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探索三维中的树木:VoxR,一个新的基于体素的 R 包,专门用于分析树冠的复杂排列。

Exploring trees in three dimensions: VoxR, a novel voxel-based R package dedicated to analysing the complex arrangement of tree crowns.

机构信息

Department of Biological Sciences, Centre for Forest Research (CEF) and NSERC/Hydro-Québec Chair on tree growth control, Université du Québec à Montréal, Montreal, Canada.

Department of Natural Resources, Institute of Temperate Forest Sciences and Centre for Forest Research (CEF), Université du Québec en Outaouais, Ripon, Canada.

出版信息

Ann Bot. 2018 Mar 14;121(4):589-601. doi: 10.1093/aob/mcx095.

Abstract

BACKGROUND

Interest in tree form assessments using the terrestrial laser scanner (TLS) has increased in recent years. Yet many existing methods are limited to small-sized trees, principally due to noise and occlusion phenomena. In this paper, a novel voxel-based program that is dedicated to the analyses of large tree structures is presented. The method is based on the assumption that architectural trait variations (i.e. branching angle, bifurcation ratio, biomass allocation, etc.) influence the way a tree explores space. This method uses the concept of space exploration that considers a voxel as a portion of space explored by the tree. Once the TLS scene is voxelized, the program provides tools that extract qualitative (geometrical) and quantitative (volumetric) metrics. These tools measure (1) voxel dispersion in three dimensions (3-D), (2) projections of the voxel cloud in 2-D and (3) multi-temporal changes within a single tree crown.

SCOPE

To test algorithm capabilities of measuring larger tree architectural traits, two application studies were conducted using point clouds that were either generated by a tree growth simulation model, thereby allowing algorithm application in a perfectly controlled environment, or acquired in the field with a TLS device. The space exploration concept makes it possible to take advantage of the volumetric nature of voxels to compensate for occlusion. The hypothesis that large-sized voxels can be used to reduce occlusion in the original point cloud was tested, as well as the consequences of voxel size on quantification of tree volume and on precision of derived metrics.

CONCLUSIONS

Results show that space exploration is well adapted to highlight architectural differences among trees. They also suggest that large-sized voxels are efficient for occlusion compensation at the expense of metrics precision in some cases. The best resolution to choose depending on the research objectives and quality of the TLS scan is discussed.

摘要

背景

近年来,人们对使用地面激光扫描仪(TLS)进行树木形态评估的兴趣日益浓厚。然而,许多现有的方法主要由于噪声和遮挡现象,仅限于小型树木。在本文中,提出了一种新的基于体素的程序,专门用于分析大型树木结构。该方法基于这样的假设,即结构特征变化(例如分枝角度、分支比、生物量分配等)影响树木探索空间的方式。该方法使用空间探索的概念,将体素视为树木探索空间的一部分。一旦对 TLS 场景进行体素化,该程序就会提供用于提取定性(几何)和定量(体积)指标的工具。这些工具可测量:(1)三维(3-D)中的体素分散度,(2)体素云的二维(2-D)投影,以及(3)单棵树冠内的多时相变化。

范围

为了测试算法测量较大树木结构特征的能力,使用两种应用研究,分别使用由树木生长模拟模型生成的点云,或者使用 TLS 设备在野外获取的点云。空间探索的概念使得可以利用体素的体积性质来补偿遮挡。测试了使用大体积体素来减少原始点云中遮挡的假设,以及体素大小对树木体积量化和衍生指标精度的影响。

结论

结果表明,空间探索非常适合突出树木之间的结构差异。它们还表明,大体积体素可以有效地补偿遮挡,但在某些情况下会牺牲指标精度。根据研究目标和 TLS 扫描的质量,讨论了最佳分辨率的选择。

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