Terrestrial Laser Scanning Data Research Articles

Treegraph: tree architecture from terrestrial laser scanning point clouds

AbstractAccurate quantification of tree architecture is critical to interpreting the growth, health and functioning of trees and forests. Terrestrial laser scanning (TLS) offers millimetre‐level point cloud data, but current approaches to 3D tree reconstruction from TLS point clouds primarily focus on retrieving total volume at tree scale for aboveground biomass (AGB) estimation. Few methods have been designed specifically to provide tree architectural properties, including branch‐level morphology and topology, rather than AGB; derived topological traits have tended to be a compromise, and of secondary importance to volume. We present Treegraph, a new approach explicitly designed to retrieve the architectural traits of trees at multiple scales, from the whole tree scale down to individual branches and internodes, using TLS data with limited assumptions about tree form. It provides morphological traits such as branch length and diameter, alongside topological traits including parent–daughter connections of branches and internodes, furcation (branching) number and branch order. We compare Treegraph‐derived morphological and topological traits with manual measurements of branches from eight destructively harvested trees, yielding RMSE values of 0.60 m (5.96%) for branch length, 2.99 cm (33.45%) for branch diameter, 0.46 (19.38%) for furcation number and 0.08 m (33.16%) for internode length, respectively. In a broader application to 603 trees from tropical, temperate and urban forests, we demonstrate that the derived morphological and topological traits support testing of structure‐related metabolic scaling theories. Testing branches over 10 cm in diameter across 18 657 branching nodes shows that Treegraph‐derived branch‐level scaling exponents deviate from WBE predictions, exhibiting area‐preserving behaviour while displaying asymmetry in length and diameter of daughter branches. Available as open‐source Python software, Treegraph provides fine‐level branching network information, promoting improved insights into tree structure and function. This data‐driven approach reduces the need for empirical heuristic parameters, which has the potential for advancing large‐scale ecological studies on tree architecture.

Combining terrestrial lidar with single line transects to investigate geomorphic change: A case study on the Upper Verde River, Arizona

The Upper Verde River in northern Arizona, USA is a vital resource for the wildlife and humans that rely on its waters. We characterize the riparian corridor topography using terrestrial laser scanner (TLS) data from 2021 to 2022. We also quantify geomorphic changes associated with human and climate-driven alterations in river flow and vegetation changes by combining the contemporary lidar surveys with legacy measurements from single line geomorphology transects measured by the United States Forest Service (USFS) in 2009. Seventeen plots along the Upper Verde River were surveyed with the TLS and the data were coregistered within individual plots with a Root Mean Square Error of <0.03 m among scan positions. Digital Elevation Models (DEM) were derived for each plot from the TLS data at 10 cm resolution and compared to the 2009 USFS cross-section data to quantify elevation changes. In areas with statistically significant change, we detected maximum changes in elevation due to erosion and deposition of −0.37 m and + 0.97 m, respectively. Topographic changes over the 13-year period were predominately aggradation and associated with sediment deposition, which we hypothesize might have resulted from altered river flow and vegetation encroachment. This study also demonstrates a quantitative and statistical methodology to fuse traditional single line cross-section data with contemporary lidar data to quantify geomorphic change. The novel approach demonstrated here is broadly applicable to natural resource managers for integrating and contextualizing legacy topographic data for understanding past, present, and future landscape and habitat changes.

