Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns

This paper presents a 3D delineation method for airborne laser scanning point cloud. The method is based on an unsupervised clustering technique applied on horizontal slices followed by vertical merging based on overlapping among clusters. On an Alpine forest dataset, we analysed the effects of different forest structures and point cloud densities on tree crown delineation. Forest structure affects mainly the omission error, which eases with homogeneous tree spacing and sizes, while on the commission error forest structure has only slight effect. Delineation accuracy increases with higher point densities where Mann-Whitney-Wilcoxon test shows that accuracy differences between thinned data and original data are statistically significant