aTrunk—An ALS-Based Trunk Detection Algorithm

Remote Sensing

Sebastian Lamprecht¹  Johannes Stoffels²  Sandra Dotzler²  Erik Haß²  Thomas Udelhoven²  Peter Krzystek²  Clement Atzberger² 

[1] Sensing & Geoinformatics Department, Trier University, Behringstraße, Trier 54286, Germany;

关键词: airborne LiDAR;    stem detection;    tree recognition;    trunk orientation;    clustering;    forest;    3D;   

DOI  :  10.3390/rs70809975

来源: mdpi

【 摘 要 】

This paper presents a rapid multi-return ALS-based (Airborne Laser Scanning) tree trunk detection approach. The multi-core Divide & Conquer algorithm uses a CBH (Crown Base Height) estimation and 3D-clustering approach to isolate points associated with single trunks. For each trunk, a principal-component-based linear model is fitted, while a deterministic modification of LO-RANSAC is used to identify an optimal model. The algorithm returns a vector-based model for each identified trunk while parameters like the ground position, zenith orientation, azimuth orientation and length of the trunk are provided. The algorithm performed well for a study area of 109 trees (aboutEuropean Beech), with a point density of 7.6 points per m², while a detection rate of about 75% and an overall accuracy of 84% were reached. Compared to crown-based tree detection methods, the aTrunk approach has the advantages of a high reliability (5% commission error) and its high tree positioning accuracy (0.59 m average difference and 0.78 m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.