A Study on Three-Dimensional Modelling of the Baitul Ilmi Mosque's Itera Using Terrestrial Laser Scanner Technology

As advancements in three-dimensional (3D) modeling continue to progress, the demand for precise and efficient measurement techniques has led to significant innovations in the methods and tools used for creating 3D models. Among these, the utilization of Terrestrial Laser Scanners (TLS) has become increasingly popular. This study focuses on the integration of TLS and total station technologies for 3D modeling of a mosque. The total station was employed to conduct five sets of perfectly bonded closed polygon measurements, each consisting of five polygon points, from which three marker points were derived. Concurrently, the TLS was used to perform a comprehensive scan of the mosque from 17 different positions. The data obtained from the TLS scan provided a detailed 3D model, while the total station measurements served as control points to ensure the model's global coordinate accuracy. The processing of the closed polygon data was executed using the Bowditch method, and the TLS data was registered using the cloud-to-cloud method. This approach yielded an overall quality assessment, including a bundle error of 0.008 m, an overlap of 27%, a strength of 65%, and a cloud-to-cloud discrepancy of 0.008 m. The integration of point clouds with total station coordinates resulted in an error margin of 0.160 m. Geometric validation of the model was conducted by comparing its dimensions to measurements obtained using a measuring tape, which yielded a Root Mean Square Error (RMSE) of 0.045 m.

Branch architecture quantification of large-scale coniferous forest plots using UAV-LiDAR data

Branch architecture plays an important role in forest physical and ecological processes, greatly affecting forest spatial structure and the accumulation, production and distribution of organic carbon. Very few studies have quantified the branch architecture of large-scale forest plots, due to the lack of high-resolution large-scale three-dimensional data. The emergence of unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) provides great potential to overcome this limitation. However, due to insufficient quality of UAV-LiDAR data for branch reconstruction, existing algorithms remain tremendously challenging. We propose an effective branch reconstruction algorithm for UAV-LiDAR data to quantify branch architecture. First, individual branches are extracted using a region growth method that is guided by the branch growth direction and local smoothness constraint. Second, incorrect branches are eliminated based on three pieces of branch features, i.e., specific angles between branches at the same whorl, specific height differences between branches at different whorls, and the growth pattern of branches from the stem outward, reaching maximum distances at tips. Finally, branches are reconstructed using polynomial fitting, and branch architecture parameters are extracted based on branch models. The proposed algorithm simplifies branch reconstruction by skipping the separation of photosynthetic and non-photosynthetic components. It also adapts well to UAV-LiDAR point clouds, generating more realistic reconstructions. The algorithm successfully identified non-photosynthetic components in experiments involving 240 trees, including Scots pine, Norway spruce, and Radiata pine. The average overall accuracy for these three species was 0.88, 0.76, and 0.74, respectively. The proposed algorithm was tested for branch reconstruction using UAV-LiDAR data from a two-hectare Larix principis-rupprechtii plot. The accuracy of branch identification and parameter extraction accuracy were evaluated branch by branch based on the individual branches manually identified in terrestrial laser scanning data. Results showed the F-score of branch and stem identification was 0.58 and 1. The relative RMSE of branch length and angle was 36.87% and 18.3%, and ones of stem length and diameter were 1.46% and 6.16%. The proposed algorithm outperformed the well-known reconstruction algorithms, including TreeQSM, AdTree and Laplacian-based algorithms.

Determining the trend of geometrical changes of a hydrotechnical object based on data in the form of LiDAR point clouds

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article.

Digital 3D Reconstruction of Ancient Chinese Great Wild Goose Pagoda by TLS Point Cloud Hierarchical Registration

Digital reconstruction of ancient buildings is very challenging due to its architectural complexity and structural delicacy. Therefore, how to apply new Earth Observation (EO) technology to digitally reconstruct the complete and real model of ancient buildings without damage has become an urgent issue. This article proposes a multi-scan point cloud hierarchical registration method for 3D reconstruction of the ancient Chinese Great Wild Goose Pagoda based on Terrestrial Laser Scanner (TLS) data. First, a multi-scan point cloud hierarchical registration method is elaborately designed for 3D reconstruction. Then, the outline feature of the ancient Chinese Great Wild Goose Pagoda is automatically extracted by point cloud slicing. Finally, fine modeling is carried out according to the outline feature extracted from the reconstructed point cloud, and after that block combination and texture mapping are conducted for 3D digitalization and documentation. The experiments indicate that the Root Mean Square Error (RMSE) of multi-scan hierarchical registration of the translation and rotation are less than 3 cm and 0.862 ∘ , respectively, and the complete and real model of the Great Wild Goose Pagoda is obtained by fine modeling and texture mapping, which provides an important reference for 3D digital protection of ancient buildings.

