SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds

Modeling the horizontal distribution of tree crown biomass from terrestrial laser scanning data

Tree crown biomass is rarely assessed individually in forest monitoring, but when it is to be reported, standard conversion factors are commonly used for predicting crown biomass as a function of stem biomass. Further, in the conventional methods, the predicted total tree biomass is assigned exclusively to the stem position. In reality, however, tree and in particular crown biomass is spatially distributed over the entire crown projection area. In this study, we investigated the "Horizontal Biomass Distribution (HBD)" model, which serves to depict this biomass as a spatial distribution over the crown projection area: here, the individual tree crown biomass is modeled as a continuous distribution within the area defined by the crown projection. We examined two empirical HBD prediction models: (1) Weibull distribution; and (2) Segmented polynomial regression; which describe the biomass contained up to a given crown radius on the horizontal projection of individual trees, i.e., spatial distribution of crown biomass as a function of the horizontal distance from the stem. The approach was demonstrated using terrestrial laser scanning (TLS) on a sample of 33 urban trees from eight species. We found that (1) the segmented polynomial regression model revealed better performance in defining the HBD for various tree species; (2) a certain variability in HBD patterns was observed between the sample trees, with the variability being more pronounced between species groups than within species; and (3) the methodological approaches using TLS proxies are suitable and convenient to non-destructively assess the HBD, which would be otherwise impractical by direct measurements.

A multilayered urban tree dataset of point clouds, quantitative structure and graph models

The significance of urban trees in promoting human health and well-being has been amplified by urbanization and the climate change effects. Simultaneously, advancements in remote sensing techniques have enhanced the opportunities for studying urban trees. The TreeML-Data has been compiled to support these efforts. It consists of labelled point clouds of 40 scanning projects of streets in Munich, 3,755 leaf-off (scans in winter) point clouds of individual trees, quantitative structure models (QSM), tree structure measurements, and tree graph structure models of these trees. The dataset offers valuable data for generating and evaluating models in various scientific disciplines, which include remote sensing, computer vision, machine learning, urban forestry, urban ecosystem, green architecture, and graph analysis. To ensure its quality, the tree structure measurements and QSM have been crosschecked. For instance, the tree diameter at breast height (DBH) in the sample dataset exhibits a deviation of approximately 1.5 cm (4.3%) when compared to manual measurements. In conclusion, the quality checks confirm its reliability for subsequent studies when compared to manual measurements.

Co-developing an international TLS network for the 3D ecological understanding of global trees: System architecture, remote sensing models, and functional prospects

Trees are spread worldwide, as the watchmen that experience the intricate ecological effects caused by various environmental factors. In order to better understand such effects, it is preferential to achieve finely and fully mapped global trees and their environments. For this task, aerial and satellite-based remote sensing (RS) methods have been developed. However, a critical branch regarding the apparent forms of trees has significantly fallen behind due to the technical deficiency found within their global-scale surveying methods. Now, terrestrial laser scanning (TLS), a state-of-the-art RS technology, is useful for the in situ three-dimensional (3D) mapping of trees and their environments. Thus, we proposed co-developing an international TLS network as a macroscale ecotechnology to increase the 3D ecological understanding of global trees. First, we generated the system architecture and tested the available RS models to deepen its ground stakes. Then, we verified the ecotechnology regarding the identification of its theoretical feasibility, a review of its technical preparations, and a case testification based on a prototype we designed. Next, we conducted its functional prospects by previewing its scientific and technical potentials and its functional extensibility. Finally, we summarized its technical and scientific challenges, which can be used as the cutting points to promote the improvement of this technology in future studies. Overall, with the implication of establishing a novel cornerstone-sense ecotechnology, the co-development of an international TLS network can revolutionize the 3D ecological understanding of global trees and create new fields of research from 3D global tree structural ecology to 3D macroecology.

Faster navigation of semi-structured forest environments using multirotor UAVs

Modern approaches for exploration path planning generally do not assume any structural information regarding the operational area. Therefore, they offer good performance when the region of interest is entirely unknown. However, for some applications such as plantation forest surveying, partial information regarding the survey area is known before the exploration process. Because the region of interest consists only of the lower portions of the tree stems themselves, the ground and high-elevation sections of the environment are unimportant and do not need to be observed. Due to these unconventional conditions, existing methods favoring faster survey speeds produce suboptimal surveys as they do not try and ensure even coverage across the entire exploration volume, while methods that favor reconstruction accuracy produce excessively long survey times. This work proposes a structured exploration approach specifically for plantation forests utilizing a lawnmowing pattern to maximize coverage while minimizing re-visited regions, guiding the unmanned aerial vehicle to visit all areas. Experiments are conducted in various environments, with comparisons made to state-of-the-art exploration planners regarding survey time and coverage. Results suggest that the proposed methods produce surveys with significantly more predictable coverage and survey times at the expense of a longer survey.

Phyllotaxis transition over the lifespan of a palm tree using Magnetic Resonance Imaging (MRI) and Terrestrial Laser Scanning (TLS): the case of Jubaea chilensis

BACKGROUND

Jubaea chilensis (Molina) Baillon, is a uniquely large palm species endemic to Chile. It is under threatened status despite its use as an ornamental species throughout the world. This research seeks to identify the phyllotaxis of the species based on an original combination of non-destructive data acquisition technologies, namely Magnetic Resonance Imaging (MRI) in saplings and young individuals and Terrestrial Laser Scanning (TLS) in standing specimens, and a novel analysis methodology.

RESULTS

Two phyllotaxis parameters, parastichy pairs and divergence angle, were determined by analyzing specimens at different developmental stages. Spiral phyllotaxis patterns of J. chilensis progressed in complexity from parastichy pairs (3,2) and (3,5) in juvenile specimens and (5,3), (8,5) and (8,13) for adult specimens. Divergence angle was invariable and averaged 136.9°, close to the golden angle. Phyllotactic pattern changes associated with establishment phase, the adult vegetative and the adult reproductive phases were observed. Both technologies, MRI and TLS proved to be adequate for the proposed analysis.

