Change detection of trees in urban areas using mutil-temporal airborne lidar point clouds

Light detection and ranging (lidar) provides a promising way of detecting changes of vegetation in three dimensions (3D) because the beam of laser may penetrate through the foliage of vegetation. This study aims at the detection of changes in trees in urban areas with a high level of automation using mutil-temporal 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 was applied first to group the points of trees together. The attributes of components were utilized to differentiate tree components from misclassified non-tree components. A point based local maxima algorithm was implemented to distinguish single tree from multiple tree components. After that, the parameters of trees were derived through two independent ways: a point based method using 3D alpha shapes and convex hulls; and a model based method which fits a Pollock tree model to the points. Then the changes were detected by comparing the parameters of corresponding tree components which were matched by a tree to tree matching algorithm using the overlapping of bounding boxes and point to point distances. The results were visualized and statistically analyzed. The difference of parameters and the difference of changes derived from point based and model based methods were both lower than 10%. The comparison of these two methods illustrates the consistency and stability of the parameters. The detected changes show the potential to monitor the growth and pruning of trees.