| description abstract | During urbanization, undisturbed land surfaces are altered to create manufactured landscapes. Classifications of these new urban surfaces are utilized in urban planning, environmental monitoring, and other applications such as storm water management and roof runoff harvesting system design. To evaluate runoff volume and design storm water control devices, areas of different urban surfaces need to be identified and defined as pervious (e.g., undisturbed soils and landscaped areas) and impervious surfaces (e.g., roofs, roads, parking lots, sidewalks, driveways). This study presents a means to facilitate urban surface classification and quantification by analyzing high resolution aerial photographs in conjunction with light detection and ranging (LiDAR) data in a custom application for the geographic information system software. This software processes aerial photographs using red/green/blue (RGB) bands to produce a raster with saturation (S) values. In parallel, LiDAR data are used to distinguish the major surface categories of pavement from roofs and pervious surfaces, and topologically integrated geographic encoding and referencing (TIGER) centerlines are used to identify streets. This process was tested for two different land uses: institutional (University of Alabama, Tuscaloosa, Alabama) and residential (also located in Tuscaloosa, Alabama). Compared to manually delineated areas, the urban area classification differences ranged from 0.2 to 5.2% for roofs, streets, parking lots, and pervious areas. The efficiency of the process compared to the manual delineation of surface areas resulted in time and effort savings ranging from 80 to 90% depending on the size of the area processed. Manual verification by field observations of other characteristics, such as the curb-side drainage system type, still needs to be performed regardless of the surface characterization method employed. Determining the basic area measurements greatly accelerates this initial phase into a comprehensive storm water management planning effort. | |