CONCLUSIONS

Understanding phyllotactic transitions may assist identification of developmental stages of wild J. chilensis specimens. The proposed methodology may also be useful for the study of other palm species.

Fast Tree Skeleton Extraction Using Voxel Thinning Based on Tree Point Cloud

Tree skeletons play an important role in tree structure analysis and 3D model reconstruction. However, it is a challenge to extract a skeleton from a tree point cloud with complex branches. In this paper, an automatic and fast tree skeleton extraction method (FTSEM) based on voxel thinning is proposed. In this method, a wood–leaf classification algorithm was introduced to filter leaf points for the reduction of the leaf interference on tree skeleton generation, tree voxel thinning was adopted to extract a raw tree skeleton quickly, and a breakpoint connection algorithm was used to improve the skeleton connectivity and completeness. Experiments were carried out in Haidian Park, Beijing, in which 24 trees were scanned and processed to obtain tree skeletons. The graph search algorithm (GSA) was used to extract tree skeletons based on the same datasets. Compared with the GSA method, the FTSEM method obtained more complete tree skeletons. The time cost of the FTSEM method was evaluated using the runtime and time per million points (TPMP). The runtime of FTSEM was from 1.0 s to 13.0 s, and the runtime of GSA was from 6.4 s to 309.3 s. The average value of TPMP was 1.8 s for FTSEM and 22.3 s for GSA, respectively. The experimental results demonstrate that the proposed method is feasible, robust, and fast with good potential for tree skeleton extraction.

An Artificial Intelligence Agent That Synthesises Visual Abstractions of Natural Forms to Support the Design of Human-Made Habitat Structures

Biodiversity is in a state of global collapse. Among the main drivers of this crisis is habitat degradation that destroys living spaces for animals, birds, and other species. Design and provision of human-made replacements for natural habitat structures can alleviate this situation. Can emerging knowledge in ecology, design, and artificial intelligence (AI) help? Current strategies to resolve this issue include designing objects that reproduce known features of natural forms. For instance, conservation practitioners seek to mimic the function of rapidly disappearing large old trees by augmenting utility poles with perch structures. Other approaches to restoring degraded ecosystems employ computational tools to capture information about natural forms and use such data to monitor remediation activities. At present, human-made replacements of habitat structures cannot reproduce significant features of complex natural forms while supporting efficient construction at large scales. We propose an AI agent that can synthesise simplified but ecologically meaningful representations of 3D forms that we define as visual abstractions. Previous research used AI to synthesise visual abstractions of 2D images. However, current applications of such techniques neither extend to 3D data nor engage with biological conservation or ecocentric design. This article investigates the potential of AI to support the design of artificial habitat structures and expand the scope of computation in this domain from data analysis to design synthesis. Our case study considers possible replacements of natural trees. The application implements a novel AI agent that designs by placing three-dimensional cubes – or voxels – in the digital space. The AI agent autonomously assesses the quality of the resulting visual abstractions by comparing them with three-dimensional representations of natural trees. We evaluate the forms produced by the AI agent by measuring relative complexity and features that are meaningful for arboreal wildlife. In conclusion, our study demonstrates that AI can generate design suggestions that are aligned with the preferences of arboreal wildlife and can support the development of artificial habitat structures. The bio-informed approach presented in this article can be useful in many situations where incomplete knowledge about complex natural forms can constrain the design and performance of human-made artefacts.

3D modeling and reconstruction of plants and trees: A cross-cutting review across computer graphics, vision, and plant phenotyping

This paper reviews the past and current trends of three-dimensional (3D) modeling and reconstruction of plants and trees. These topics have been studied in multiple research fields, including computer vision, graphics, plant phenotyping, and forestry. This paper, therefore, provides a cross-cutting review. Representations of plant shape and structure are first summarized, where every method for plant modeling and reconstruction is based on a shape/structure representation. The methods were then categorized into 1) creating non-existent plants (modeling) and 2) creating models from real-world plants (reconstruction). This paper also discusses the limitations of current methods and possible future directions.

What happens to epiphytic bromeliads in a windy spot?

Several studies of hurricane damage on epiphyte communities implied that epiphytes might be in danger of being blown off their host when subjected to strong wind. There is very limited knowledge about the mechanical impact that wind may have on epiphytes. Using a wind-triggered camera set-up, we observed how epiphytic tank bromeliads are affected by wind. Despite offering a relatively large area of ‘attack’ to the airflow, bromeliads moved relatively little themselves. Rather than being directly moved by wind, the bromeliads in the upper crown of tall trees moved with the sway of the branches. Only when the substrate did not move, bromeliads with long broad leaves showed considerable disturbance due to wind. Our observations underline the complexity of the system and emphasise that our current understanding of the mechanical aspects of the epiphyte–host system is still very limited.

Forest Structural Complexity Tool—An Open Source, Fully-Automated Tool for Measuring Forest Point Clouds

Forest mensuration remains critical in managing our forests sustainably, however, capturing such measurements remains costly, time-consuming and provides minimal amounts of information such as diameter at breast height (DBH), location, and height. Plot scale remote sensing techniques show great promise in extracting detailed forest measurements rapidly and cheaply, however, they have been held back from large-scale implementation due to the complex and time-consuming workflows required to utilize them. This work is focused on describing and evaluating an approach to create a robust, sensor-agnostic and fully automated forest point cloud measurement tool called the Forest Structural Complexity Tool (FSCT). The performance of FSCT is evaluated using 49 forest plots of terrestrial laser scanned (TLS) point clouds and 7022 destructively sampled manual diameter measurements of the stems. FSCT was able to match 5141 of the reference diameter measurements fully automatically with mean, median and root mean squared errors (RMSE) of 0.032 m, 0.02 m, and 0.103 m respectively. A video demonstration is also provided to qualitatively demonstrate the diversity of point cloud datasets that the tool is capable of measuring. FSCT is provided as open source, with the goal of enabling plot scale remote sensing techniques to replace most structural forest mensuration in research and industry. Future work on this project will seek to make incremental improvements to this methodology to further improve the reliability and accuracy of this tool in most high-resolution forest point clouds.

