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Abstract

Airborne laser scanning (ALS) is known as an operational tool for collecting high resolution elevation information (> 4 pt/m²). The characteristics of the emitted pulses, i.e. their spatial extent, allow the detection of multiple echoes, which occur especially in areas covered with high vegetation. In the case of forested areas this means that not only the first reflection on the canopy but also reflections on or near the ground surface are recorded. The detection of high vegetation in urban areas (single trees, groups, and small forests next to residential areas) is needed for several applications. Classified vegetation and derived parameters, such as height, volume and density, are used in urban planning, urban ecology and 3D city modeling. The here presented algorithm follows the principle of object-based point cloud analysis (OBPA), which consists of (i) segmentation of the original ALS point cloud, (ii) feature calculation for the delineated segments and (iii) classification to label the objects of interest. The segmentation is based on an intelligent seed point selection by surface roughness, initializing a region growing process. Point features for the segmentation and classification, respectively, are e.g. roughness, the ratio between 3D and 2D point density, or statistics on first an last echo occurrence within the segments. The advantage of the developed algorithm is that no calculation of a digital terrain model is needed and that it solely works in the original point cloud, maintaining the maximal achievable accuracy. For the evaluation of the method a flight campaign of the city of Innsbruck/Austria is used as test site.