Spatial Pattern Impact of Impervious Surface Density on Urban Heat Island Effect: A Case Study in Xuzhou, China

Impervious surfaces (IPS) are the major source of urban heat island effect (UHI), and the relationships between IPS and land surface temperature (LST) have been widely studied. However, the spatial impact of landscape patterns of patches with different IPS density (IPSD) on the thermal environment remains largely unexplored. Based on three Landsat 8 images of the Xuzhou built-up area obtained in May and the corresponding ground observations from 2014 to 2020, the IPSD and LST maps were inversed through a linear spectral mixture analysis and mono-window algorithm, respectively. The landscape patterns of the five IPSD levels were characterized by four landscape-level and five class-level metrics. Finally, the spatial correlation between all landscape metrics and LST were analyzed using bivariate Moran’s I. The results were as follows: (1) The findings revealed that for the landscape-level metrics, LST had significant positive spatial correlations with Shannon’s diversity index (SHDI), Shannon’s evenness index (SHEI), and patch density (PD), while showing a significant negative correlation with contagion index (CONTAG), indicating that increasing the types, even distribution degree, and density of patches, or decreasing the aggregation degree of the five IPSD levels will lead to the enhancement of the thermal environment. (2) Furthermore, the class-level metrics of each IPSD level, percentage of landscape (PLAND), largest patch index (LPI), landscape shape index (LSI), aggregation index (AI), and patch cohesion index (COHESION) showed significant correlations and LST, which signified that the spatial characteristics of patch proportion, predominance degree, shape complexity, aggregation degree, and natural connectivity degree of each IPSD level are important factors affecting UHI. In addition, the spatial correlations between LST and class-level metrics were significantly positive for IPSD levels 4 and 5 with an evidently higher Moran’s I value, indicating that landscape patterns of IPSD levels 4 and 5 were the key factors in UHI enhancement. Furthermore, the impact weights of each class-level metric of IPSD levels 4 and 5 on LST were also analyzed by applying the principal component analysis and the multivariate regression standardization coefficient. These results reveal the importance and impact mechanism of the IPSD spatial patterns on UHI evolution, which may provide a valuable reference for future urban planning and climate management. This study also suggests that regional UHI can be mitigated by reducing the area proportion, natural connectivity, and shape complexity of high-density impervious surfaces.