Automatic extraction and measurement of individual trees from mobile laser scanning point clouds of forests

BACKGROUND AND AIMS

In addition to terrestrial laser scanning (TLS), mobile laser scanning (MLS) is increasingly arousing interest as a technique which provides valuable 3-D data for various applications in forest research. Using mobile platforms, the 3-D recording of large forest areas is carried out within a short space of time. Vegetation structure is described by millions of 3-D points which show an accuracy in the millimetre range and offer a powerful basis for automated vegetation modelling. The successful extraction of single trees from the point cloud is essential for further evaluations and modelling at the individual-tree level, such as volume determination, quantitative structure modelling or local neighbourhood analyses. However, high-precision automated tree segmentation is challenging, and has so far mostly been performed using elaborate interactive segmentation methods.

METHODS

Here, we present a novel segmentation algorithm to automatically segment trees in MLS point clouds, applying distance adaptivity as a function of trajectory. In addition, tree parameters are determined simultaneously. In our validation study, we used a total of 825 trees from ten sample plots to compare the data of trees segmented from MLS data with manual inventory parameters and parameters derived from semi-automatic TLS segmentation.

KEY RESULTS

The tree detection rate reached 96 % on average for trees with distances up to 45 m from the trajectory. Trees were almost completely segmented up to a distance of about 30 m from the MLS trajectory. The accuracy of tree parameters was similar for MLS-segmented and TLS-segmented trees.

CONCLUSIONS

Besides plot characteristics, the detection rate of trees in MLS data strongly depends on the distance to the travelled track. The algorithm presented here facilitates the acquisition of important tree parameters from MLS data, as an area-wide automated derivation can be accomplished in a very short time.

Integrating terrestrial laser scanning with functional-structural plant models to investigate ecological and evolutionary processes of forest communities

BACKGROUND

Woody plants (trees and shrubs) play an important role in terrestrial ecosystems, but their size and longevity make them difficult subjects for traditional experiments. In the last 20 years functional-structural plant models (FSPMs) have evolved: they consider the interplay between plant modular structure, the immediate environment and internal functioning. However, computational constraints and data deficiency have long been limiting factors in a broader application of FSPMs, particularly at the scale of forest communities. Recently, terrestrial laser scanning (TLS), has emerged as an invaluable tool for capturing the 3-D structure of forest communities, thus opening up exciting opportunities to explore and predict forest dynamics with FSPMs.

SCOPE

The potential synergies between TLS-derived data and FSPMs have yet to be fully explored. Here, we summarize recent developments in FSPM and TLS research, with a specific focus on woody plants. We then evaluate the emerging opportunities for applying FSPMs in an ecological and evolutionary context, in light of TLS-derived data, with particular consideration of the challenges posed by scaling up from individual trees to whole forests. Finally, we propose guidelines for incorporating TLS data into the FSPM workflow to encourage overlap of practice amongst researchers.

CONCLUSIONS

We conclude that TLS is a feasible tool to help shift FSPMs from an individual-level modelling technique to a community-level one. The ability to scan multiple trees, of multiple species, in a short amount of time, is paramount to gathering the detailed structural information required for parameterizing FSPMs for forest communities. Conventional techniques, such as repeated manual forest surveys, have their limitations in explaining the driving mechanisms behind observed patterns in 3-D forest structure and dynamics. Therefore, other techniques are valuable to explore how forests might respond to environmental change. A robust synthesis between TLS and FSPMs provides the opportunity to virtually explore the spatial and temporal dynamics of forest communities.

Terrestrial laser scanning: a new standard of forest measuring and modelling?

BACKGROUND

Laser scanning technology has opened new horizons for the research of forest dynamics, because it provides a largely automated and non-destructive method to rapidly capture the structure of individual trees and entire forest stands at multiple spatial scales. The structural data themselves or in combination with additional remotely sensed data also provide information on the local physiological state of structures within trees. The capacity of new methods is facilitated by the ongoing development of automated processing tools that are designed to capture information from the point cloud data provided by the remote measurements.

SCOPE

Terrestrial laser scanning (TLS), performed from the ground or from unmanned aerial vehicles, in particular, has potential to become a unifying measurement standard for forest research questions, because the equipment is flexible to use in the field and has the capacity to capture branch-level structural information at the forestplot or even forest scale. This issue of Annals of Botany includes selected papers that exemplify the current and potential uses of TLS, such as for examination of crown interactions between trees, growth dynamics of mixed stands, non-destructive characterization of urban trees, and enhancement of ecological and evolutionary models. The papers also present current challenges in the applicability of TLS methods and report recent developments in methods facilitating the use of TLS data for research purposes, including automatic processing chains and quantifying branch and above-ground biomass. In this article, we provide an overview of the current and anticipated future capacity of TLS and related methods in solving questions that utilize measurements and models of forests.

CONCLUSIONS

Due to its measurement speed, TLS provides a method to effortlessly capture large amounts of detailed structural forest information, and consequent proxy data for tree and forest processes, at a far wider spatial scale than is feasible with manual measurements. Issues with measurement precision and occlusion of laser beams before they reach their target structures continue to reduce the accuracy of TLS data, but the limitations are counterweighted by the measurement speed that enables large sample sizes. The currently high time-cost of analysing TLS data, in turn, is likely to decrease through progress in automated processing methods. The developments point towards TLS becoming a new and widely accessible standard tool in forest measurement and modelling.

