Tree segmentation and classification of deciduous park trees in Sanssouci Park, Potsdam, Germany, using airborne and terrestrial lidar point clouds

While automated, lidar-based tree delineation has proven successful for
conifer-dominated forests, deciduous tree stands remain a challenge.  But
automatic and reliable segmentation of trees at large spatial scales is a
prerequisite for a supervised classification into tree species. We propose an
aspect driven tree segmentation that clusters local elevation minima across
different aspects. These clusters define tree outlines that respect tree
inherent local elevation minima. We validate this approach with more than
25.000 mapped trees of the Sanssouci Park, Potsdam, using an airborne lidar
point cloud collected in 2018, and various terrestrial lidar scans for a large
fraction of the same park. Further, we demonstrate the tree segmentation by
supervised tree species classifications for the most common tree species using
random forests and Gaussian process classifiers with geometric parameters
derived from individual tree crowns.