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.

/pdf/dominant-factors-and-spatial-heterogeneity-of-land-surface-3bhv14s5.pdf

Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China

Numerous studies have explored multiple factors that affect the urban thermal environment. Most current studies focus on establishing the holistic quantitative relationships between the characteristics of urban thermal environmental factors and land surface temperature (LST), but less attention has been given to exploring urban subdivisions in detail. The local climate zone (LCZ) system has been proven effective in establishing the quantitative relationships between urban climate and spatial factors, thereby revealing the characteristics of LST spatial variations. This study utilized the random forest regression model to analyze the relative importance and marginal effects of the urban thermal environmental factors on seasonal LSTs based on the whole study area and the LCZ-derived built type area of Fuzhou city, known as a “furnace city” in southeastern China. The results show that: (1) Building morphology and spatial distribution exert a significant impact on LST, with the LST levels of different building forms ranked as open < compact and high-rise < mid-rise < low-rise. (2) Landscape composition exhibits the greatest influence on seasonal LSTs in the whole study city. (3) Building morphology emerges as the most influential category for LST in the LCZ built type area, with the standard deviation of building height having the highest relative importance at approximately 34%. (4) Concentrated compact low-rise self-built houses experience higher temperatures and should be a focus for future urban renewal efforts. We believe that these findings provide quantitative insights for urban heat island analysis and have substantial implications for climate-friendly urban planning. 

Exploring the relationship between thermal environmental factors and land surface temperature of a “furnace city” based on local climate zones

Integrated Land Use and Urban Function Impacts on Land Surface Temperature: Implications on Urban Heat Mitigation in Berlin with Eight-Type Spaces

Investigating the effects of urban morphological factors on seasonal land surface temperature in a “Furnace city” from a block perspective

Optimizing the urban spatial form to mitigate the urban heat island (UHI) effect is considered an efficient technical approach. This study focused on district-scale UHIs in typical high-density cities in northern China. The urban spatial characteristics were extracted and quantified from multiple data sources. Utilizing remote sensing inversion and spatial analysis methods, we calculated the surface urban heat island (SUHI) intensity within each block and the values of 15 urban spatial factors. Multi-scale geographically weighted regression (MGWR) was employed to explore the impact of various factors on the SUHI and their heterogeneity in spatial relationships. Additionally, the random forest model was utilized to assess the contributions of selected factors to the SUHI. The results indicate that the building height and the ratio of permeable surfaces are the dominant factors influencing the SUHI, with contribution rates of 12.0% and 10.3%, respectively. The topographic relief has the least influence on the SUHI, and it can only explain 2.5% of variance. The local variable water area ratio exhibits a distinct cooling effect, while the industrial area ratio demonstrates a noticeable warming effect. The impact of most factors on the SUHI intensity exhibits significant spatial heterogeneity, which proves that the MGWR can better elucidate the extent of spatial variations in the coefficients of factors compared with the global model. The findings help reveal the causes of the spatial heterogeneity of the SUHI and provide a theoretical basis for addressing UHI issues. 

Influences of urban spatial factors on surface urban heat island effect and its spatial heterogeneity: A case study of Xi'an

Block-based variations in the impact of characteristics of urban functional zones on the urban heat island effect: A case study of Beijing

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Block-based variations in the impact of characteristics of urban functional zones on the urban heat island effect: A case study of Beijing