Adaptive Methods for Individual Tree Detection on Airborne Laser Based Canopy Height Model

One problem of individual tree detection on aerial images or on raster canopy height models is handling of tree crowns of different sizes. On laser scanner data one size attribute, height, is directly available. This gives possibilities to develop processing methods that adapt to the object size. In this study, three adaptive methods were developed and tested for individual tree detection on canopy height model (CHM). The CHM of 0.5 m pixel size was computed from dense first-pulse point data that was acquired with a smallfootprint airborne laser scanner. The field data consisted of 10 tree mapped field plots in Kalkkinen, southern Finland. The plots were mainly on mature, heavily stocked forest stands, many of which had multi-layered canopy structure. In the first method, the CHM was smoothed with canopy height based selection of degree of smoothing and local maxima on the smoothed CHM were considered as tree locations. In the second and third methods, we utilised crown diameter predicted from tree height. The second method used elimination of candidate tree locations based on the predicted crown diameter and distance and valley depth between two locations studied. The third method was modified from scale-space method used for blob detection. Instead of automatic scale selection of the scale-space method, the scale for Laplacian filtering, used in blob detection, was determined according to the predicted crown diameter. The presented three methods are compared based on the accuracy of individual tree detection. Differences of the proposed methods considering tree crown delineation by segmentation methods are discussed.