The Spatial Variability of Urban Land Surface Temperature

Abstract

Urban land surface temperatures (LST) are often correlated with thematic land use and land cover (LULC) data in order to understand its spatial variability. This study aimed to analyze the spatial pattern of LST in the City of Indianapolis, the United States by using sub-pixel biophysical variables extracted from a spectral mixture analysis (SMA). A Landsat Enhanced Thematic Mapper Plus image of the study area, acquired on June 22, 2002, was spectrally unmixed into four fraction endmembers, namely green vegetation, soil, high and low albedo. Impervious surface was then computed based on the high and low albedo images. A hybrid classification procedure was developed to classify the fraction images into seven land use and land cover classes. Next, pixel-based LST measurements were related to urban surface biophysical descriptors. Correlation analyses were conducted to investigate land cover based relationships between LST and impervious surface and green vegetation fractions for an analysis of the causes of LST variations. Results indicate that fraction images derived from SMA were effective for quantifying the urban morphology and for providing reliable measurements of biophysical variables such as vegetation abundance, soil, and impervious surface. An examination of LST variations within census block groups and their relationships with the compositions of LULC types, biophysical descriptors, and other relevant spatial data shows that LST possessed a weaker relation with the LULC compositions than with other variables (including urban biophysical descriptors, remote sensing biophysical variables, GIS-based impervious surface variables, and population density).