Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data

By Pasi Raumonen, Mikko Kaasalainen, Markku Åkerblom, S. Kaasalainen, Harri Kaartinen, Mikko Vastaranta, Markus Holopainen, Mathias I. Disney, Philip Lewis. Remote Sensing, 2013

788 citations recorded by the source corpus.

This paper presents a new method for constructing quickly and automatically precision tree models from point clouds of the trunk and branches obtained by terrestrial laser scanning. The input of the method is a point cloud of a single tree scanned from multiple positions. The surface of the visible parts of the tree is robustly reconstructed by making a flexible cylinder model of the tree. The thorough quantitative model records also the topological branching structure. In this paper, every major step of the whole model reconstruction process, from the input to the finished model, is presented in detail. The model is constructed by a local approach in which the point cloud is covered with small sets corresponding to connected surface patches in the tree surface. The neighbor-relations and geometrical properties of these cover sets are used to reconstruct the details of the tree and, step by step, the whole tree. The point cloud and the sets are segmented into branches, after which the branches are modeled as collections of cylinders. From the model, the branching structure and size properties, such as volume and branch size distributions, for the whole tree or some of its parts, can be approximated. The approach is validated using both measured and modeled terrestrial laser scanner data from real trees and detailed 3D models. The results show that the method allows an easy extraction of various tree attributes from terrestrial or mobile laser scanning point clouds.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/947244e69c7ec794ac4c61e809470ad68a6a2672

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Citations in the corpus

SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds (2015, methodology): “Methodname (a, b) is here-on a wild-card for a method with name Methodname, utilizing input parameters a and b.”
Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy (2015, background): “, 2014) and TLS can accuratelymeasurewoody volume (Raumonen et al., 2013), potentially allowing rapid biomass measurement.”
Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density (2015, methodology): “[26] we adjusted the method by detecting stem and large branch points before the circle fitting routine.”
Leaf and wood classification framework for terrestrial LiDAR point clouds (2019, methodology): “These include the accurate estimation of above- ground biomass (Calders et al., 2015; Gonzalez de Tanago et al., 2018; Momo Takoudjou et al., 2018; Raumonen et al., 2013), 3D vegetation density (Grau, Durrieu, Fournier, Gastellu- Etchegorry, & Yin, 2017), canopy profiles (CuniSanchez et al.”
Nondestructive estimates of above‐ground biomass using terrestrial laser scanning (2015, methodology): “Next, the patches are segmented into stem and branches using the algorithm presented in Raumonen et al. (2013), with some additional correctional steps: 1.”