A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants from Terrestrial Laser Scanning Time Series

Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset

A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series

Terrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset

Automated in-situ laser scanner for monitoring forest Leaf Area Index.

An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest Leaf Area Index (LAI) from satellite sensors. The scanning lidar instrument was developed primarily from low cost, commercially accessible components. While the 635 nm wavelength lidar is not ideally suited to vegetation studies, there was an acceptable trade-off between cost and performance. Tests demonstrated reliable range estimates to live foliage up to a distance of 60 m during night-time operation. Given the instrument's scan angle of 57.5 degrees zenith, the instrument is an effective tool for monitoring LAI in forest canopies up to a height of 30 m. An 18 month field trial of three co-located instruments showed consistent seasonal trends and mean LAI of between 1.32 to 1.56 and a temporal LAI variation of 8 to 17% relative to the mean

Automated In-Situ Laser Scanner for Monitoring Forest Leaf Area Index

An automated laser rangefinding instrument was developed to characterize overstorey and understorey vegetation dynamics over time. Design criteria were based on information needs within the statewide forest monitoring program in Victoria, Australia. The ground-based monitoring instrument captures the key vegetation structural information needed to overcome ambiguity in the estimation of forest Leaf Area Index (LAI) from satellite sensors. The scanning lidar instrument was developed primarily from low cost, commercially accessible components. While the 635 nm wavelength lidar is not ideally suited to vegetation studies, there was an acceptable trade-off between cost and performance. Tests demonstrated reliable range estimates to live foliage up to a distance of 60 m during night-time operation. Given the instrument’s scan angle of 57.5 degrees zenith, the instrument is an effective tool for monitoring LAI in forest canopies up to a height of 30 m. An 18 month field trial of three co-located instruments showed consistent seasonal trends and mean LAI of between 1.32 to 1.56 and a temporal LAI variation of 8 to 17% relative to the mean

Assessment of forest canopy vertical structure with multi-scale remote sensing: from the plot to the large area

Assessment of vegetation over large, remote and inaccessible areas is an ongoing challenge for land managers in Australia and around the world. This research aimed to develop metrics, techniques and acquisition specifications that are suitable for characterising vegetation across large forested areas. New methods were therefore required to be transferable between forest types as well as robust where forest structure is unknown a priori. Remote sensing techniques were utilised as they have been previously identified as key in forest assessment, owing to their synoptic capture as well as relative cost. Additionally, active remote sensing instruments, such as LiDAR, are capable of sensing 3-dimensional canopy structure. Canopy height and the canopy height profile are fundamental descriptors of forest structure and can be used for estimating biomass, habitat suitability and fire susceptibility. To investigate the ability of remote sensing to characterise vegetation structure across large areas, three key research questions were formulated: I. Which metrics of canopy height and vertical canopy structure are suitable for application across forested landscapes? II. What is the appropriate ALS sampling frequency for attribution of forest structure across different forest types? III. How can plot level estimates of canopy structure be scaled to generate continuous large area maps? A number of inventory measured canopy height metrics were compared with LiDAR analogues, these were shown to be accurate at estimating canopy height and transferable between forest types. Existing techniques for attributing the canopy height profile were found to be inadequate when applied across heterogeneous forests. Therefore a new technique was developed that utilised a nonparametric regression of LiDAR derived gap probability that identified major canopy features e.g. dominant canopy strata and shade tolerant layers beneath. The impact of sampling frequency was assessed using three key descriptors of canopy structure at six sites across Australia covering a range of forest types. The research concluded that forest structure can be adequately characterised with a pulse density of 0.5 pulses m-2 when compared to a higher density acquisition - 10 pulses m-2. At pulse density of <0.5 pulses m-2, the inability to generate an adequate ground surface model lead to poor results, particularly in high biomass forest. The outcomes of this research will allow land managers to specify lower pulse densities when commissioning LiDAR capture, which may result in significant cost savings. Finally, LiDAR derived plot estimates were scaled to an area of 2.9 million hectares of forest, where forest type ranged from short, open woodland to tall, closed canopy rainforest. Attribution was achieved using a two-stage sampling approach utilising the ensemble regression technique Random Forest. Predictor variables included freely available datasets such as Landsat TM and MODIS satellite imagery. Canopy height was estimated with a RMSE of 30% or ~5.5 m when validated with an independent forest inventory dataset. Attribution of the canopy height profile was less successful for a number of reasons, for example, the relatively high spatial variability of shade tolerant vegetation. Inclusion of additional synoptic datasets, such as radar, may improve this in the future

Assessing biomass and architecture of tropical trees with terrestrial laser scanning

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 thesis, 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 thesis, 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, I infer AGB, develop allometric models, and estimate metabolic plant scaling of individual tropical trees. All these metrics are validated against a traditional forest inventory data and destructively harvested trees. 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. The accurate estimation of AGB of large tropical trees (diameter > 70 cm) is particularly relevant due to their major influence on tropical forest AGB variation. Nevertheless, current allometric models have large uncertainties for large tree AGB, partly due to the relative lack of large trees in the empirical datasets used to create them. The key result of this chapter is that TLS and 3D modelling are able to provide individual large tree volume and AGB estimates that are less likely to be biased by tree size or structural irregularities, and are more accurate than allometric models. 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. Current tropical forest AGB estimates typically rely on pantropical allometric models that are developed with relatively few large trees. This leads to large uncertainties with increasing tree size and often results in an underestimation of AGB for large trees. I showed in Chapter 2 that AGB of individual large trees can be estimated regardless of their size and architecture. This chapter evaluates the performance of my local allometric models against existing pantropical models and evidenced that inclusion of TLS-based metrics to build allometric models provides as good as, or even better, AGB estimates than current pantropical models. Chapter 4 provides an insight into the architecture and branching structure of tropical trees. In Chapter 2, I demonstrated the potential of TLS to characterize woody tree structure as a function of tree volume, but little is known regarding their detailed architecture. Previous studies have quantitatively described tree architectural traits, but they are limited to the intensity of quantifying tree structure in-situ with enough detail. Here, I analysed the length and diameter of individual branches, and compared them to reference measurements. I demonstrated that basic tree architecture parameters could be reconstructed from large branches (> 40 cm diameter) with sufficient accuracy. I also discuss the limitations found when modelling small branches and how future studies could use my results as a basis for understanding tree architecture. 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). This chapter demonstrates that architecture-based metabolic scaling can be estimated for big branches of tropical trees with some limitations and provides an alternative method that can be implemented for large-scale assessments and provides better understanding of metabolic scaling. 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. I encourage further testing of my work using more samples including other types of forests to reduce inherent uncertainties.</p