Reply on RC1

Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as Digital Hemispherical Photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial Laser Scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of five species in Mediterranean forests, we compare three TLS methods (LiDAR Pulse, 2D Intensity Image and Voxel-Based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the LiDAR Pulse method agrees most closely with DHP, but is limited to single scan data so cannot determine individual tree α. The Voxel-Based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the Voxel-Based method, we show that species explain some variation in α, however, height and density were stronger predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function, but also the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods, and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large scale monitoring of PAI and LAI.

The accurate estimation of tree attributes is essential for sustainable forest management. Terrestrial Laser Scanning (TLS) is a viable remote sensing technology suitable for estimating under canopy structure. However, TLS measurements generally underestimate tree height in taller trees, which leads to the underestimation of other tree attributes (e.g., stem volume). The integration of information derived from TLS and Unmanned Aerial Vehicle (UAV) photogrammetry could potentially improve tree height estimation. This study investigated the applicability of integrating TLS and UAV photogrammetry to estimate individual tree attributes in managed coniferous forests of Japan. Diameter at breast height (DBH), tree height, and stem volume were estimated by (1) TLS data only, (2) integrating TLS and UAV data with TLS tree locations, and (3) integrating TLS and UAV data with treetop detections of the tree canopy. The TLS data only approach achieved high accuracy for DBH estimations with a root mean squared error (RMSE) of 2.36 cm (RMSE% 5.6%); however, tree height was greatly underestimated, with an RMSE of 8.87 m (28.9%) and a bias of −8.39 m. Integrating TLS and UAV photogrammetric data improved tree height estimation accuracy for both the TLS tree location (RMSE of 1.89 m and a bias of −0.46 m) and the treetop detection (RMSE of 1.77 m and a bias of 0.36 m) approaches. Integrating TLS and UAV photogrammetric data also improved the accuracy of the stem volume estimations with RMSEs of 0.21 m3 (10.8%) and 0.21 m3 (10.5%) for the TLS tree location and treetop detection approaches, respectively. Although the tree height of suppressed trees tended to be overestimated by TLS and UAV photogrammetric data integration, a good performance was obtained for dominant trees. The results of this study indicate that the integration of TLS and UAV photogrammetry is beneficial for the accurate estimation of tree attributes in coniferous forests.

Northern peatlands are major terrestrial carbon sinks, storing 415 ± 150 Gt of carbon. The composition of peatland vegetation affects this carbon storage capacity, and thus quantifying the vegetation helps to constrain uncertainty in peatland carbon storage estimates. Ground layer vegetation, such as Sphagnum sp. moss contributes greatly to carbon storage capacity. In forested peatlands, the tree canopy structure directly influences peatland solar insolation, soil temperature, and water table levels. Each of these factors impacts the ground layer vegetation. Currently, there is uncertainty about how the peatland tree canopy structure is influenced by elevated levels of carbon dioxide (CO2) and temperature. Providing canopy structural metrics in a nondestructive, spatially comprehensive way across different temperature and CO2 treatments is challenging for traditional methods such as destructive harvesting, Digital Hemispherical Photography (DHP), and allometric regressions. Terrestrial Laser Scanning (TLS) is well-suited to provide non-destructive detailed horizontal and vertical canopy structural information. As part of the Spruce and Peatland Responses Under Changing Environments (SPRUCE) study located in northern Minnesota, USA, we use TLS to evaluate leaf area index (LAI), leaf area density, and leaf inclination angle over time (2015 - 2022) and space of two conifer species, Picea mariana (black spruce) and Larix laricina (eastern larch). The SPRUCE site is in a treed peatland bog under elevated CO2 and temperature conditions. The research questions in this study are 1) How accurately can we predict the LAI of the spruce and larch trees using TLS data? 2) How are the spruce and larch tree canopy structures within 12 SPRUCE plots changing from 2015 - 2022? We expected 1) A volumetric pixel-based model (VCP) will predict LAI with an accuracy of 90% as validated by destructively harvested and DHP LAI estimates 2) Under elevated CO2 and temperature, LAI will increase, leaf area density will decrease in lower canopies, and leaf inclination angles will become more vertical. At the species level, we expected the spruce and larch trees to respond with opposing trends for each metric under the same treatment. Using TLS data, we developed a modified VCP model that uses measures of point contact frequency to estimate LAI, leaf inclination angles, and leaf area density. The results indicate that the model predicts LAI with a coefficient of determination of 0.89 (R2 = 0.89), an RMSE of 0.98, and a normalized RMSE = 0.17. We also found that the model maintains moderate accuracy across voxel size input parameters, suggesting it may maintain accuracy in different treatment conditions where tree structural relationships can change. Our canopy structural results supported the hypothesis that LAI increases more significantly over time under warmer conditions when compared to control plots. Lower canopy leaf area density trends did not support the hypothesis as they showed no statistically significant trends across time. Leaf inclination angle trends through time did not support the hypothesis as they tended to decrease. As temperatures increased across the temperature gradient, leaf angles became more vertical in upper canopies under elevated CO2, leading to inconclusive support for or against the hypothesis. Species data did not support the hypothesis that spruce and larch canopy structures would differ significantly under the same treatments. The larch LAI, however, did not increase as significantly through time as the spruce under elevated CO2 conditions. Additionally, we identified anomalous fluctuations in time series data and proposed potential temperature thresholds where LAI differed the most under ambient or elevated CO2 conditions. The findings from this study suggest that accurately quantifying canopy structure through time may be possible in different environmental conditions and species using TLS. We add support to previous findings that LAI increases more significantly through time under warming conditions compared to control conditions. These results demonstrate TLS’s utility for making species-level canopy structural estimates across horizontal and vertical profiles. Incorporating vertical canopy profile metrics such as leaf area density with LAI data can assist in better explaining how LAI is changing across time and temperature gradients.

