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Many analyses in ecology and forestry require wood volume estimates of trees. However, non-destructive measurements are not straightforward because trees are differing in their three-dimensional structures and shapes. In this paper we compared three methods (one voxel-based and two cylinder-based methods) for wood volume calculation of trees from point clouds obtained by terrestrial laser scanning. We analysed a total of 24 young trees, composed of four different species ranging between 1.79 m to 7.96 m in height, comparing the derived volume estimates from the point clouds with xylometric reference volumes for each tree. We found that both voxel- and cylinder-based approaches are able to compute wood volumes with an average accuracy above 90% when compared to reference volumes. The best results were achieved with the voxel-based method (r2 = 0.98). Cylinder-model based methods (r2 = 0.90 and 0.92 respectively) did perform slightly less well but offer valuable additional opportunities to analyse structural parameters for each tree. We found that the error of volume estimates from point clouds are strongly species-specific. Therefore, species-specific parameter sets for point-cloud based wood volume estimation methods are required for more robust estimates across a number of tree species.

African savanna elephants (Loxodonta africana) can substantially modify their habitat through their interactions with woody vegetation. Nonetheless, the scale, intensity and characteristics of these relations are not yet fully understood. Consequently, it is unclear how vegetation-megafauna interactions can be disrupted by external factors, such as land management. This study attempted to quantify and characterize structural changes in vegetation caused by elephants, from landscape to tree level scales. We applied multi-scale geospatial tools, including airborne (ALS) and terrestrial laser scanning (TLS), to address the following questions: (1) How do elephants shape landscape level vegetation structure in conservation areas? (2) Are the impacts of elephants evident on individual tree architecture? Our study area was located at the Taita Hills Wildlife Sanctuary in South-eastern Kenya. The occurrence of elephants was estimated using elephant observation records and proximity to elephant tracks. Landscape level structure was assessed using tree density maps calculated based on individually detected treetops from ALS data. Next, TLS measurements of 72 trees were processed using quantitative structural modelling to characterize their architecture. Our results demonstrate a widespread influence of elephants on both landscape and tree level structural characteristics. This influence was strongly mediated by management, as we observed differences in vegetation structure inside and outside conservation areas. Tree density was up to 42% lower (5.84 trees/ha) in conservation areas than in non-conservation areas (10.17 trees/ha). Trees were relatively larger with closer proximity to elephant tracks, while smaller trees were more often observed in areas further away from elephants. At an architectural level, trees closer to elephant tracks had lower ratio between the crown length and the tree height, demonstrating a substantial influence of elephants on the morphological characteristics of trees. Our results highlight the importance of accounting for vegetation fauna interactions when planning conservation areas in African savannahs. • We quantified structural changes in vegetation caused by elephants at multiple scales. • The impacts of elephants were assessed using airborne and terrestrial LiDAR. • We demonstrate the influence of elephants on landscape and individual tree levels. • Tree density was up to 42% lower in conservation areas than in non-conservation areas. • Trees consumed by elephants had reduced crown length-to-height ratio. [ABSTRACT FROM AUTHOR]

Buttressed trees provide mechanical support for themselves and offer essential ecological functions, such as nutrient acquisition, while being one of the largest sources of volume or biomass estimation variation in tropical forests. In this study, we collected 51 buttressed trees from (33) Democratic Republic of Congo, (12) Indonesia, and (6) Costa Rica, including (48) point clouds, and (3) destructive measurement. Specifically, we compared the performance of the Alpha Shape Algorithm (ASA) and the Slice Triangulation (ST) method on buttress volume estimation based on 30 point clouds with two species. Six point clouds from Costa Rica were used to validate the 3D surface reconstruction method. Meanwhile, we developed three allometric models based on 36 point clouds: a diameter above the buttress-based (DAB, 39 to 203 cm) model, a diameter computed from the non-convex area (D area130) model, and the convex hull perimeter (D convex130) of the breast height model. The developed models were validated with independent data, including (6) point clouds and (3) destructive measurements, to highlight the broader contextualization and application of these methods. Volume estimated by the ASA and ST showed a high agreement with the reference volume acquired using the Smalian formula (relative RMSE of 0.07 and 0.11, respectively, regardless of species effect). ASA was also robust when modeling trees with more and shallower horizontal buttresses. D area130 was the most accurate predictor to estimate buttress volume, with a lower Akaike information criterion (−66.25) than DAB (−59.55) and D convex130 (30.56); however, DAB and D area130 (relative RMSE of 0.21 and 0.23, respectively) showed similar performance when validated with independent datasets. Our results indicate that the ASA approach performs better than both the ST and allometric models used in this study. Furthermore, the ASA method can help correct the bias in the present and past estimates of volume and biomass of large trees, which are foundational components for understanding biomass allocation and dynamics in tropical forests contemporary fields. • The alpha shape and slice triangulation methods show similar performance on buttress volume estimation. • The alpha shape algorithm overestimates volume when the tree presents more and shallower horizontal buttresses. • Concerning allometric models, D area130 is the most robust predictor to estimate buttress volume. • The 3D surface reconstruction methods outperform traditional allometric models to estimate buttress volume. [ABSTRACT FROM AUTHOR]

