Factors Affecting the High-Intensity Cooling Distance of Urban Green Spaces: A Case Study of Xi’an, China

Abstract

Urban green space has a cooling effect and its cooling effect can extend to the surrounding environment, conspicuously decreasing with distance. Land surface temperature (LST) and cooling distance are generally researched based on remote sensing and temperature inversion algorithms; this distance is affected by internal and external environment factors, but the high-intensity cooling distance (HCD) is overlooked by using large scale datasets. In addition, the comprehensive relationship between internal and external factors with cooling distance and HCD is still unclear. The aim of this study is to identify the HCD of green spaces by monitoring the changes of LST away from it and to quantify the influences of 12 internal and external factors on HCD. A multiple linear regression model is used to analyze the relationship between them. In the summer of 2022, we measured and calculated HCD for 59 urban green spaces in Xi’an, China. The analysis results show that the HCD is not only affected by the internal landscape factors of green spaces, but also closely related to factors associated with the natural environmental, urban attributes, and surrounding structures. These findings can rationally assist the planning of the allocation of urban green spaces and provide a scientific basis for mitigating the urban heat island effect.