Most conventional Leaf Area Index (LAI) retrieval methods using Terrestrial Laser Scanning (TLS) data are based on Beer’s law and are severely affected by the effects of leaf occlusion and aggregation. Moreover, the correction of LAI using the Clumping Index (CI) relies on assumptions and is generally not robust. This paper exploits the high spatial resolution and penetration capability of TLS to explore the physical meaning of point cloud data sampling and then model the leaf cluster envelope by the Alpha-shape algorithm. Subsequently, canopy LAI is obtained by counting the surface area of the envelope of each leaf cluster within the canopy and combining it with the projected area of the canopy. The entire process is physically based and introduces a new LAI inversion approach based on TLS. We tested the approach by simulating the TLS data of 25 synthetic trees with different leaf areas and morphologies to evaluate its robustness. Four strategies were adopted for parameter selection in the envelope modeling step to automate the process of finding the optimal envelope radius and improve the inversion accuracy of LAI. In comparison with the traditional LAI retrieval method based on Beer’s law (RMSE% is 47.3%), we found that the method proposed in this letter has a higher inversion accuracy with a minimum RMSE% of 27.7%. Our method also is significantly more robust for high LAI scenes and performs well in scenes with high occlusion and aggregation.

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

L1-Tree: A novel algorithm for constructing 3D tree models and estimating branch architectural traits using terrestrial laser scanning data

Abstract. The precision of Terrestrial Laser Scanning (TLS) data depends mainly on the inherent random range error, which hinders extraction of small details from TLS measurements. New post processing algorithms have been developed that reduce or eliminate the noise and therefore enable modelling details at a smaller scale than one would traditionally expect. The aim of this research is to find the optimum denoising method such that the corrected TLS data provides a reliable estimation of small-scale rock joint roughness. Two wavelet-based denoising methods are considered, namely Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), in combination with different thresholding procedures. The question is, which technique provides a more accurate roughness estimates considering (i) wavelet transform (SWT or DWT), (ii) thresholding method (fixed-form or penalised low) and (iii) thresholding mode (soft or hard). The performance of denoising methods is tested by two analyses, namely method noise and method sensitivity to noise. The reference data are precise Advanced TOpometric Sensor (ATOS) measurements obtained on 20 × 30 cm rock joint sample, which are for the second analysis corrupted by different levels of noise. With such a controlled noise level experiments it is possible to evaluate the methods’ performance for different amounts of noise, which might be present in TLS data. Qualitative visual checks of denoised surfaces and quantitative parameters such as grid height and roughness are considered in a comparative analysis of denoising methods. Results indicate that the preferred method for realistic roughness estimation is DWT with penalised low hard thresholding.

