aTrunk—An ALS-Based Trunk Detection Algorithm
AbstractThis 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
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Lamprecht, S.; Stoffels, J.; Dotzler, S.; Haß, E.; Udelhoven, T. aTrunk—An ALS-Based Trunk Detection Algorithm. Remote Sens. 2015, 7, 9975-9997.
Lamprecht S, Stoffels J, Dotzler S, Haß E, Udelhoven T. aTrunk—An ALS-Based Trunk Detection Algorithm. Remote Sensing. 2015; 7(8):9975-9997.Chicago/Turabian Style
Lamprecht, Sebastian; Stoffels, Johannes; Dotzler, Sandra; Haß, Erik; Udelhoven, Thomas. 2015. "aTrunk—An ALS-Based Trunk Detection Algorithm." Remote Sens. 7, no. 8: 9975-9997.