Influence of urban landscape pattern on summer surface temperature and its spatial scale effects

Urban planning is pivotal in understanding how urban landscapes influence land surface temperature (LST) across various spatial scales. Using Landsat 8 OLI_TIRS images, we studied the spatial heterogeneity of LST in Hefei city of Anhui province, China. The results show that landscape pattern index (LSPI) can explain the spatial distribution of LST, remote sensing indices such as modified normalized difference water index (MNDWI) and normalized difference vegetation index (NDVI), play dominant roles in LST, and the explanatory power is stronger at small scales. As the spatial scale decreased, the explanatory power of NDVI and Built-land proportion on LST increases. The combined explanatory power of water and woodland on LST is more significant than the individual effect. When the proportion of Built-up land exceeds 60%, increasing the proportion of water and woodland can reduce the LST. This study provides insights to mitigate urban heat island (UHI) effect in urban planning.