Over the last two decades, terrestrial light detection and ranging (LiDAR), also known as terrestrial laser scanning (TLS) has become a valuable tool in assessing the woody structure of trees, in a method that is accurate, non-destructive, and replicable. This technique provides the ability to scan an area, and utilizes specialized software to create highly detailed 3D point cloud representations of its surroundings. Although the original usage of LiDAR was for precision survey applications, researchers have begun to apply LiDAR to forest research. Tree metrics can be extracted from TLS tree point clouds, and in combination with structure modelling, can be used to extract tree volume, aboveground biomass (AGB), growth, species, and to understand ecological questions such as tree mechanics, branching architecture, and surface area. TLS can provide a robust and rapid assessment of tree characteristics. These characteristics will improve current global efforts to measure forest carbon emissions, understand their uncertainties, and provide new insight into tropical forest ecology. Thus, the main objective of this PhD is to explore the use of 3D models from terrestrial laser scanning point clouds to estimate biomass and architecture of tropical trees. TLS-derived biomass and TLS-derived architecture can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests.
In this project, a dataset of forest inventory with TLS point clouds and destructive tree harvesting were created from three tropical regions: Indonesia, Guyana, and Peru. A total of 1858 trees were traditionally inventoried, 135 trees were TLS scanned, and 55 trees were destructively harvested. In this project, procedures to estimate tree metrics such as tree height (H), diameter at breast height (D), crown diameter (CD), and the length and diameter of individual branches were developed using 3D point clouds and 3D modelling. From these tree metrics, we inferred AGB, developed allometric models, and estimated metabolic plant scaling of individual tropical trees. All these metrics are validated against a traditional forest inventory data and destructively harvested trees.
This project involved the development of a PhD research consisting of six chapters (Introduction, 4 research chapters and Synthesis). The main objective of the thesis was to explore the use of 3D models from TLS point clouds to estimate above-ground biomass and architecture of tropical trees (Research Question - RQ 1). TLS-derived AGB and TLS-derived architecture can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests (RQ 2). For that, procedures were developed in the following chapters (chapters 2 to 5) to estimate accurate tropical tree information using terrestrial laser scanning.
Chapter 2 presents a procedure to estimate tree volume and quantify AGB for large tropical trees based on estimates of tree volume and basic wood density. This chapter is based on the research article from Gonzalez de Tanago, et al., 2018. Chapter 3 focuses on the development of accurate local allometric models to estimate tree AGB in Guyana based solely on TLS-based tree metrics (H, CD, and D) and validated against destructive measurements. Chapter 4 provides an insight into the architecture and branching structure of tropical trees. In Chapter 2, the potential of TLS to characterize woody tree structure as a function of tree volume was proved, but little is known regarding their detailed architecture. This chapter is based on the research article from Lau, et al., 2018. Finally, Chapter 5 describes an alternative approach to estimating metabolic scaling exponents using the branching architecture derived from TLS point clouds. This approach does not rely on destructive sampling and can help to increase data collection. A theory on metabolic scaling, the West, Brown & Enquist (WBE) theory, suggests that metabolic rate and other biological functions have their origins in an optimal branching system network (among other assumptions).
Research involved four TLS fieldwork campaigns across three tropical regions (Figure 2). The first one, in the , in the southwest region of Madre de Dios in Peru is featured in Chapter 2. The second fieldwork campaign, also featured in Chapter 2, is located in a peat swamp forest in central Kalimantan, Indonesia. The third fieldwork campaign is located in a lowland tropical moist forest in central Guyana. The third fieldwork campaign is located in a lowland tropical moist forest in central Guyana.
The results from this thesis provide a scientific contribution to the current development of new methods using terrestrial LiDAR and 3D modelling in tropical forests. The results can potentially be used to generate significant quality data for a better understanding of ecological challenges in tropical forests. We encourage further testing of my work using more samples including other types of forests to reduce inherent uncertainties.
Thesis was successfully defended on 30th October 2018 and can be seen via wurTV.