Recent Progress in Thermal Remote Sensing of Urban Areas

Remotely sensed imagery has been increasingly used to study the urban heat island phenomenon by deriving and analyzing land surface temperatures. The technology of remote sensing has the advantage of providing a time-synchronized dense grid of temperature data over a whole city region and distinctive temperature measurements for individual buildings. Moreover, some remote sensing images have high temporal resolution, in addition to being cost-effective. A key issue in the application of remote sensing technology is how to use land surface temperature measurements at the micro-scale to characterize, quantify, and model heat islands observed at the meso-scale. The findings reported here are from our funded project by U.S. National Science Foundation, which is entitled " Role of urban canopy composition and structure in determining heat islands: a synthesis of remote sensing and landscape ecology approach ". The project aimed at examining the effect of urban surface composition and structure on urban surface energy budgets and a better understanding of the thermal behavior of urban landscapes and the heat island phenomena, and was conducted in Indianapolis, Indiana, United States, with satellite images from sensors of various spatial, spectral, and temporal resolutions, including Landsat TM, ETM+, Terra's ASTER, and MODIS thermal infrared data. Specifically, there were three objectives: (1) To investigate the relationship between land surface temperature (LST) and quantitative surface biophysical descriptors, which are sub-pixel measurements, derived from spectral unmixing of medium-resolution satellite images; (2) To examine the scaling-up effect on the relationship between land use and land cover (LULC) and LST patterns by the use of landscape metrics; and (3) To relate micro-scale (pixel) measurements of LST to meso-scale UHI measurements of the entire city, in order to derive UHI parameters: magnitude, the spatial extent, the orientation, and the central location. To meet Objective 1, an analytical procedure was developed for characterizing and quantifying the urban landscape in Indianapolis based on linear spectral mixture analysis (LSMA). A Landsat