Abstract
Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.
Highlights
Tree extraction from remotely sensed data, in addition to applications in mapping and three-dimensional modelling, is worthwhile from the aspect of environmental term and urban green space management
Aerial image interpretation is impeded by different spectroradiometric distortions caused by vignetting effects, atmospheric variations, sun-target-sensor geometry or view/illumination geometry and topographyinduced illumination variations (Lillesand et al 2008)
Airborne LiDAR can penetrate in tree crown, provide the geometric information and show some internal structure of the trees, and these capabilities may increase by increasing in point density
Summary
Tree extraction from remotely sensed data, in addition to applications in mapping and three-dimensional modelling, is worthwhile from the aspect of environmental term and urban green space management. In the study of Kumar, the impact of the degree of softening on the number of detected peaks is clearly visible on table 2 In these algorithms, the number and the locations of detected peaks heavily effect on the process of detecting individual trees (Kumar, 2012). In the study of Duncanson et al intermediate trees are over predicted (commission error) and understory trees are often undetected (omission error) (Duncanson et al, 2014) To overcome this problem, Alonzo et al, tried to control the distance between the detected peaks by applying one or more thresholds (Alonzo et al, 2014). A new method to extract individual trees points from LiDAR data with no need to extract seed points is developed. The main idea of this method consists in extracting deterministic segments of individual tree points and investigating membership of other points to them
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