, land use patterns undergo dramatic changes: the original vegetation and water systems are replaced by impermeable surfaces. The urban heat island (UHI) phenomenon [1] has been shown to cause a variety of problems, including reduced biodiversity [2], deteriorating air quality [3–5], and even increased health risks and mortality [6–8]. UHIs are mainly divided into the urban canopy layer (UCL), urban boundary layer (UBL), and surface urban heat island (SUHI) [9]. With the current widespread use of remote sensing technology and the development of GIS software packages, more extensive research has been conducted regarding SUHI. Such studies monitored SUHI temperature changes by inverting land surface temperatures (LSTs) from remotely sensed data. Among the many factors that affect UHIs, the main factors that are widely studied are: (1) land cover characteristics: expanding
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