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Remote Sens. 2015, 7(8), 9975-9997; doi:10.3390/rs70809975

aTrunk—An ALS-Based Trunk Detection Algorithm

Remote Sensing & Geoinformatics Department, Trier University, Behringstraße, Trier 54286, Germany
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Academic Editors: Peter Krzystek, Clement Atzberger and Prasad S. Thenkabail
Received: 16 April 2015 / Revised: 10 July 2015 / Accepted: 31 July 2015 / Published: 5 August 2015
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Abstract

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 (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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lamprecht, S.; Stoffels, J.; Dotzler, S.; Haß, E.; Udelhoven, T. aTrunk—An ALS-Based Trunk Detection Algorithm. Remote Sens. 2015, 7, 9975-9997.

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