Mesh Surface And Morphological Hierarchies For Individual Tree Detection And Segmentation From LiDAR Data

This paper presents a novel and efficient individual tree detection and segmentation method for LiDAR point clouds. We rely on a surface model of the forest to find tree tops in the canopy. Efficient connected component filtering is used to filter the surface model, detect and segment individual trees by tuning a single physically interpretable parameter. We validate our method on a genuine LiDAR point cloud and tree inventory dataset and show on-par results with a recent state-of-the-art individual tree detection study. Our method is original because, unlike the previous methods based on connected components, we do not depend on an intermediate raster to carry out the morphological filtering. Instead, our method relies on a graph that directly connects the points of the LiDAR data. This original approach not only opens direct improvements for tree detection in surface models, but also provides a broader and more efficient way to process LiDAR point clouds beyond individual tree detection and segmentation.