Assessing the quantitative relationships between the impervious surface area and surface heat island effect during urban expansion

Abstract

As an important component of underlying urban surfaces, the distribution pattern and density of the impervious surface area (ISA) play an important role in the generation of surface urban heat island (SUHI) effects. However, the quantitative and localized exploration of the ISA’s influence on SUHIs in the process of urban expansion from the perspective of temporal and spatial changes is still not clear. Based on multisource remote sensing data, the SUHI effect of urban expansion is revealed by using geospatial analysis methods such as profile, difference and regression analysis. The results show the following: (1) urban expansion plays a significant role in aggravating SUHIs. Overall, the ISA and land surface temperature (LST) have obvious consistency in terms of spatial distribution patterns. However, local spatial differentiation is significant. The areas with the highest LST were not concentrated in the downtown area with the highest ISA but were scattered in the cultivated land and exposed surface areas under development in the northern part of the city. (2) In general, the ISA can explain the spatial distribution of LST well, there is an obvious positive correlation between them, and the quadratic polynomial function is the best fitting model between them. (3) The density and spatial allocation of ecological elements, such as green space and water bodies, play an important role in alleviating SUHIs. This study found that the urban center with the highest ISA coverage rate has no significant SUHI due to the reasonable allocation of green space and water bodies. The research results can provide a scientific basis for future urban planning and ecological environment construction.

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

“…By comparing Tables 2 and 3, it could be seen that when considering the interactions between IFs in the summer model, the cooling impact of IPS on LST became insignificant. On the contrary, as shown in Figure 6e, IPS exhibited a significant warming effect on the land surface in the autumn model based on MGAMI, which was more consistent with the findings reported in the literature [12,63]. It was also found that the significant effect of WB disappeared in the spring model based on MGAM when considering the influence of interactions between IFs.…”

Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China

“…According to the characteristics of different thermal infrared remote sensing data, there are three main types of land surface temperature retrieval algorithms: atmospheric correction method, single-channel algorithm and split window algorithm [20,26,40]. In this paper, the atmospheric correction method suitable for Landsat satellite imagery is used to retrieve the surface temperature of the thermal infrared bands of TM and TIRS.…”

“…Based on the existing remote sensing image data, we build a 100 × 100 fishing net in ArcMap software to generate sampling points. Then extract the LULC pixel value to the center of the grid and use this value as one of the input parameters of ANN [26]. Similarly, we extracted the LST, NDBI, NDBSI, longitude and latitude of the main urban area of Wuhan in 2000, 2011 and 2020 respectively.…”

“…There are also scholars discussing the relationship between human activities and other social and economic factors of urban development and LST. Their research mainly analyzed the influence mechanism of population density, economic development status, intensity and type of human activities on LST [26,27].…”

Outdoor Microclimate in Courtyard Buildings: Impact of Building Perimeter Configuration and Tree Density

“…Beyond the expected rising temperature, many researchers agree that the lack of green areas in favour of large impervious surfaces that absorb more energy and cool more slowly is one of the main reasons that heat stress is higher in cities [5,6], along with the fact that building elevations and density reduce wind speed (air flow) and prevent heat dispersion [7][8][9]. In addition, human activities such as heating/cooling buildings and driving cars warm the environment.…”