Forest above-ground volume assessments with terrestrial laser scanning: a ground-truth validation experiment in temperate, managed forests

BACKGROUND AND AIMS

Quantifying the Earth's forest above-ground biomass (AGB) is indispensable for effective climate action and developing forest policy. Yet, current allometric scaling models (ASMs) to estimate AGB suffer several drawbacks related to model selection and uncertainties about calibration data traceability. Terrestrial laser scanning (TLS) offers a promising non-destructive alternative. Tree volume is reconstructed from TLS point clouds with quantitative structure models (QSMs) and converted to AGB with wood basic density. Earlier studies have found overall TLS-derived forest volume estimates to be accurate, but highlighted problems for reconstructing finer branches. Our objective was to evaluate TLS for estimating tree volumes by comparison with reference volumes and volumes from ASMs.

METHODS

We quantified the woody volume of 65 trees in Belgium (from 77 to 2800 L; Pinus sylvestris, Fagus sylvatica, Larix decidua, and Fraxinus excelsior) with QSMs and destructive reference measurements. We tested a volume expansion factor (VEF) approach by multiplying the solid and merchantable volume from QSMs by literature VEF values.

KEY RESULTS

Stem volume was reliably estimated with TLS. Total volume was overestimated by +21 % using original QSMs, by +9 % and -12 % using two sets of VEF-augmented QSMs, and by -7.3 % using best-available ASMs. The most accurate method differed per site, and the prediction errors for each method varied considerably between sites.

CONCLUSIONS

VEF-augmented QSMs were only slightly better than original QSMs for estimating tree volume for common species in temperate forests. Despite satisfying estimates with ASMs, the model choice was a large source of uncertainty, and species-specific models did not always exist. Therefore, we advocate for further improving tree volume reconstructions with QSMs, especially for fine branches, instead of collecting more ground-truth data to calibrate VEF and allometric models. Promising developments such as improved co-registration and smarter filtering approaches are ongoing to further constrain volumetric errors in TLS-derived estimates.

Evaluation of automated pipelines for tree and plot metric estimation from TLS data in tropical forest areas

BACKGROUND AND AIMS

Terrestrial LiDAR scanning (TLS) data are of great interest in forest ecology and management because they provide detailed 3-D information on tree structure. Automated pipelines are increasingly used to process TLS data and extract various tree- and plot-level metrics. With these developments comes the risk of unknown reliability due to an absence of systematic output control. In the present study, we evaluated the estimation errors of various metrics, such as wood volume, at tree and plot levels for four automated pipelines.

METHODS

We used TLS data collected from a 1-ha plot of tropical forest, from which 391 trees >10 cm in diameter were fully processed using human assistance to obtain control data for tree- and plot-level metrics.

KEY RESULTS

Our results showed that fully automated pipelines led to median relative errors in the quantitative structural model (QSM) volume ranging from 39 to 115 % at the tree level and 10 to 134 % at the 1-ha plot level. For tree-level metrics, the median error for the crown-projected area ranged from 46 to 59 % and that for the crown-hull volume varied from 72 to 88 %. This result suggests that the tree isolation step is the weak link in automated pipeline methods. We further analysed how human assistance with automated pipelines can help reduce the error in the final QSM volume. At the tree scale, we found that isolating trees using human assistance reduced the error in wood volume by a factor of 10. At the 1-ha plot scale, locating trees with human assistance reduced the error by a factor of 3.

CONCLUSIONS

Our results suggest that in complex tropical forests, fully automated pipelines may provide relatively unreliable metrics at the tree and plot levels, but limited human assistance inputs can significantly reduce errors.

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.

Measuring the Contribution of Leaves to the Structural Complexity of Urban Tree Crowns with Terrestrial Laser Scanning

Trees have a fractal-like branching architecture that determines their structural complexity. We used terrestrial laser scanning technology to study the role of foliage in the structural complexity of urban trees. Forty-five trees of three deciduous species, Gleditsia triacanthos, Quercus macrocarpa, Metasequoia glyptostroboides, were sampled on the Michigan State University campus. We studied their structural complexity by calculating the box-dimension (Db) metric from point clouds generated for the trees using terrestrial laser scanning, during the leaf-on and -off conditions. Furthermore, we artificially defoliated the leaf-on point clouds by applying an algorithm that separates the foliage from the woody material of the trees, and then recalculated the Db metric. The Db of the leaf-on tree point clouds was significantly greater than the Db of the leaf-off point clouds across all species. Additionally, the leaf removal algorithm introduced bias to the estimation of the leaf-removed Db of the G. triacanthos and M. glyptostroboides trees. The index capturing the contribution of leaves to the structural complexity of the study trees (the ratio of the Db of the leaf-on point clouds divided by the Db of the leaf-off point clouds minus one), was negatively correlated with branch surface area and different metrics of the length of paths through the branch network of the trees, indicating that the contribution of leaves decreases as branch network complexity increases. Underestimation of the Db of the G. triacanthos trees, after the artificial leaf removal, was related to maximum branch order. These results enhance our understanding of tree structural complexity by disentangling the contribution of leaves from that of the woody structures. The study also highlighted important methodological considerations for studying tree structure, with and without leaves, from laser-derived point clouds.

Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park

Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds using photogrammetric mapping. Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also provide very dense 3D point clouds. While point clouds derived from both photogrammetric and LiDAR sensors can allow the accurate estimation of critical tree canopy parameters, so far a comparison of both techniques is missing. Point clouds derived from these sources vary according to differences in data collection and processing, a detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary. In this research, point clouds produced by UAV-photogrammetry and -LiDAR over an urban park along with the estimated tree canopy parameters are compared, and results are presented. The results show that UAV-photogrammetry and -LiDAR point clouds are highly correlated with R2 of 99.54% and the estimated tree canopy parameters are correlated with R2 of higher than 95%.

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture.

