Individual Tree Crown Modeling and Change Detection From Airborne Lidar Data

Abstract

Light detection and ranging (lidar) provides a promising way of detecting changes of trees in three-dimensional (3-D) because laser beams can penetrate through the foliage and therefore provide full coverage of trees. The aim is to detect changes in trees in urban areas using multitemporal airborne lidar point clouds. Three datasets covering a part of Rotterdam, The Netherlands, have been classified into several classes including trees. A connected components algorithm is applied first to cluster the tree points. However, closely located and intersected trees are clustered together as multi-tree components. A tree-shaped model-based continuously adaptive mean shift (CamShift) algorithm is implemented to further segment these components into individual trees. Then, the tree parameters are derived in two independent methods: a point-based method using the convex hull and a model-based method which fits a tree-shaped model to the lidar points. At last, changes are detected by comparing the parameters of corresponding tree models which are matched by a tree-to-tree matching algorithm using overlapping bounding boxes and point-to-point distances. The results are visualized and statistically analyzed. The CamShift using a tree model kernel yields high segmentation accuracies. The model-based change detection is consistent with the point-based method according to the small differences between the parameters of single trees. The highlight is that it is more robust to data noise and to the segmentation of multi-tree components compared to the point-based method. The detected changes show the potential of the method to monitor the growth of urban trees.