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.
Highlights
Urbanization represents the most dramatic human alteration of the land surface, typically resulting in the formation of urban heat islands (UHI) [1]
Overall building wall variables explained 4.7% out of 41.2% total explained variance for Model 1 (January day), 5.5% out of 55.2% total explained variance for Model 2 (January night), 4.8% out of 45.3% explained variance in Model 3 (August day) and 7.2% out of 47.2% total explained variance for Model 4 (August night). These results show that wall types have a stronger predictive power in the nighttime models of the total variation of land surface temperature (LST), and demonstrate that these variables are much weaker at explaining the variation of LST compared to impervious surface fraction (ISF), Dist2Water, and albedo in general
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Summary
Urbanization represents the most dramatic human alteration of the land surface, typically resulting in the formation of urban heat islands (UHI) [1]. UHI refers to the higher atmospheric and surface temperature in urban areas compared to surrounding areas with more vegetation. There are two categories of UHI: atmospheric UHI and surface UHI (SUHI) [3]. They are based on how temperature is measured, e.g., air temperature versus land surface temperature (LST). Researchers studied the UHI phenomena using air temperature data measured by thermometers at weather stations or on automobiles [4,5,6]. With the advancement of remote sensing technology, a wide range of moderate and high spatial resolution thermal infrared images have been employed to study UHI and LST, such as Landsat ETM+ (Enhanced Thematic Mapper+) with 30 m resolution, ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) with 90 m resolution, and MODIS (Moderate Resolution Imaging Spectroradiometer) with 1 km resolution [10,11,12,13,14,15,16,17,18,19,20]
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