A New Analysis of the Urban Heat Island Effect
Imhoff, M.L., Zhang, P., Wolfe, R.E. and Bounoua, L. 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment 114: 504-513.
In a study of 38 of the most populous cities in the continental United States and their rural surroundings, Imhoff et al. obtained land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Earth Observing System (EOS) satellites, which they used in a spatial analysis to assess UHI skin temperature amplitude and its relationship to development intensity, size and ecological setting over three annual cycles (2003-2005), where urban impervious surface area (ISA) was obtained from the Landsat TM-based NLCD 2001 dataset. The results indicated that a city's fractional ISA was a good linear predictor of LST for all cities in the continental United States in all biomes except deserts and xeric shrublands, and that the fraction of ISA explains about 70% of the total variance in LST for all cities combined, with the highest correlations (90%) in the northeastern U.S., where urban areas are often embedded in temperate broadleaf and mixed forests. They also determined that the largest urban-rural LST differences for all biomes occurred during the summer around midday, and that the greatest amplitudes were found for urban areas that displaced forests (6.5-9.0°C) followed by temperate grasslands (6.3°C) and tropical grasslands and savannas (5.0°C). Last of all, they determined that the contrast between urban cores and rural zones was typically "accentuated during the time when the vegetation is physiologically active, especially in forested lands," and that "the amplitude of the UHI is significantly diminished during the winter season when vegetation loses its leaves or is stressed by lower temperatures."
Based on these findings, Imhoff et al. conclude that "the use of ISA as an estimator of the extent and intensity of urbanization is more objective than population density based methods and can be consistently applied across large areas for inter-comparison of impacts on biophysical processes."