Tree Branch Characterisation from Point Clouds: a Comprehensive Review

Abstract 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.