Abstract:This paper presents a new technique for mapping detention basins and measuring their spatial attributes using high-resolution airborne LiDAR (Light Detection and Ranging) data. An efficient least-cost search algorithm is employed to identify surface depressions from a bare-earth LiDAR digital elevation model (DEM). Surface depressions are automatically delineated into hydrological objects using the connected component identification and indexing algorithm. Various spatial attributes are derived for these hydrologic objects, including location, perimeter, surface area, depth, storage volume and shape properties. Based on spatial attributes, a rule-based classifier is established to separate detention basins from other types of surface depressions. We have successfully applied our technique to an urban watershed in the Houston Metropolitan area, Texas. Detention basins at regional and residential subdivision levels are mapped out for the watershed, and measurements on the spatial attributes are derived for each detention basin. The quantitative information derived from LiDAR data provides a scientific basis for formulating an appropriate management plan for detention basins and for assessing their effects on flood control and storm water quality treatment.