aTrunk—An ALS-Based Trunk Detection AlgorithmReport as inadecuate

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Remote Sensing & Geoinformatics Department, Trier University, Behringstraße, Trier 54286, Germany


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Academic Editors: Peter Krzystek, Clement Atzberger and Prasad S. Thenkabail

Abstract This paper presents a rapid multi-return ALS-based Airborne Laser Scanning tree trunk detection approach. The multi-core Divide and 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 about 2-3 Norway Spruce and 1-3 European 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 0.59m average difference and 0.78m RMSE. The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks. View Full-Text

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

Author: Sebastian Lamprecht * , Johannes Stoffels, Sandra Dotzler, Erik Haß and Thomas Udelhoven



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