Development of a Precise Tree Structure from LiDAR Point Clouds

A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%

Leaf and wood classification framework for terrestrial LiDAR point clouds

Leaf and wood separation is a key step to allow a new range of estimates from Terrestrial LiDAR data, such as quantifying above-ground biomass, leaf and wood area and their 3D spatial distributions. We present a new method to separate leaf and wood from single tree point clouds automatically. Our approach combines unsupervised classification of geometric features and shortest path analysis. The automated separation algorithm and its intermediate steps are presented and validated. Validation consisted of using a testing framework with synthetic point clouds, simulated using ray-tracing and 3D tree models and 10 field scanned tree point clouds. To evaluate results we calculated accuracy, kappa coefficient and F-score. Validation using simulated data resulted in an overall accuracy of 0.83, ranging from 0.71 to 0.94. Per tree average accuracy from synthetic data ranged from 0.77 to 0.89. Field data results presented and overall average accuracy of 0.89. Analysis of each step showed accuracy ranging from 0.75 to 0.98. F-scores from both simulated and field data were similar, with scores from leaf usually higher than for wood. Our separation method showed results similar to others in literature, albeit from a completely automated workflow. Analysis of each separation step suggests that the addition of path analysis improved the robustness of our algorithm. Accuracy can be improved with per tree parameter optimization. The library containing our separation script can be easily installed and applied to single tree point cloud. Average processing times are below 10min for each tree

Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests

Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package

Leaf and wood classification framework for terrestrial LiDAR point clouds

Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. Leaf and wood separation is a key step to allow a new range of estimates from Terrestrial LiDAR data, such as quantifying above-ground biomass, leaf and wood area and their 3D spatial distributions. We present a new method to separate leaf and wood from single tree point clouds automatically. Our approach combines unsupervised classification of geometric features and shortest path analysis. The automated separation algorithm and its intermediate steps are presented and validated. Validation consisted of using a testing framework with synthetic point clouds, simulated using ray-tracing and 3D tree models and 10 field scanned tree point clouds. To evaluate results we calculated accuracy, kappa coefficient and F-score. Validation using simulated data resulted in an overall accuracy of 0.83, ranging from 0.71 to 0.94. Per tree average accuracy from synthetic data ranged from 0.77 to 0.89. Field data results presented and overall average accuracy of 0.89. Analysis of each step showed accuracy ranging from 0.75 to 0.98. F-scores from both simulated and field data were similar, with scores from leaf usually higher than for wood. Our separation method showed results similar to others in literature, albeit from a completely automated workflow. Analysis of each separation step suggests that the addition of path analysis improved the robustness of our algorithm. Accuracy can be improved with per tree parameter optimization. The library containing our separation script can be easily installed and applied to single tree point cloud. Average processing times are below 10 min for each tree

Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

Separation of foliar and woody materials using remotely sensed data is crucial for the accurate estimation of leaf area index (LAI) and woody biomass across forest stands. In this paper, we present a new method to accurately separate foliar and woody materials using terrestrial LiDAR point clouds obtained from ten test sites in a mixed forest in Bavarian Forest National Park, Germany. Firstly, we applied and compared an adaptive radius near-neighbor search algorithm with a fixed radius near-neighbor search method in order to obtain both radiometric and geometric features derived from terrestrial LiDAR point clouds. Secondly, we used a random forest machine learning algorithm to classify foliar and woody materials and examined the impact of understory and slope on the classification accuracy. An average overall accuracy of 84.4% (Kappa = 0.75) was achieved across all experimental plots. The adaptive radius near-neighbor search method outperformed the fixed radius near-neighbor search method. The classification accuracy was significantly higher when the combination of both radiometric and geometric features was utilized. The analysis showed that increasing slope and understory coverage had a significant negative effect on the overall classification accuracy. Our results suggest that the utilization of the adaptive radius near-neighbor search method coupling both radiometric and geometric features has the potential to accurately discriminate foliar and woody materials from terrestrial LiDAR data in a mixed natural forest

Innovative surveying methodologies through Handheld Terrestrial LIDAR Scanner technologies for forest resource assessment

Precision Forestry is an innovative sector that is currently of great importance for forest and spatial planning. It enables complex analyses of forest data to be carried out in a simple and economical way and facilitates collaboration between technicians, industry operators and stakeholders, thus ensuring transparency in forestry interventions (Corona et al., 2017). The principles of "Precision Forestry" are to use modern tools and technologies with the aim to obtain as much real information as possible, to improve decision-making, and to ensure the current objectives of forest management. Thanks to the rapid technological developments in remote sensing during the last few decades, there have been remarkable improvements in measurement accuracy, and consequentially improvements in the quality of technical elaborations supporting planning decisions. During this period, several scientific publications have demonstrated the potential of the LIDAR system for measuring and mapping forests, geology, and topography in large-scale forest areas. The LIDAR scans obtained from the TLS and HLS systems provide detailed information about the internal characteristics of tree canopys, making them an essential tool for studying stem allometry, volume, light environments, photosynthesis, and production models. In light of these considerations, this thesis aims to expand the current knowledge on the terrestrial LIDAR system applications for monitoring forest ecosystems and dynamics by providing insight on the feasibility and effectiveness of these systems for forest planning. In particular, this study fills a gap in the literature regarding practical examples of the use of innovative technologies in forestry. The main themes of this work are: A) The strengths and weaknesses of the mobile LIDAR system for a forest company; B) The applicability and versatility of the LIDAR HLS tool for sustainable forest management applications; C) Single tree analysis from HLS LIDAR data.   To investigate these themes, we analyzed six cases studies: 1) An investigation of the feasibility and efficiency of LIDAR HLS scanning for an accurate estimation of forest structural attributes by comparing scans using the LIDAR HLS survey method (Handheld Mobile Laser Scanner) to traditional instruments; 2) An examination of walking scan path density’s influence on single-tree attribute estimation by HMLS, taking into account the structural biodiversity of two forest ecosystems under examination, and an estimation of the cost-effectiveness of each type of laser survey based on the path scheme considered; 3) A study of how LIDAR HLS surveys can contribute to fire prevention interventions by providing a quantitative classification of fuels and a preliminary description of the structural and spatial development of the forest in question; 4) An application of a method for assessing and rating stem straightness in tree posture using LIDAR HLS surveys to quantify differences between stands of different log qualities; 5) The identification of features of a Mediterranean old-growth forest using LIDAR HLS surveys according to the criteria established in the literature; 6) The extrapolation of dimensional information for Ficus macrophylla subsp. columnaris to identify the monumental character of the tree by comparing the most appropriate LIDAR HLS point cloud processing methodologies and estimating the total volume of individual trees. In conclusion, the results of these cases studies are useful to determine new research aspects within the system in the forest environment by applying recently published analysis methodologies and indications of relevant terrestrial LIDAR methodologies

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