The Horizontal Distribution of Branch Biomass in European Beech: A Model Based on Measurements and TLS Based Proxies

Forest biomass is currently among the most important and most researched target variables in forest monitoring. The common approach of observing individual tree biomass in forest inventory is to assign the total tree biomass to the dimensionless point of the tree position. However, the tree biomass, in particular in the crown, is horizontally distributed above the crown projection area. This horizontal distribution of individual tree biomass (HBD) has not attracted much attention—but if quantified, it can improve biomass estimation and help to better represent the spatial distribution of forest fuel. In this study, we derive a first empirical model of the branch HBD for individual trees of European beech (Fagus sylvatica L.). We destructively measured 23 beech trees to derive an empirical model for the branch HBD. We then applied Terrestrial Laser Scanning (TLS) to a subset of 17 trees to test a simple point cloud metric predicting the branch HBD. We observed similarities between a branch HBD and commonly applied taper functions, which inspired our HBD model formulations. The models performed well in representing the HBD both for the measured biomass, and the TLS-based metric. Our models may be used as first approximations to the HBD of individual trees—while our methodological approach may extend to trees of different sizes and species.

New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6-1.2 m, height: 30-46 m, AGB: 3960-18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.

Modelling and Comparing Shading Effects of 3D Tree Structures with Virtual Leaves

Reduced solar radiation brought about by trees on agricultural land can both positively and negatively affect crop growth. For a better understanding of this issue, we aim for an improved simulation of the shade cast by trees in agroforestry systems and a precise estimation of insolation reduction. We present a leaf creation algorithm to generate realistic leaves to be placed upon quantitative structure models (QSMs) of real trees. Further, we couple it with an enhanced approach of a 3D model capable of quantifying shading effects of a tree, at a high temporal and spatial resolution. Hence, 3D data derived from wild cherry trees (Prunus avium L.) generated by terrestrial laser scanner technology formed a basis for the tree reconstruction, and served as leaf-off mode. Two leaf-on modes were simulated: realistic leaves, fed with leaf data from wild cherry trees; and ellipsoidal leaves, having ellipsoids as leaf-replacement. For comparison, we assessed the shading effects using hemispherical photography as an alternative method. Results showed that insolation reduction was higher using realistic leaves, and that the shaded area was greater in size than with the ellipsoidal leaves or leaf-off conditions. All shading effects were similarly distributed on the ground, with the exception of those derived through hemispherical photography, which were greater in size, but with less insolation reduction than realistic leaves. The main achievements of this study are: the enhancement of the leaf-on mode for QSMs with realistic leaves, the updates of the shadow model, and the comparison of shading effects. We provide evidence that the inclusion of realistic leaves with precise 3D data might be fundamental to accurately model the shading effects of trees.

Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

An Automatic Tree Skeleton Extraction Approach Based on Multi-View Slicing Using Terrestrial LiDAR Scans Data

Effective 3D tree reconstruction based on point clouds from terrestrial Light Detection and Ranging (LiDAR) scans (TLS) has been widely recognized as a critical technology in forestry and ecology modeling. The major advantages of using TLS lie in its rapidly and automatically capturing tree information at millimeter level, providing massive high-density data. In addition, TLS 3D tree reconstruction allows for occlusions and complex structures from the derived point cloud of trees to be obtained. In this paper, an automatic tree skeleton extraction approach based on multi-view slicing is proposed to improve the TLS 3D tree reconstruction, which borrowed the idea from the medical imaging technology of X-ray computed tomography. Firstly, we extracted the precise trunk center and then cut the point cloud of the tree into slices. Next, the skeleton from each slice was generated using the kernel mean shift and principal component analysis algorithms. Accordingly, these isolated skeletons were smoothed and morphologically synthetized. Finally, the validation in point clouds of two trees acquired from multi-view TLS further demonstrated the potential of the proposed framework in efficiently dealing with TLS point cloud data.

Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR

The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards.

AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

V-Mango: a functional-structural model of mango tree growth, development and fruit production

BACKGROUND AND AIMS

Mango (Mangifera indica L.) is the fifth most widely produced fruit in the world. Its cultivation, mainly in tropical and sub-tropical regions, raises a number of issues such as the irregular fruit production across years, phenological asynchronisms that lead to long periods of pest and disease susceptibility, and the heterogeneity of fruit quality and maturity at harvest. To address these issues, we developed an integrative functional-structural plant model that synthesizes knowledge about the vegetative and reproductive development of the mango tree and opens up the possible simulation of cultivation practices.

METHODS

We designed a model of architectural development in order to precisely characterize the intricate developmental processes of the mango tree. The appearance of botanical entities was decomposed into elementary stochastic events describing occurrence, intensity and timing of development. These events were determined by structural (position and fate of botanical entities) and temporal (appearance dates) factors. Daily growth and development of growth units and inflorescences were modelled using empirical distributions and thermal time. Fruit growth was determined using an ecophysiological model that simulated carbon- and water-related processes at the fruiting branch scale.

KEY RESULTS

The model simulates the dynamics of the population of growth units, inflorescences and fruits at the tree scale during a growing cycle. Modelling the effects of structural and temporal factors makes it possible to simulate satisfactorily the complex interplays between vegetative and reproductive development. The model allowed the characterization of the susceptibility of mango tree to pests and the investigatation of the influence of tree architecture on fruit growth.

CONCLUSIONS

This integrative functional-structural model simulates mango tree vegetative and reproductive development over successive growing cycles, allowing a precise characterization of tree phenology and fruit growth and production. The next step is to integrate the effects of cultivation practices, such as pruning, into the model.

