Integrating terrestrial laser scanning with functional–structural plant models to investigate ecological and evolutionary processes of forest communities

Abstract

Background Woody plants (trees and shrubs) play an important role in terrestrial ecosystems, but their size and longevity make them difficult subjects for traditional experiments. In the last 20 years Functional-Structural Plant Models (FSPMs) have evolved: they consider the interplay between plant modular structure, the immediate environment and internal functioning. However, computational constraints and data deficiency have long been limiting factors in a broader application of FSPMs, particularly at the scale of forest communities. Recently, Terrestrial Laser Scanning (TLS), has emerged as an invaluable tool for capturing the 3D structure of forest communities, thus opening up exciting opportunities to explore and predict forest dynamics with FSPMs. Scope The potential synergies between TLS-derived data and FSPMs have yet to be fully explored. Here, we summarize recent developments in FSPM and TLS research, with a specific focus on woody plants. We then evaluate the emerging opportunities for applying FSPMs in an ecological and evolutionary context, in light of TLS-derived data, with particular consideration of the challenges posed by scaling up from individual trees to whole forests. Finally, we propose guidelines for incorporating TLS data into the FSPM workflow to encourage overlap of practice amongst researchers. Conclusions We conclude that TLS is a feasible tool to help shift FSPMs from an individual-level modelling technique to a community-level one. The ability to scan multiple trees, of multiple species, in a short amount of time, is paramount to gathering the detailed structural information required for parameterising FSPMs for forest communities. Conventional techniques, such as repeated manual forest surveys, have their limitations in explaining the driving mechanisms behind observed patterns in 3D forest structure and dynamics. Therefore, other techniques are valuable to explore how forests might respond to environmental change. A robust synthesis between TLS and FSPMs provides the opportunity to virtually explore the spatial and temporal dynamics of forest communities.

The shape of trees: Reimagining forest ecology in three dimensions with remote sensing

“…Reconstruction methods such as quantitative structural models (QSMs; see Figure 1a,b) allow the measurement of fine‐scale structural details such as twigs, leaves and branches, and how they change through time, from TLS data (Kaasalainen et al, 2014; Raumonen et al, 2013; Wilkes et al, 2021). Furthermore, such data enable new ways to explicitly test theories on crown resource optimisation, light use efficiency and self‐shading (Niinemets & Anten, 2009) and pair these to structural‐functional plant models (O'Sullivan et al, 2021), leading to new knowledge of structural trade‐offs complimentary to well‐established leaf and wood economics spectra (Verbeeck et al, 2019). Very high‐resolution reconstructions of single trees that reliably preserve fine‐scale internal structure may also open new avenues of research to understand the fundamental relationships between genetics, plant morphogenesis and architecture, including testing theories of nested levels of architectural organisation (Barthélémy & Caraglio, 2007), revisiting geometric theories of plant architectural growth (Godin et al, 1999), and improving understanding genetic controls on branching architecture in woody plants (Teichmann & Muhr, 2015).…”

Inferring plant–plant interactions using remote sensing

“…Ideally, such data are collected over time, allowing temporal dynamics to be captured. Non‐invasive and non‐destructive quantification of these traits is therefore preferred, and this is where remote‐sensing techniques can play an important role (O'Sullivan et al, 2021). For example, Perez et al (2022) suggested that integrating lidar‐derived shoot architectural parameters of oil palm with an FSP model can help quantify shoot allometry in relation to neighbourhood competition intensity.…”

A Deep Learning-Based Method for Extracting Standing Wood Feature Parameters from Terrestrial Laser Scanning Point Clouds of Artificially Planted Forest

“…The laser scanning systems commonly used to collect forestry information can be divided into the following categories depending on the carrier platform: terrestrial laser scanning (including terrestrial laser, backpack laser, and vehicle-borne laser), satellite lidar scanning, and airborne laser scanning. Among them, terrestrial laser scanning systems are widely used in forest remote sensing because of their high flexibility and portability and good point cloud quality [14][15][16][17][18]. The datasets we collected in this paper are based on terrestrial laser scanning.…”