Seasonal and diurnal characteristics of land surface temperature and major explanatory factors in Harris County, Texas

Abstract

The effects of biophysical and meteorological factors on land surface temperature (LST) have been well studied in previous research. However, less attention has been paid to examine how building materials influence the magnitude of LST within an urban environment. This study investigates the interaction of biophysical and building wall materials to influence LST in Harris County, Texas, USA using multiple stepwise linear regression analyses and neighborhood analysis. Working at 1 km grid resolution, LST data is related to impervious surface fraction, albedo, distance to water bodies, and seven major wall types. Ten years of aggregated MODIS (Moderate Resolution Imaging Spectroradiometer) daily LST products were used to calculate the mean LST in January and August for daytime and nighttime conditions. Harris County 2010 parcel level building property data were used to create composition characteristics of the building wall types. Our results demonstrate that both biophysical and building wall characteristics significantly influence the spatiotemporal variations of LST. However, biophysical factors are the dominant explaining factors compared to building wall materials. Impervious surface fraction is the most significant variable to explain the variation of LST, and has positive effects on LST. In contrast, high albedo materials and the presence of open water bodies significantly affect LST and are good candidate variables to mitigate the heat island effect. Furthermore, the building wall variables all increase LST for both daytime and nighttime, but different wall materials have various effects on LST. Brick/veneer and frame/concrete block are the two dominant wall types in Harris County and tend to generate higher LST. These results demonstrate how building materials, in combination with biophysical factors, can be used to mitigate neighborhood-scale LST. This methodology works reasonably well for Houston, but is likely to be more effective in higher density urban settings.