Quantifying land surface temperature change from LISA clusters: An alternative approach to identifying urban land use transformation
Despite the potential to provide new insight into the underlying heterogeneity of urban thermal landscapes, local indicators of spatial autocorrelation (LISA) remains underutilized in land surface temperature (LST) related study. The present research applies local Moran's I as a unique approach to recognize the pattern of statistically significant LST increase by detecting clusters of localized hot spots. The single channel algorithm has been applied to extract LST from nine temporal Landsat datasets ranging from 2001 to 2013.