A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds

Tree-level information can be estimated based on light detection and ranging (LiDAR) point clouds. We propose to develop a quantitative structural model based on terrestrial laser scanning (TLS) point clouds to automatically and accurately estimate tree attributes and to detect real trees for the first time. This model is suitable for forest research where branches are involved in the calculation. First, the Adtree method was used to approximate the geometry of the tree stem and branches by fitting a series of cylinders. Trees were represented as a broad set of cylinders. Then, the end of the stem or all branches were closed. The tree model changed from a cylinder to a closed convex hull polyhedron, which was to reconstruct a 3D model of the tree. Finally, to extract effective tree attributes from the reconstructed 3D model, a convex hull polyhedron calculation method based on the tree model was defined. This calculation method can be used to extract wood (including tree stem and branches) volume, diameter at breast height (DBH) and tree height. To verify the accuracy of tree attributes extracted from the model, the tree models of 153 Chinese scholartrees from TLS data were reconstructed and the tree volume, DBH and tree height were extracted from the model. The experimental results show that the DBH and tree height extracted based on this model are in better consistency with the reference value based on field survey data. The bias, RMSE and R2 of DBH were 0.38 cm, 1.28 cm and 0.92, respectively. The bias, RMSE and R2 of tree height were −0.76 m, 1.21 m and 0.93, respectively. The tree volume extracted from the model is in better consistency with the reference value. The bias, root mean square error (RMSE) and determination coefficient (R2) of tree volume were −0.01236 m3, 0.03498 m3 and 0.96, respectively. This study provides a new model for nondestructive estimation of tree volume, above-ground biomass (AGB) or carbon stock based on LiDAR data.

Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

Wood density (WD) relates to important tree functions such as stem mechanics and resistance against pathogens. This functional trait can exhibit high intraindividual variability both radially and vertically. With the rise of LiDAR-based methodologies allowing nondestructive tree volume estimations, failing to account for WD variations related to tree function and biomass investment strategies may lead to large systematic bias in AGB estimations. Here, we use a unique destructive dataset from 822 trees belonging to 51 phylogenetically dispersed tree species harvested across forest types in Central Africa to determine vertical gradients in WD from the stump to the branch tips, how these gradients relate to regeneration guilds and their implications for AGB estimations. We find that decreasing WD from the tree base to the branch tips is characteristic of shade-tolerant species, while light-demanding and pioneer species exhibit stationary or increasing vertical trends. Across all species, the WD range is narrower in tree crowns than at the tree base, reflecting more similar physiological and mechanical constraints in the canopy. Vertical gradients in WD induce significant bias (10%) in AGB estimates when using database-derived species-average WD data. However, the correlation between the vertical gradients and basal WD allows the derivation of general correction models. With the ongoing development of remote sensing products providing 3D information for entire trees and forest stands, our findings indicate promising ways to improve greenhouse gas accounting in tropical countries and advance our understanding of adaptive strategies allowing trees to grow and survive in dense rainforests.

Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner

Kernels found in stone pinecones are of great economic value, often surpassing timber income for most forest owners. Visually evaluating cone production on standing trees is challenging since the cones are located in the sun-exposed part of the crown, and covered by two vegetative shoots. Very few studies were carried out in evaluating how new remote sensing technologies such as terrestrial laser scanners (TLS) can be used in assessing cone production, or in trying to explain the tree-to-tree variability within a given stand. Using data from 129 trees in 26 plots located in the Spanish Northern Plateau, the gain observed by using TLS data when compared to traditional inventory data in predicting the presence, the number, and the average weight of the cones in an individual tree was evaluated. The models using TLS-derived metrics consistently showed better fit statistics, when compared to models using traditional inventory data pertaining to site and tree levels. Crown dimensions such as projected crown area and crown volume, crown density, and crown asymmetry were the key TLS-derived drivers in understanding the variability in inter-tree cone production. These results underline the importance of crown characteristics in assessing cone production in stone pine. Moreover, as cone production (number of cones and average weight) is higher in crowns with lower density, the use of crown pruning, abandoned over 30 years ago, might be the key to increasing production in combination with stand density management.

Developing Allometric Equations for Teak Plantations Located in the Coastal Region of Ecuador from Terrestrial Laser Scanning Data

Traditional studies aimed at developing allometric models to estimate dry above-ground biomass (AGB) and other tree-level variables, such as tree stem commercial volume (TSCV) or tree stem volume (TSV), usually involves cutting down the trees. Although this method has low uncertainty, it is quite costly and inefficient since it requires a very time-consuming field work. In order to assist in data collection and processing, remote sensing is allowing the application of non-destructive sampling methods such as that based on terrestrial laser scanning (TLS). In this work, TLS-derived point clouds were used to digitally reconstruct the tree stem of a set of teak trees (Tectona grandis Linn. F.) from 58 circular reference plots of 18 m radius belonging to three different plantations located in the Coastal Region of Ecuador. After manually selecting the appropriate trees from the entire sample, semi-automatic data processing was performed to provide measurements of TSCV and TSV, together with estimates of AGB values at tree level. These observed values were used to develop allometric models, based on diameter at breast height (DBH), total tree height (h), or the metric DBH2 × h, by applying a robust regression method to remove likely outliers. Results showed that the developed allometric models performed reasonably well, especially those based on the metric DBH2 × h, providing low bias estimates and relative RMSE values of 21.60% and 16.41% for TSCV and TSV, respectively. Allometric models only based on tree height were derived from replacing DBH by h in the expression DBH2 x h, according to adjusted expressions depending on DBH classes (ranges of DBH). This finding can facilitate the obtaining of variables such as AGB (carbon stock) and commercial volume of wood over teak plantations in the Coastal Region of Ecuador from only knowing the tree height, constituting a promising method to address large-scale teak plantations monitoring from the canopy height models derived from digital aerial stereophotogrammetry.