Intricacies of Opening Geometry Detection in Terrestrial Laser Scanning: An Analysis Using Point Cloud Data from BLK360

This study investigates the use of terrestrial laser scanning (TLS) in urban excavation sites, focusing on enhancing ground deformation detection by precisely identifying opening geometries, such as gaps between pavement blocks. The accuracy of TLS data, affected by equipment specifications, environmental conditions, and scanning geometry, is closely examined, especially with regard to the detection of openings between blocks. The experimental setup, employing the BLK360 scanner, aimed to mimic real-world paving situations with varied opening widths, allowing an in-depth analysis of how factors related to scan geometry, such as incidence angles and opening orientations, influence detection capabilities. Our examination of various factors and detection levels reveals the importance of the opening width and orientation in identifying block openings. We discovered the crucial role of the opening width, where larger openings facilitate detection in 2D cross-sections. The overall density of the point cloud was more significant than localized variations. Among geometric factors, the orientation of the local object geometry was more impactful than the incidence angle. Increasing the number of laser beam points within an opening did not necessarily improve detection, but beams crossing the secondary edge were vital. Our findings highlight that larger openings and greater overall point cloud densities markedly improve detection levels, whereas the orientation of local geometry is more critical than the incidence angle. The study also discusses the limitations of using a single BLK360 scanner and the subtle effects of scanning geometry on data accuracy, providing a thorough understanding of the factors that influence TLS data accuracy and reliability in monitoring urban excavations.

Building a Mycenaean chamber tomb catalogue from terrestrial laser scan data

Detailed 3D information on vulnerable archaeological sites can document cultural heritage and contribute to its preservation. The Late Bronze Age Mycenaean cemetery of Aidonia, Greece, is a representative case of a vulnerable site. Tomb looting has occurred sporadically since the 1970s, when the Greek government was made aware of the site. Anthropogenic activities and natural denudation may affect the loss of structural integrity of tombs. In this contribution, terrestrial laser scanning and geosciences are combined to document the vulnerable cemetery through the generation of a tomb catalogue. The emphasis is on techniques applied to point clouds to extract architectural elements. The catalogue consists of 208 architectural and geological measurements, 112 qualitative observations, maps, and point clouds images displaying the architecture of 16 tombs. The tombs are mainly orientated northeast-southwest and northwest-southeast, and their average total length is 13 m. The average volume of chambers with preserved roofs is 46 m3.

Accurate estimation of concrete consumption in tunnel lining using terrestrial laser scanning

Accurate estimation of concrete (including shotcrete) consumption plays a crucial role in tunnel construction. A novel method has been introduced to accurately estimate concrete consumption with terrestrial laser scanning (TLS). The estimation needs to capture TLS data of tunnel surfaces at different stages of construction. Unrolling point clouds, a novel two-stage algorithm consisting of noise removal and hole filling has been used to generate resampled points. Furthermore, resampled points from two scans (before and after lining construction) ultimately generate an innovative computation model composed of multiple hexahedral elements, which is used for calculating volumes. The proposed technique was applied to the Tiantaishan highway tunnel and Da Fang Shan high-speed railway tunnel. The calculation relative error of the rebound rate is 0.19%, and the average relative error in predicting the demand for secondary lining concrete is 0.15%. Compared with 3D Delaunay with curve fitting, the proposed technique offers a more straightforward operation and higher accuracy. Considering factors such as tunnel geometry, support design, and concrete properties, a computational model will provide valuable insights into optimizing resource allocation and reducing material waste during construction.

