aTrunk

aTrunk - An ALS-Based Trunk Detection Algorithm. 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 (0.59 m average difference and 0.78 m RMSE). The usage of overlapping segments with parametrizable size allows a seamless detection of the tree trunks.

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References in zbMATH (referenced in 1 article )

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  1. Sebastian Lamprecht: Pyoints: A Python package for point cloud, voxel and raster processing (2019) not zbMATH