Driving Mechanism of Differentiation in Urban Thermal Environment during Rapid Urbanization

Abstract

To achieve sustainable urban development, it is essential to gain insight into the spatial and temporal differentiation characteristics and the driving mechanisms of the urban thermal environment (UTE). As urbanization continues to accelerate, human activity and landscape configuration and composition interact to complicate the UTE. However, the differences in UTE-driven mechanisms at different stages of urbanization remain unclear. In this study, the UTE of Shenyang was measured quantitatively by using the land surface temperature (LST). The spatial and temporal differentiation characteristics were chronologically studied using the standard deviation ellipse (SDE) and hotspot analysis (Getis–Ord Gi*). Then, the relationship between human activities, landscape composition and landscape configuration and LST was explored in a hierarchical manner by applying the geographical detector. The results show that the UTE in Shenyang continues to deteriorate with rapid urbanization, with significant spatial and temporal differentiation characteristics. The class-level landscape configuration is more important than that at the landscape level when studying UTE-driven mechanisms. At the class level, the increased area and abundance of cropland can effectively reduce LST, while those of impervious surfaces can increase LST. At the landscape level, LST is mainly influenced by landscape composition and human activities. Due to rapid urbanization, the nonlinear relationship between most drivers and LST shifts to near-linear. In the later stage of urbanization, more attention needs to be paid to the effect of the interaction of drivers on LST. At the class level, the interaction between landscape configuration indices for impervious surfaces, cropland and water significantly influenced LST. At the landscape level, the interactions among the normalized difference building index (NDBI) and other selected factors are significant. The findings of this study can contribute to the development of urban planning strategies to optimize the UTE for different stages of urbanization.

Spatio-Temporal Differentiation Characteristics and Driving Factors of Urban Thermal Environment: A Case Study in Shaanxi Province, China

“…Second, artificial construction activities in Shaanxi Province are closely related to the spatio-temporal variation in AAT (Figure 4), which may increase the intensity of UTE [18,41], resulting in the deterioration of the UTE in different areas between 1970 and 2017, including the Southern, Central, and Northern regions. This is consistent with previous studies conducted in Shaanxi Province [28,33] which found that significant changes in the UTE have been observed in the northwest and southeast of Shaanxi Province. Due to having imperious surfaces which absorb solar radiation and releasing long-wave radiation [28], which directly warms the air, vegetation reduces solar radiation.…”

“…This is consistent with previous studies conducted in Shaanxi Province [28,33] which found that significant changes in the UTE have been observed in the northwest and southeast of Shaanxi Province. Due to having imperious surfaces which absorb solar radiation and releasing long-wave radiation [28], which directly warms the air, vegetation reduces solar radiation.…”

“…The UTE is affected by different natural and socio-economic factors, for example, wind speed, humidity, and solar radiation [28]. The increases in population and anthropogenic activities in urban areas have also changed the UTE distribution pattern [29].…”

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

“…The socioeconomic factor typically refers to human activities and industrial production and can be quantified by population density (PD) and industrial added value (IAV) [25][26][27]. The increase in PD and IAV tends to be accompanied by elevated heat emissions, thereby resulting in a rise in LST [28,29].…”

“…In terms of research methodology, the previous methods employed include statistical analysis, ordinary least squares regression (OLSR), and machine learning methods, such as neural network models and random forests [25,37,38]. However, statistical analysis and OLSR are inadequate in capturing the nonlinear relationship between LST and IFs.…”