Estimating forest stand structure attributes from terrestrial laser scans

Forest stands are often parameterized by vegetation indices such as the Leaf Area Index (LAI). However, other indices (i.e. stand denseness, espacement, canopy density, canopy cover, foliage cover, crown porosity, gap fraction) may better characterize forest structure. Terrestrial and airborne active sensor data has been used to describe canopy structural diversity and provide accurate estimates of forest structure indices. This study uses Terrestrial Laser Scanner (TLS) to characterize forest structure through the above-mentioned indices. The relationship between all of them was studied to assess the extent to which they relate and their capability to properly describe forest stands. A strong correlation was visible between LAI and the canopy density index (r = 0.87 to 0.91 depending on the extraction methods) despite the underevaluated values of the first. Even though more precise LAI estimates were expected from using co-registered multiple scans, the LAI variability proved to be low and correlations with the remaining indices weakened when compared to a single scan approach. An exception was canopy cover, a structural index that disregards the three-dimensionality of the canopy, with which the LAI obtained from multiple scans maintained a strong correlation. This suggests that multiple scanning leads to an unweighted oversampling of the scene, overshadowing its advantages in removing tree occlusions. Weak correlations were visible between classic forest structural indices (basal area density index, espacement index, denseness index) and the rest of the descriptors. Despite this exception, most of the forest indices showed average to strong correlations in-between each other. Therefore, we conclude that a better description of forest stands structure may be achieved through unsegmented single scan point cloud processing in both 3D and 2D space, optical data from the incorporated digital camera being a plus, but not an essential requirement.

Intraspecific Competition Affects Crown and Stem Characteristics of Non-Native Quercus rubra L. Stands in Germany

Accurate guidelines for silvicultural management of exotic tree species in Germany are sparse. For example, northern red oak (Quercus rubra L.) is the most commonly planted exotic deciduous tree species in Germany, but its response to varying levels of competition intensity has not yet been adequately explored. Here, we used terrestrial laser scanning to non-destructively examine the responses of stem and crown characteristics of Quercus rubra to intraspecific competition. A total of 100 dominant red oak trees were investigated in ten pure red oak stands, located in five federal states of Germany. The external stem quality characteristics namely stem non-circularity and bark anomalies decreased with increasing tree competition. Also, the crown characteristics crown volume, crown surface area, maximum crown area, crown length, and branch length declined by the degree of individual tree competition. We conclude that individual tree properties can be controlled by competition intensity, resulting in improved timber quality as shown for other tree species.

AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees

Laser scanning is an effective tool for acquiring geometric attributes of trees and vegetation, which lays a solid foundation for 3-dimensional tree modelling. Existing studies on tree modelling from laser scanning data are vast. However, some works cannot guarantee sufficient modelling accuracy, while some other works are mainly rule-based and therefore highly depend on user inputs. In this paper, we propose a novel method to accurately and automatically reconstruct detailed 3D tree models from laser scans. We first extract an initial tree skeleton from the input point cloud by establishing a minimum spanning tree using the Dijkstra shortest-path algorithm. Then, the initial tree skeleton is pruned by iteratively removing redundant components. After that, an optimization-based approach is performed to fit a sequence of cylinders to approximate the geometry of the tree branches. Experiments on various types of trees from different data sources demonstrate the effectiveness and robustness of our method. The overall fitting error (i.e., the distance between the input points and the output model) is less than 10 cm. The reconstructed tree models can be further applied in the precise estimation of tree attributes, urban landscape visualization, etc. The source code of this work is freely available at https://github.com/tudelft3d/adtree.

Semi-automatic extraction of liana stems from terrestrial LiDAR point clouds of tropical rainforests

Lianas are key structural elements of tropical forests having a large impact on the global carbon cycle by reducing tree growth and increasing tree mortality. Despite the reported increasing abundance of lianas across neotropics, very few studies have attempted to quantify the impact of lianas on tree and forest structure. Recent advances in high resolution terrestrial laser scanning (TLS) systems have enabled us to quantify the forest structure, in an unprecedented detail. However, the uptake of TLS technology to study lianas has not kept up with the same pace as it has for trees. The slower technological adoption of TLS to study lianas is due to the lack of methods to study these complex growth forms. In this study, we present a semi-automatic method to extract liana woody components from plot-level TLS data of a tropical rainforest. We tested the method in eight plots from two different tropical rainforest sites (two in Gigante Peninsula, Panama and six in Nouragues, French Guiana) along an increasing gradient of liana infestation (from plots with low liana density to plots with very high liana density). Our method uses a machine learning model based on the Random Forest (RF) algorithm. The RF algorithm is trained on the eigen features extracted from the points in 3D at multiple spatial scales. The RF based liana stem extraction method successfully extracts on average 58% of liana woody points in our dataset with a high precision of 88%. We also present simple post-processing steps that increase the percentage of extracted liana stems from 54% to 90% in Nouragues and 65% to 70% in Gigante Peninsula without compromising on the precision. We provide the entire processing pipeline as an open source python package. Our method will facilitate new research to study lianas as it enables the monitoring of liana abundance, growth and biomass in forest plots. In addition, the method facilitates the easier processing of 3D data to study tree structure from a liana-infested forest.

Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data

Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm.

Comparison of Mature Douglas-Firs’ Crown Structures Developed with Two Quantitative Structural Models Using TLS Point Clouds for Neighboring Trees in a Natural Regime Stand

