Purpose of Review

Since the late 1990s, researchers have been increasingly utilising digital methodologies to assess the branch structure of trees. The emergence of commercial terrestrial laser scanners during this period catalysed an entirely new domain focused on point cloud-based research. Over the years, this field has transformed from a complex computational discipline into a practical tool that effectively supports research endeavours. Through the combined use of non-destructive remote sensing techniques and advanced analytical methods, branch characterisation can now be carried out at an unprecedented level.

Recent Findings

While terrestrial laser scanning has traditionally been the dominant methodology for this research domain, the increased use of mobile laser scanners and unmanned aerial vehicles indicates a transition towards more mobile platforms. Quantitative structural modelling (QSM) has been pivotal in advancing this field, enhancing branch characterisation capabilities across diverse fields. The past five years have seen increased uptake of 2D and 3D deep learning techniques as alternatives.

Summary

This article presents a comprehensive synthesis of approximately 25 years of research in the field of digital branch characterisation, reviewing the data capture technologies and analytical methods, along with the forest types and tree species to which these technologies have been applied. It explores the current trends in this dynamic field of research, research gaps and some of the key challenges that remain within this field. In this review, we placed particular emphasis on the potential resolution of the significant challenge associated with occlusion through the utilisation of mobile technologies, such as mobile laser scanners and unmanned aerial vehicles. We highlight the need for a more cohesive method for assessing point cloud quality and derived structural model accuracy, and benchmarking data sets that can be used to test new and existing algorithms.

中文翻译：

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点云的树枝表征：综合回顾

 审查目的

自 20 世纪 90 年代末以来，研究人员越来越多地利用数字方法来评估树木的树枝结构。这一时期商业地面激光扫描仪的出现催生了一个专注于基于点云的研究的全新领域。多年来，该领域已从复杂的计算学科转变为有效支持研究工作的实用工具。通过结合使用无损遥感技术和先进的分析方法，现在可以以前所未有的水平进行分支表征。

 最近的发现

虽然地面激光扫描历来是该研究领域的主要方法，但移动激光扫描仪和无人机的使用增加表明了向更多移动平台的过渡。定量结构建模（QSM）在推动该领域发展、增强跨不同领域的分支表征能力方面发挥了关键作用。过去五年，2D 和 3D 深度学习技术作为替代技术的采用有所增加。

 概括

本文全面综合了数字树枝表征领域约 25 年的研究成果，回顾了数据采集技术和分析方法，以及应用这些技术的森林类型和树种。它探讨了这个动态研究领域的当前趋势、研究差距以及该领域仍然存在的一些关键挑战。在本次审查中，我们特别强调通过利用移动技术（例如移动激光扫描仪和无人机）来解决与遮挡相关的重大挑战的潜在解决方案。我们强调需要一种更有凝聚力的方法来评估点云质量和派生结构模型的准确性，以及可用于测试新算法和现有算法的基准数据集。