Terrestrial laser scanning (TLS) plays a significant role in forest resource investigation, forest parameter inversion and tree 3D model reconstruction. TLS can accurately, quickly and nondestructively obtain 3D structural information of standing trees. TLS data, rather than felled wood data, were used to construct a mixed model of the taper function based on the tree effect, and the TLS data extraction and model prediction effects were evaluated to derive the stem diameter and volume. TLS was applied to a total of 580 trees in the nine larch (Larix olgensis) forest plots, and another 30 were applied to a stem analysis in Mengjiagang. First, the diameter accuracies at different heights of the stem analysis were analyzed from the TLS data. Then, the stem analysis data and TLS data were used to establish the stem taper function and select the optimal basic model to determine a mixed model based on the tree effect. Six basic models were fitted, and the taper equation was comprehensively evaluated by various statistical metrics. Finally, the optimal mixed model of the plot was used to derive stem diameters and trunk volumes. The stem diameter accuracy obtained by TLS was >98%. The taper function fitting results of these data were approximately the same, and the optimal basic model was Kozak (2002)-II. For the tree effect, a6 and a9 were used as the mixed parameters, the mixed model showed the best fit, and the accuracy of the optimal mixed model reached 99.72%.The mixed model accuracy for predicting the tree diameter was between 74.22% and 97.68%, with a volume estimation accuracy of 96.38%. Relative height 70 (RH70) was the optimum height for extraction, and the fitting accuracy of the mixed model was higher than that of the basic model.

Abstract. Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.

Background and Objectives: Teak (Tectona grandis) is one of Thailand's most valuable economic tree species, prized for its exceptional qualities such as durability, aesthetic appeal, and resistance to pests and diseases. Its popularity spans both domestic and international markets, making accurate estimation of merchantable teak volume essential for forest management and the timber industry. Precise volume estimation facilitates accurate economic valuation and minimizes resource losses associated with traditional destructive tree-felling methods. Recent advancements in technology, light detection and ranging or LiDAR, have provided opportunities to enhance the accuracy and efficiency of forest resource data collection. Among these tools, the Terrestrial Laser Scanner (TLS) stands out for its ability to generate high-resolution three-dimensional (3D) models of individual trees and forest stands in a non-destructive method. Unlike traditional methods, TLS enables data collection without damaging trees, offering a sustainable and efficient alternative for forestry applications. This study aimed to develop a methodology for utilizing TLS to construct 3D models of teak trees to estimate merchantable wood volume. The results were compared with the traditional felling method, which is considered the most accurate but destructive. In addition, the study attempts to establish predictive equations and volume tables for estimating merchantable teak volume. These outputs aimed to support sustainable forest management practices, reduce resource wastes, and facilitate the economic valuations of teak for the timber industry. Methods: The study was conducted in the Mae Chang forest plantation, located in Mae Mo district, Lampang province, Thailand. The stratified random sampling was employed to establish 12 sample plots, each sizing 40 m × 40 m. The plots were divided into two age groups based on plantation year: 18 years old (planted in 2006) and 39 years old (planted in 1985), with six plots in each group. Data collection involved using a Faro Focus S150 TLS to scan individual trees in each plot. The TLS data were processed to create detailed 3D tree models for further analysis. Merchantable timber volume was calculated using two methods: the stem curve method and quantitative structure models (QSM), both implemented through the 3D forest software. The TLS-derived volume estimates were validated by felling selected sample trees. The diameters and merchantable lengths of these felled trees were measured, and their volumes were calculated using Huber’s formula. Statistical analysis was performed to compare TLS-derived volumes with traditional felling-based volumes using paired t-tests. The accuracy of TLS-derived estimates was evaluated using root mean square error (RMSE). Additionally, predictive equations for merchantable volume estimation were developed using regression analysis. Two types of equations were created: one using diameter at breast height (DBH) as the sole variable and the other incorporating both DBH and merchantable height (HM). A volume table was constructed based on these equations, enabling non-destructive volume estimation for teak trees of varying DBH and HM ranges. Results: The 3D models generated using TLS provided merchantable volume estimates that closely aligned with volumes derived from felled trees. Statistical analysis revealed no significant differences between TLS-derived volumes and traditional felling-based volumes (p > 0.05). The RMSE values for TLS-derived estimates were 0.02 and 0.03 cubic meters when using the stem curve and QSM methods, respectively, indicating high accuracy. Two predictive equations for merchantable volume estimation were developed. The first equation utilized DBH as a single predictor variable and demonstrated a coefficient of determination (R²) of 0.966. The second equation incorporated both DBH and HM, achieving a higher R² value of 0.988. Validation of these equations using data from felled trees showed mean absolute percentage errors (MAPE) of 17.81% and 11.01%, respectively, confirming their suitability for practical applications. The most accurate equation was subsequently used to construct a merchantable volume table, which estimating based on DBH and HM ranges without requiring tree felling. This table provides a practical tool for forest managers and timber industry stakeholders to assess teak volume efficiently and sustainably. Conclusion: The results of this study demonstrate that TLS is a reliable and effective tool for non-destructive estimation of merchantable teak volume. The 3D models generated using TLS data enable precise volume estimation through methods such as stem curve and QSM, producing results that are statistically comparable to traditional destructive felling methods. The acceptable error margins and high accuracy of TLS-derived estimates highlight its potential as a sustainable alternative to conventional volume estimation techniques. By minimizing the need for destructive sampling, TLS-based methods contribute to sustainable forest management practices, preserving valuable tree resources while maintaining economic productivity. Furthermore, the development of predictive equations and merchantable volume tables offers practical solutions for forest managers and timber industry professionals, streamlining the assessment of merchantable volume and economic valuation. Overall, this study underscores the importance of integrating advanced technologies such as TLS into forestry practices. By improving the accuracy and sustainability of resource assessments, these innovations can support long-term forest conservation and enhance the economic potential of Thailand's teak plantations.

