Impacts of urban biophysical composition on land surface temperature in urban heat island clusters

Abstract

The spatio-temporal pattern of biophysical composition significantly affects land surface temperature (LST). Previous studies, however, mostly characterized urban heat island (UHI) clusters being spatially homogeneous. The landscape spatial heterogeneity in urban across UHI clusters challenges us to more accurately characterize the relationships between LST and corresponding urban biophysical composition. In this study, we introduced an innovative integrated approach that combined object-oriented image segmentation with local indicators of spatial autocorrelations (LISA) to extract UHI clusters from an LST image. We used a regression tree model to examine the nonlinear relationships between LST and each of three satellite-based indices within the UHI clusters: normalized differential vegetation index (NDVI), normalized differential build-up index (NDBI), and normalized difference bareness index (NDBaI). We found that both NDVI and NDBI are strongly correlated with the variations of LST whereas NDBaI has a weaker correlation with LST. We also found that the regression tree model built in this study enabled us to effectively detect the nonlinear relationship between LST and biophysical composition. Furthermore, based on a set of rules derived from a regression tree analysis, we found that urban landscapes strongly affect LST and its spatial heterogeneity within a UHI. These rules were used to detect the nonlinear impacts of complex urban biophysical composition on LST. The results of this study provided insights into how LST within UHI varies with urban surface characteristics at fine spatial scale and also a new method for investigating effects of land surface composition on LST in urbanized areas.