Abstract
The reconstruction of trees from point clouds that were acquired with terrestrial LiDAR scanning (TLS) may become a significant breakthrough in the study and modelling of tree development. Here, we develop an efficient method and a tool based on extensive modifications to the skeletal extraction method that was first introduced by Verroust and Lazarus in 2000. PypeTree, a user-friendly and open-source visual modelling environment, incorporates a number of improvements into the original skeletal extraction technique, making it better adapted to tackle the challenge of tree perennial tissue reconstruction. Within PypeTree, we also introduce the idea of using semi-supervised adjustment tools to address methodological challenges that are associated with imperfect point cloud datasets and which further improve reconstruction accuracy. The performance of these automatic and semi-supervised approaches was tested with the help of synthetic models and subsequently validated on real trees. Accuracy of automatic reconstruction greatly varied in terms of axis detection because small (length < 3.5 cm) branches were difficult to detect. However, as small branches account for little in terms of total skeleton length, mean reconstruction error for cumulated skeleton length only reached 5.1% and 1.8% with automatic or semi-supervised reconstruction, respectively. In some cases, using the supervised tools, a perfect reconstruction of the perennial tissue could be achieved.
Highlights
IntroductionTerrestrial LiDAR Scanning (TLS) measures the precise location of objects in 3D space and, represents a promising tool for studying large and complex organisms such as trees [1,2]
Terrestrial LiDAR Scanning (TLS) measures the precise location of objects in 3D space and, represents a promising tool for studying large and complex organisms such as trees [1,2].In recent years, it has become an efficient alternative to established forest inventory methods for obtaining data on forest structure [3,4,5] and has been used to extract whole-tree or crown traits from a tree or a group of trees [6,7,8,9]
The PypeTree reconstruction engine is based on the skeletal extraction algorithm that was introduced by Verroust and Lazarus [22], which we have extensively studied to devise a series of improvements
Summary
Terrestrial LiDAR Scanning (TLS) measures the precise location of objects in 3D space and, represents a promising tool for studying large and complex organisms such as trees [1,2]. Studies using virtual tree models that have been developed from TLS data result in improved realistic surveys. Such models include the imprint that is left by all real past stochastic events on the studied tree. Several recent studies have devised algorithms to perform tree reconstruction from TLS-acquired point clouds [8,9,15,16,17,18,19]. These studies have yielded promising results (from saplings to adult trees), empirical validation of these tools is rarely performed. We describe the model interface and validate the accuracy of PypeTree reconstructions against actual field measurements taken from several sapling tree species
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