Glacial lake outburst floods (GLOFs) from ice-dammed lakes are frequent in Svalbard, impacting local ice dynamics, and subglacial hydrological systems, causing geomorphological changes, and posing flooding hazards. Additionally, GLOFs can influence nutrient dynamics in the fjord of tidewater glaciers, affecting the local ecosystem.In this study, we use high-resolution topographic data to monitor the formation of an ice-dammed lake and identify the drainage mechanisms of a GLOF that occurred in the summer of 2021 on the Kongsvegen glacier, a surge-type tidewater glacier located in Kongsfjorden (Svalbard). Additionally, seismometers were deployed to monitor the subglacial dynamics at the kilometre scale.Over the 2.5-month-long process starting in early June, terrestrial laser scanning (TLS) data and drone images were acquired at nearly daily intervals to monitor the ice-dammed lake formation and drainage. A time-lapse camera and pressure logger installed at the border of the ice-dammed lake allowed us to estimate the drainage timing, occurring from July 23 to July 26, resulting in a total drainage duration of 77 hours. To reconstruct the lake volume, the lake extension was manually digitized from the TLS data and drone orthophotos. Elevation information of the corresponding lake outlines was extracted from a 1 m resolution Digital Elevation Model (DEM) generated from Pléiades stereo satellite images acquired on 20 September 2020, at the end of the thaw season. This DEM serves as bathymetric data, representing the lake bottom. The extracted water level was used to calculate the stage-volume curve. The lake's maximum volume reached approximately 7.17 million m3 with an average discharge rate of 26 m3/s. Analyzing seismic data allowed for monitoring of the development of the subglacial drainage, assessing the transition from an inefficient to an efficient system.This study highlights the importance of very high spatial and temporal resolution data for accurate lake volume quantification and a better understanding of the link between GLOF and subglacial system.

A critical component of the forest ecosystem, the understorey supports the vast majority of wildlife habitat and total ecosystem floristic diversity. Remote sensing data have been developed to provide information at different scales for surveys of forest ecosystems, but obtaining information about the understorey remains a challenge. As rapid and efficient tools for forest structure attribute estimation, Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) have attracted much attention. We examine the relationship between ALS and TLS data and detect changes in the forest understorey caused by forest-tending events in the study area. We conducted trials in five plots within a young Khasi pine (Pinus kesiya Royle ex Gord.) plantation in Yunnan province, China, before and after forest tending. We collected bi-temporal ALS data in this area and TLS data from 10 scanning stations. Canopy height profiles were retrieved from ALS and TLS data, and understorey material volume retrieved from filled TLS voxels volume agreed well with the understorey point clouds percentile distribution (PD) obtained from ALS data. The PD value for the understorey from ALS multiplied by the percentage of ALS return points in the overstorey had a stronger correlation (R2 = 0.90) with the TLS-derived understorey material volume than did the ALS PD value for the understorey only (R2 = 0.71). Taking the effect of the overstorey into consideration will improve evaluations of forest understorey parameters from ALS data. This study confirmed the potential of TLS as a validation tool to assess the accuracy of forest understorey material volume estimation at large scales using ALS data.

Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested and compared to a method without the use of TLS. Single-tree Norway spruce crown biomass was predicted using an ALS-model with training data obtained by TLS. ALS and TLS data were collected for sets of sample trees, including 68 trees with both ALS and TLS data. In total, 29 destructively sampled trees were used to fit a TLS crown biomass model, which then was used to predict crown biomass in a separate set of 68 trees. This dataset was subsequently used to fit an ALS crown biomass model. When validating the model, using a separate dataset with accurately measured crown biomass obtained through destructive sampling, the mean error was 32% of the observed mean biomass. Corresponding crown biomass predictions derived with ALS-predicted diameters and the use of conventional and existing allometric models resulted in a mean error of 35%. Thus, in the present study, a slight improvement, in terms of prediction accuracy, was found when using training data with ground reference values obtained by TLS.

A crucial part of carbon accounting is quantifying a tree’s aboveground biomass (AGB) using allometric equations, but species-specific equations are limited because data to inform these equations requires destructive harvesting of many trees which is difficult and time-consuming. Here, we used terrestrial laser scanning (TLS) to non-destructively estimate AGB for 282 trees from 5 species at 3 locations in Northern California using stem and branch volume estimates from quantitative structure models (QSMs) and wood density from the literature. We then compared TLS QSM estimates of AGB with published allometric equations and used TLS-based AGB, diameter at breast height (DBH), and height to derive new species-specific allometric AGB equations for our study species. To validate the use of TLS, we used traditional forestry approaches to collect DBH (n = 550) and height (n = 291) data on individual trees. TLS-based DBH and height were not significantly different from field inventory data (R2 = 0.98 for DBH, R2 = 0.95 for height). Across all species, AGB calculated from TLS QSM volumes were approximately 30% greater than AGB estimates using published Forest Service’s Forest Inventory and Analysis Program equations, and TLS QSM AGB estimates were 10% greater than AGB calculated with existing equations, although this variation was species-dependent. In particular, TLS AGB estimates for Quercus agrifolia and Sequoia sempervirens differed the most from AGB estimates calculated using published equations. New allometric equations created using TLS data with DBH and height performed better than equations that only included DBH and matched most closely with AGB estimates generated from QSMs. Our results support the use of TLS as a method to rapidly estimate height, DBH, and AGB of multiple trees at a plot-level when species are identified and wood density is known. In addition, the creation of new TLS-based non-destructive allometric equations for our 5 study species may have important applications and implications for carbon quantification over larger spatial scales, especially since our equations estimated greater AGB than previous approaches.

Terrestrial laser scanning (TLS) represents the gold standard in remote quantification of woody vegetation structure and volume, but is costly and time consuming to acquire. TLS data is typically collected at spatial scales of 1 ha or smaller, which limits its suitability for representing heterogeneous landscapes, and for training and validating satellite-based models which are needed for larger area monitoring. Advances in unoccupied aerial vehicle laser scanning (UAV-LS) sensors have recently narrowed the gap in quality between what TLS delivers and what can be acquired over larger areas from UAV platforms. We tested how well new nadir-forward–backward (NFB) UAV-LS technology can capture the structure of individual trees in a tropical savanna setting with a diversity of tree sizes and growth forms. UAV-LS data was acquired with a RIEGL VUX-120 LiDAR sensor mounted on a Acecore NOA hexacopter. Reference data was obtained with a RIEGL VZ-2000i TLS scanner using a multi-scan approach. Point clouds were segmented into individual trees and volumetrically reconstructed with RayCloudTools (RCT). We found no statistical difference between UAV-LS and TLS derived estimates of tree height, canopy cover, diameter, and wood volume. Mean tree height and DBH derived from UAV-LS were within 3% of the TLS estimate, and there was less than 1% deviation in stand wood volume. Our findings ease the advancements on the detailed monitoring of open forests, potentially achieving large-scale mapping and multi-temporal investigations. The open structure of savanna systems is well suited to UAV-LS sensing, but more research is needed across diverse ecosystems to understand the generality of these findings in landscapes with greater canopy closure or complex understorey conditions.