3D modeling and volume measurement of bulk grains stored in large warehouses using bi-temporal multi-site terrestrial laser scanning data

Terrestrial laser scanning (TLS) is a promising technology for quantity checking huge grain stocks with low cost, light workload and high efficiency. Existing applications of TLS in bulk grain measurement and quantification lack the ability to capture complete structural information of grain bulks and thus will result in large errors. In this paper, we propose a bi-temporal TLS scheme for fast 3D modeling and accurate volume measurement of bulk grains stored in large warehouses. The scheme uses bi-temporal multi-site TLS datasets acquired under both empty and full or high loading conditions to obtain complete surface information about grain bulk’s structure. In order for a grain bulk’s all external surfaces and the 3D volumetric model reconstructed therefrom to be automatically derived from the bi-temporal TLS dataset, several dedicated methods are developed for the scheme. A fully automated marker-free strategy exploring structurally semantic information inherent in the large grain storehouses is adopted to register multi-scan TLS point cloud data captured in large-scale granary scenes. Also, a local minima-based region growing technique is devised to extract underlying surfaces from a granary scene point cloud model. Experiments show that the proposed 3D modeling and volume measurement scheme can work effectively and quickly in TLS-based granary field applications and repeated test data demonstrate its correctness, repeatability and accuracy. Compared with the conventional manual measurement approach, the bi-temporal TLS scheme can not only achieve much higher measurement precision, but also greatly improve efficiency by significantly reducing cost, workload, and manpower. It has good potential for use in the area of nation-wide grain inventory inspection in China.

Numerical simulations of recent and future evolution of monte perdido glacier

Glaciers are globally retreating due to climate change, and the Pyrenees Mountain range is no exception. This study uses the Open Global Glacier Model (OGGM) to explore the dynamics of the Monte Perdido glacier, one of the largest remaining glaciers in the Pyrenees. We explored three calibration approaches to assess their performances when reproducing observed volume decreases. The first approach involved mass balance calibration using terrestrial laser scanning data from 2011 to 2022 and climate data from a nearby weather station. The second approach used terrestrial laser scanning calibration with default climate data provided by OGGM (GSWP3-W5E5). The third approach used default geodetic mass balance calibration and default climate data. By comparing these calibration strategies and analysing historical data (terrestrial laser scanning and ground penetrating radar), we obtain insights of the applicability of OGGM to this small, mild conditions, Pyrenean glacier. The first calibration approach is identified as the most effective, emphasising the importance of selecting appropriate climate data and calibration methods. Additionally, we conducted future volume projections using an ensemble of General Circulation Models (GCMs) under the RCP2.6 and RCP8.5 scenarios. The results indicate a potential decrease in total ice volume ranging from 91.60% to 95.16% by 2100, depending on the scenario. Overall, this study contributes to the understanding of the Monte Perdido glacier’s behaviour and its response to climate change through the calibration of the OGGM, while also providing the first estimate of its future melting under different emission scenarios.

Limitations of estimating branch volume from terrestrial laser scanning

Quantitative structural models (QSMs) are frequently used to simplify single tree point clouds obtained by terrestrial laser scanning (TLS). QSMs use geometric primitives to derive topological and volumetric information about trees. Previous studies have shown a high agreement between TLS and QSM total volume estimates alongside field measured data for whole trees. Although already broadly applied, the uncertainties of the combination of TLS and QSM modelling are still largely unexplored. In our study, we investigated the effect of scanning distance on length and volume estimates of branches when deriving QSMs from TLS data. We scanned ten European beech (Fagus sylvatica L.) branches with an average length of 2.6 m. The branches were scanned from distances ranging from 5 to 45 m at step intervals of 5 m from three scan positions each. Twelve close-range scans were performed as a benchmark. For each distance and branch, QSMs were derived. We found that with increasing distance, the point cloud density and the cumulative length of the reconstructed branches decreased, whereas individual volumes increased. Dependent on the QSM hyperparameters, at a scanning distance of 45 m, cumulative branch length was on average underestimated by − 75%, while branch volume was overestimated by up to + 539%. We assume that the high deviations are related to point cloud quality. As the scanning distance increases, the size of the individual laser footprints and the distances between them increase, making it more difficult to fully capture small branches and to adjust suitable QSMs.