The Douglas fir crown structure serves important ecological functions in regulating the ecosystem of the Pacific Northwest (PNW). Mapping and modeling of the Douglas-fir crown has traditionally focused on young plantations or old-growth forests. The crown description in natural regime forests is limited by data availability. Terrestrial laser scanning (TLS) enables the acquisition of crown structural attributes, even in dense forests, at a fine scale. The certical and horizontal distributions of the fine-scale branch attributes, such as branch diameter, branch length, and branch insertion angle, will reflect the crown behaviors towards light resources availability, as a result of neighborhood competition. The main objective of the study is to compare crown structural models of a group of neighboring trees developed with two TLS-based procedures, namely: semi-automatic (Cyclone software) and automatic (TreeQSM) procedures. The estimated crown attributes are the branch diameter, branch length, branch insertion angle, height of branch insertion point, and branch azimuth. The results show that branch azimuth distribution does not differ between TreeQSM and Cyclone for most of the sample trees. However, the TreeQSM and Cyclone identified branches exhibit different distributions of insertion height. A paired t-test indicates no difference between the mean branch diameter of Cyclone and TreeQSM at an individual tree level. However, Cyclone estimated that the branch length and branch insertion angle are 0.49 m and 9.9° greater than the TreeQSM estimates, respectively. Repeat measurements of the analysis of variance (ANOVA) suggest that the height along the stem is an influential factor of the difference between the Cyclone and TreeQSM branch diameter estimates. To test whether TLS-based estimates are within the ranges of the previous observations, we computed the tree crown attributes of second- and old-growth trees using Monte Carlo simulations for diameter at breast height (DBH) class 50–55 cm, 60–65 cm, and 85–105 cm. We found that the crown attributes estimated from both of the TLS-based methods are between the simulated second- and old-growth trees, except for DBH 85–105 cm. The TLS-based crown structural models show increasingly diverse distributions of branch insertion angles and increasing branch exclusion as DBH increases. Cyclone-based crown structural models are consistent with previous studies. However, TreeQSM-based crown structural models omitted a significant number of branches and generated crown structures with reduced plausibility.

Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R 2 = 0.92–0.93) than traditional pantropical models ( R 2 = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R 2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

How a measure of tree structural complexity relates to architectural benefit-to-cost ratio, light availability, and growth of trees

Aboveground tree architecture is neither fully deterministic nor random. It is likely the result of mechanisms that balance static requirements and light-capturing efficiency. Here, we used terrestrial laser scanning data to investigate the relationship between tree architecture, here addressed using the box-dimension (D b), and the architectural benefit-to-cost ratio, the light availability, and the growth of trees. We detected a clear relationship between D b and the benefit-to-cost ratio for the tested three temperate forest tree species (Fagus sylvatica L., Fraxinus excelsior L., and Acer pseudoplatanus L.). In addition, we could also show that D b is positively related to the growth performance of several tropical tree species. Finally, we observed a negative relationship between the strength of competition enforced on red oak (Quercus rubra L.) trees and their D b. We therefore argue that D b is a meaningful and integrative measure that describes the structural complexity of the aboveground compartments of a plant as well as its relation to structural efficiency (benefit-to-cost ratio), productivity, and growing conditions (competition or availability of light).

Relationships between Satellite-Based Spectral Burned Ratios and Terrestrial Laser Scanning

Three-dimensional point data acquired by Terrestrial Lidar Scanning (TLS) is used as ground observation in comparisons with fire severity indices computed from Landsat satellite multi-temporal images through Google Earth Engine (GEE). Forest fires are measured by the extent and severity of fire. Current methods of assessing fire severity are limited to on-site visual inspection or the use of satellite and aerial images to quantify severity over larger areas. On the ground, assessment of fire severity is influenced by the observers’ knowledge of the local ecosystem and ability to accurately assess several forest structure measurements. The objective of this study is to introduce TLS to validate spectral burned ratios obtained from Landsat images. The spectral change was obtained by an image compositing technique through GEE. The 32 plots were collected using TLS in Wood Buffalo National Park, Canada. TLS-generated 3D points were converted to voxels and the counted voxels were compared in four height strata. There was a negative linear relationship between spectral indices and counted voxels in the height strata between 1 to 5 m to produce R2 value of 0.45 and 0.47 for unburned plots and a non-linear relationship in the height strata between 0 to 0.5m for burned plots to produce R2 value of 0.56 and 0.59. Shrub or stand development was related with the spectral indices at unburned plots, and vegetation recovery in the ground surface was related at burned plots. As TLS systems become more cost efficient and portable, techniques used in this study will be useful to produce objective assessments of structure measurements for fire refugia and ecological response after a fire. TLS is especially useful for the quick ground assessments which are needed for forest fire applications.

Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales

The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. In this study, we extend the optimal scale method to the multi-optimal-scale method, adaptively selecting multiple optimal scales for each point in the tree point cloud to increase the distinctiveness of extracted geometric features. Compared with the optimal scale method, our method achieves higher separation accuracy. Compared with the multi-scale method, our method achieves more stable separation accuracy with a limited number of optimal scales. The running time of our method is greatly reduced when the optimization strategy is applied.

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.

Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2-, and Landsat-7-derived vegetation indices (VIs), as well as ALOS-2 PALSAR-2- and Sentinel-1-derived backscatter intensities with a terrestrial laser scanner (TLS) and conventionally measured forest structure parameters acquired from 25 field plots in a tropical montane cloud forest in Kafa, Ethiopia. Results showed that canopy gap-related forest structure parameters had their highest correlation (|r| = 0.4 − 0.48) with optical sensor-derived VIs, while vegetation volume-related parameters were mainly correlated with red-edge- and short-wave infrared band-derived VIs (i.e., inverted red-edge chlorophyll index (IRECI), normalized difference moisture index), and synthetic aperture radar (SAR) backscatters (|r| = −0.57 − 0.49). Using stepwise multi-linear regression with the Akaike information criterion as evaluation parameter, we found that the fusion of different SRS-derived variables can improve the estimation of field-measured structural parameters. The combination of Sentinel-2 VIs and SAR backscatters was dominant in most of the predictive models, while IRECI was found to be the most common predictor for field-measured variables. The statistically significant regression models were able to estimate cumulative plant area volume density with an R2 of 0.58 and with the lowest relative root mean square error (RRMSE) value (0.23). Mean gap and number of gaps were also significantly estimated, but with higher RRMSE (R2 = 0.52, RRMSE = 1.4, R2 = 0.68, and RRMSE = 0.58, respectively). The models showed poor performance in predicting tree density and number of tree species (R2 = 0.28, RRMSE = 0.41, and R2 = 0.21, RRMSE = 0.39, respectively). This exploratory study demonstrated that SRS variables are sensitive to retrieve structural differences of tropical forests and have the potential to be used to upscale biodiversity relevant field-based forest structure estimates.