An efficient approach to 3D single tree-crown delineation in LiDAR data

This paper proposes a new method for 3D delineation of single tree-crowns in LiDAR data by exploiting the complementaries of treetop and tree trunk detections. A unified mathematical framework is provided based on the graph theory, allowing for all the segmentations to be achieved using marker-controlled watersheds. Treetops are defined by detecting concave neighbourhoods within the canopy height model using locally fitted surfaces. These serve as markers for watershed segmentation of the canopy layer where possible oversegmentation is reduced by merging the regions based on their heights, areas, and shapes. Additional tree crowns are delineated from mid- and under-storey layers based on tree trunk detection. A new approach for estimating the verticalities of the points' distributions is proposed for this purpose. The watershed segmentation is then applied on a density function within the voxel space, while boundaries of delineated trees from the canopy layer are used to prevent the overspreading of regions. The experiments show an approximately 6% increase in the efficiency of the proposed treetop definition based on locally fitted surfaces in comparison with the traditionally used local maxima of the smoothed canopy height model. In addition, 4% increase in the efficiency is achieved by the proposed tree trunk detection. Although the tree trunk detection alone is dependent on the data density, supplementing it with the treetop detection the proposed approach is efficient even when dealing with low density point-clouds.