Insights into the relationship between hydraulic safety, hydraulic efficiency and tree structural complexity from terrestrial laser scanning and fractal analysis

Key messageThis research focused on the interplay between tree structural complexity and drought tolerance, unraveling the crucial role of Db as an indicator of hydraulic efficiency and vulnerability in several tree species.The potential of trees to adapt to drier and hotter climates will determine the future state of forests in the wake of a changing climate. Attributes connected to the hydraulic network are likely to determine a tree’s ability to endure drought. However, how a tree’s architectural attributes related to drought tolerance remains understudied. To fill this gap, we compared the structural complexity of 71 trees of 18 species obtained from terrestrial laser scanning (TLS) with key hydraulic thresholds. We used three measures of xylem safety, i.e., the water potential at 12%, 50%, and 88% loss of hydraulic conductance (P12, P50, P88) and specific hydraulic conductivity (Ks) to assess the trees’ drought tolerance. TLS data were used to generate 3D attributes of each tree and to construct quantitative structure models (QSMs) to characterize the branching patterns. Fractal analysis (box-dimension approach) was used to evaluate the overall structural complexity of the trees (Db) by integrating horizontal and vertical extent as well as internal branching patterns. Our findings revealed a significant relationship between the structural complexity (Db) and the three measures of xylem safety along with Ks. Tree species with low structural complexity developed embolism-resistant xylem at the cost of hydraulic efficiency. Our findings also revealed that the Db had a stronger and more significant relationship with branch hydraulic safety and efficiency compared to other structural attributes examined. We conclude that Db seems to be a robust descriptor of tree architecture that relates to important branch hydraulic properties of a tree.

Modeling a new taper curve and form factor of tree branches using terrestrial laser scanning

Abstract Modeling branch taper curve and form factor contributes to increasing the efficiency of tree crown reconstructions: the branch taper, defined as the sequential measure of diameters along the course of the branch, is pivotal to accurately estimate key branch variables such as biomass and volume. Branch diameters or volumes have commonly been estimated from terrestrial laser scanning (TLS) based on automatized voxelization or cylinder-fitting approaches, given the whole branch length is sufficiently covered by laser reflections. The results are, however, often affected by ample variations in point cloud characteristics caused by varying point density, occlusions, and noise. As these characteristics of TLS can hardly be sufficiently controlled or eliminated in automatized techniques, we proposed a new branch taper curve model and form factor, which can be employed directly from the laser reflections and under variable point cloud characteristics. In this paper, the approach is demonstrated on primary branches using a set of TLS-derived datasets from a sample of 20 trees (six species). The results showed an R2 of 0.86 and a mean relative absolute error of 1.03 cm (29%) when validated with field-measured diameters. The approach improved the accuracy of diameter estimates for the fine branch scales (&amp;lt;10 cm) as compared to the quantitative structural model (QSM). Our approach also allowed branch diameter estimation for a relatively larger number of manually recognized primary branches (&amp;gt;85%) from point clouds when validated with panoramic images acquired simultaneously with laser scanning. Frequently used automatized crown reconstructions from QSM, on the other hand, were affected by gaps in the point clouds due to obstruction, with the crown-tops and finer branches being the most critical. Our approach reports mean form factors across the examined species of 0.35 and 0.49, with the diameters determined at 5% and 10% of the total branch length, respectively. Our approach may have the potential to produce branch volume information with reasonable accuracy from only knowing the length and respective diameter of each branch. Our model delivers a first approximation for the taper curve and form factor for primary branches but was developed on a relatively small set of samples. We believe that our approach holds the potential to improve the accuracy of the assessment of branch diameter and volume from TLS data. The approach may also be extended to other branch orders. This could expand the horizon for volumetric calculations and biomass estimates from non-destructive TLS proxies in tree crowns.

