High-resolution dataset of urban canopy parameters for Houston, Texas

Urban dispersion and air quality simulation models applied at various horizontal scales require different levels of fidelity for specifying the characteristics of the underlying surfaces. As the modeling scales approach the neighborhood level (~1 km horizontal grid spacing), the representation of urban morphological structures and surface cover properties requires much greater detail. To provide the most accurate surface characterization possible for an air quality modeling study of Houston, Texas, airborne LIDAR (Light Detection and Ranging) data were obtained at 1-m horizontal grid cell spacing for Harris County, Texas, an area of approximately 5800 km². Using GIS analysis techniques, more than 20 urban canopy parameters (UCPs) were computed including building height statistics and histograms, height-to-width ratio, plan area density function, frontal area density function, roughness length, displacement height, mean orientation of streets, and sky view factor. In an effort to improve the efficiency of the roughness length derivation, a second roughness dataset was derived using satellite data collected by Synthetic Aperture Radar (SAR) instrumentation. The comparison of the SAR roughness with the morphometric roughness suggested an integration of the base satellite and airborne LIDAR datasets may provide the most accurate roughness data layer in the most efficient manner. In this paper, we describe the high-resolution Houston UCP dataset, report on the variability of the UCPs across the Houston urban terrain, present the comparison of the morphometric roughness and SAR roughness, and summarize the assessment of methods to extrapolate UCPs to outlying areas of the city where high-resolution full-feature terrain datasets are not available.