| Popis: |
Recently-developed airborne imaging laser radar systems are capable of rapidly collecting accurate and precise spatial information for topographic characterization as well as surface imaging. However, the performance of airborne ladar (laser detection and ranging) collection systems often depends upon the density and distribution of tree canopy over the area of interest, which obscures the ground and objects close to the ground such as buildings or vehicles. Traditionally, estimates of canopy obscuration are made using ground-based methods, which are time-consuming, valid only for a small area and specific collection geometries when collecting data from an airborne platform. Since ladar systems are capable of collecting a spatially and temporally dense set of returns in 3D space, the return reflections can be used to differentiate and monitor the density of ground and tree canopy returns in order to measure, in near real-time, sensor performance for any arbitrary collection geometry or foliage density without relying on ground based measurements. Additionally, an agile airborne ladar collection system could utilize prior estimates of the degree and spatial distribution of the tree canopy for a given area in order to determine optimal geometries for future collections. In this paper, we report on methods to rapidly quantify the magnitude and distribution of the spatial structure of obscuring canopy for a series of airborne high-resolution imaging ladar collections in a mature, mixed deciduous forest. |
