Techniques to estimate urban canopy parameters using satellite data

This paper presents an array of techniques to derive urban canopy parameters (UCPs) using remote sensing data. The potential for Synthetic Aperture Radar (SAR), Light Detection and Ranging (LIDAR), Shuttle Radar Topography Mission (SRTM), and multispectral data individually, in combination, and in concert with ancillary geospatial datasets to derive UCPs is analyzed. Preliminary methods are described to determine building and tree height characteristics (e.g., mean, standard deviation), density characteristics (e.g., plan area fraction, complete aspect ratio, sky view factor), drag factors (frontal area index), and aerodynamic roughness parameters (roughness length, displacement height). The results of the remote-sensing based methods are compared to the results obtained from processing three-dimensional building and tree datasets for a number of cities in the United States with a range of morphological properties. Recommendations on future needs for remote-sensing based UCP derivation will be presented.