Using multiplatform LiDAR to identify relationships between vegetation structure and the abundance and diversity of woodland reptiles and amphibians

AbstractRemotely sensed measures of vegetation structure have been shown to explain patterns in the occurrence and diversity of several animal taxa, including birds, mammals, and invertebrates. However, very little research in this area has focused on reptiles and amphibians (herpetofauna). Moreover, most remote sensing studies on animal–habitat associations have relied on airborne or satellite data that provide coverage over relatively large areas but may not have the resolution or viewing angle necessary to measure vegetation features at scales that are meaningful to herpetofauna. Here, we combined terrestrial laser scanning (TLS), unmanned aerial vehicle laser scanning (ULS), and fused (FLS) data to provide the first test of whether vegetation structural attributes can help explain variation in herpetofauna abundance, species richness, and diversity across a woodland landscape. We identified relationships between the abundance and diversity of herpetofauna and several vegetation metrics, including canopy height, skewedness, vertical complexity, volume of vegetation, and coarse woody debris. These relationships varied across species, groups, and sensors. ULS models tended to perform as well or better than TLS or FLS models based on the methods we used in this study. In open woodland landscapes, ULS data may have some benefits over TLS data for modeling relationships between herpetofauna and vegetation structure, which we discuss. However, for some species, only TLS data identified significant predictor variables among the LiDAR‐derived structural metrics. While the overall predictive power of models was relatively low (i.e., at most R2 = 0.32 for ULS overall abundance and R2 = 0.32 for abundance at the individual species level [three‐toed skink (Chalcides striatus)]), the ability to identify relationships between specific LiDAR structural metrics and the abundance and diversity of herpetofauna could be useful for understanding their habitat associations and managing reptile and amphibian populations.

A Laser Point Area Model for Canopy Gap Fraction Retrieval Based on Terrestrial Laser Scanning Data

A Laser Point Area Model for Canopy Gap Fraction Retrieval Based on Terrestrial Laser Scanning Data

Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning

Vertical forest structure is closely linked to multiple ecosystem characteristics, such as biodiversity, habitat, and productivity. Mixing tree species in planted forests has the potential to create diverse vertical forest structures due to the different physiological and morphological traits of the composing tree species. However, the relative importance of species richness, species identity and species interactions for the variation in vertical forest structure remains unclear, mainly because traditional forest inventories do not observe vertical stand structure in detail. Terrestrial laser scanning (TLS), however, allows to study vertical forest structure in an unprecedented way. Therefore, we used TLS single scan data from 126 plots across three experimental planted forests of a large-scale tree diversity experiment in Belgium to study the drivers of vertical forest structure. These plots were 9–11 years old young pure and mixed forests, characterized by four levels of tree species richness ranging from monocultures to four-species mixtures, across twenty composition levels. We generated vertical plant profiles from the TLS data and derived six stand structural variables. Linear mixed models were used to test the effect of species richness on structural variables. Employing a hierarchical diversity interaction modelling framework, we further assessed species identity effect and various species interaction effects on the six stand structural variables. Our results showed that species richness did not significantly influence most of the stand structure variables, except for canopy height and foliage height diversity. Species identity on the other hand exhibited a significant impact on vertical forest structure across all sites. Species interaction effects were observed to be site-dependent due to varying site conditions and species pools, and rapidly growing tree species tend to dominate these interactions. Overall, our results highlighted the importance of considering both species identity and interaction effects in choosing suitable species combinations for forest management practices aimed at enhancing vertical forest structure.

Multifiltering Algorithm for Enhancing the Accuracy of Individual Tree Parameter Extraction at Eucalyptus Plantations Using LiDAR Data

Accurately quantifying individual tree parameters is a critical step for assessing carbon sequestration in forest ecosystems. However, it is challenging to gather comprehensive tree point cloud data when using either unmanned aerial vehicle light detection and ranging (UAV-LiDAR) or terrestrial laser scanning (TLS) alone. Moreover, there is still limited research on the effect of point cloud filtering algorithms on the extraction of individual tree parameters from multiplatform LiDAR data. Here, we employed a multifiltering algorithm to increase the accuracy of individual tree parameter (tree height and diameter at breast height (DBH)) extraction with the fusion of TLS and UAV-LiDAR (TLS-UAV-LiDAR) data. The results showed that compared to a single filtering algorithm (improved progressive triangulated irregular network densification, IPTD, or a cloth simulation filter, CSF), the multifiltering algorithm (IPTD + CSF) improves the accuracy of tree height extraction with TLS, UAV-LiDAR, and TLS-UAV-LiDAR data (with R2 improvements from 1% to 7%). IPTD + CSF also enhances the accuracy of DBH extraction with TLS and TLS-UAV-LiDAR. In comparison to single-platform LiDAR (TLS or UAV-LiDAR), TLS-UAV-LiDAR can compensate for the missing crown and stem information, enabling a more detailed depiction of the tree structure. The highest accuracy of individual tree parameter extraction was achieved using the multifiltering algorithm combined with TLS-UAV-LiDAR data. The multifiltering algorithm can facilitate the application of multiplatform LiDAR data and offers an accurate way to quantify individual tree parameters.

UNSUPERVISED STATISTICAL APPROACH FOR TREE-LEVEL SEPARATION OF FOLIAGE AND NON-LEAF COMPONENTS FROM POINT CLOUDS

Abstract. Accurately classifying foliage and non-leaf components in point clouds is essential for remote sensing forest applications. Existing methods rely on radiometric attributes or local geometric features, often requiring time-consuming manual labelling. In this paper, we propose a statistical approach using flexible thresholds determined based on tree-level geometric features. Selected features (local anisotropy, curvature, linearity, first principal component, verticality, and sphericity) have shown robustness in earlier studies. Threshold values are identified as points of inflection in the fitted distributions for each tree. Our method requires only two parameters and was tested with manually labelled Terrestrial Laser Scanning (TLS) data and non-labeled data from an oblique and above canopy setup (Permanent LiDAR scanner setup). We tested two boreal tree species, Scots Pine, and silver birch, with 28 trees in total (14 trees for each species) using two data sources. Compared to two alternative methods (namely, fixed thresholding and CANUPO), our approach consistently outperforms in terms of recall. We achieved an average overall accuracy of 85%, recall of 88.5%, precision of 83%, and f1 score of 85%. Visually assessing oblique and above canopy results, our algorithm effectively captures tree structures. Our statistical approach provides an effective solution for foliage and non-leaf separation, with processing times of less than five minutes for individual tree point clouds containing up to 2 million points and no need for extensive manual labelling or parameter adjustments.

MEASURING FOREST CANOPY WATER MASS IN THREE DIMENSIONS USING TERRESTRIAL LASER SCANNING

Abstract. Canopy water mass is an important plant characteristic that can indicate the water status of vegetation. However, the parameter remains under-investigated because measuring it requires defoliating the canopy. This study introduced a non-destructive approach to estimate canopy water mass using terrestrial laser scanning data. Tree 3D models were generated from dual-wavelength TLS data for six forest canopies, then the models were utilized in estimating the canopy LAI, total leaf area, and vertical profiles of canopy leaf area. The estimates were then coupled with canopy equivalent water thickness estimates and vertical profiles of canopy water mass were generated. The results revealed some over- and underestimation in the estimated LAI, but the obtained accuracy was considered sufficient as leaf-on point clouds were used to generate the 3D models. The vertical profiles of canopy water mass showed that the leaf area distribution within the canopy, and the canopy architecture were the main parameters affecting the water mass distribution within the canopy, with mid canopy layers having higher water mass than the other canopy layers. This study showed the potential of TLS to estimate canopy water mass, but controlled experiments that include defoliating canopies are still needed for a direct and accurate validation of the TLS estimates of canopy water mass.