Due to their long lifespan, trees and bushes develop higher order of branches in a perennial manner. In contrast to a tall tree, with a clearly defined main stem and branching order, a bush is shorter and has a less apparent main stem and branching pattern. To address the developmental basis of these two forms, we studied several naturally occurring architectural variants in silver birch ( Betula pendula ). Using a candidate gene approach, we identified a bushy kanttarelli variant with a loss-of-function mutation in the BpMAX1 gene required for strigolactone (SL) biosynthesis. While kanttarelli is shorter than the wild type (WT), it has the same number of primary branches, whereas the number of secondary branches is increased, contributing to its bush-like phenotype. To confirm that the identified mutation was responsible for the phenotype, we phenocopied kanttarelli in transgenic BpMAX1::RNAi birch lines. SL profiling confirmed that both kanttarelli and the transgenic lines produced very limited amounts of SL. Interestingly, the auxin (IAA) distribution along the main stem differed between WT and BpMAX1::RNAi . In the WT, the auxin concentration formed a gradient, being higher in the uppermost internodes and decreasing toward the basal part of the stem, whereas in the transgenic line, this gradient was not observed. Through modeling, we showed that the different IAA distribution patterns may result from the difference in the number of higher-order branches and plant height. Future studies will determine whether the IAA gradient itself regulates aspects of plant architecture., Competing Interests: Competing interests statement:The authors declare no competing interest.

Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Herrero-Huerta, Raumonen and Gonzalez-Aguilera.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Annals of Botany Company.)

This paper introduces a prototype of ClothFace technology, a battery-free textile-based handwriting recognition platform that includes an e-textile antenna and a 10 × 10 array of radio frequency identification (RFID) integrated circuits (ICs), each with a unique ID. Touching the textile platform surface creates an electrical connection from specific ICs to the antenna, which enables the connected ICs to be read with an external UHF (ultra-haigh frequency) RFID reader. In this paper, the platform is demonstrated to recognize handwritten numbers 0-9. The raw data collected by the platform are a sequence of IDs from the touched ICs. The system converts the data into bitmaps and their details are increased by interpolating between neighboring samples using the sequential information of IDs. These images of digits written on the platform can be classified, with enough accuracy for practical use, by deep learning. The recognition system was trained and tested with samples from six volunteers using the platform. The real-time number recognition ability of the ClothFace technology is demonstrated to work successfully with a very low error rate. The overall recognition accuracy of the platform is 94.6% and the accuracy for each digit is between 91.1% and 98.3%. As the solution is fully passive and gets all the needed energy from the external RFID reader, it enables a maintenance-free and cost-effective user interface that can be integrated into clothing and into textiles around us.

Background and Aims: Functional-structural plant models (FSPMs) allow simulation of tree crown development as the sum of modular (e.g. shoot-level) responses triggered by the local environmental conditions. The actual process of space filling by the crowns can be studied. Although the FSPM simulations are at organ scale, the data for their validation have usually been at more aggregated levels (whole-crown or whole-tree). Measurements made by terrestrial laser scanning (TLS) that have been segmented into elementary units (internodes) offer a phenotyping tool to validate the FSPM predictions at levels comparable with their detail. We demonstrate the testing of different formulations of crown development of Scots pine trees in the LIGNUM model using segmented TLS data., Methods: We made TLS measurements from four sample trees growing in a forest on a relatively poor soil from sapling size to mature stage. The TLS data were segmented into internodes. The segmentation also produced information on whether needles were present in the internode. We applied different formulations of crown development (flushing of buds and length of growth of new internodes) in LIGNUM. We optimized the parameter values of each formulation using genetic algorithms to observe the best fit of LIGNUM simulations to the measured trees. The fitness function in the estimation combined both tree-level characteristics (e.g. tree height and crown length) and measures of crown shape (e.g. spatial distribution of needle area)., Key Results: Comparison of different formulations against the data indicates that the Extended Borchert-Honda model for shoot elongation works best within LIGNUM. Control of growth by local density in the crown was important for all shoot elongation formulations. Modifying the number of lateral buds as a function of local density in the crown was the best way to accomplish density control., Conclusions: It was demonstrated how segmented TLS data can be used in the context of a shoot-based model to select model components.

Detailed and realistic tree form generators have numerous applications in ecology and forestry. For example, the varying morphology of trees contributes differently to formation of landscapes, natural habitats of species, and eco-physiological characteristics of the biosphere. Here, we present an algorithm for generating morphological tree "clones" based on the detailed reconstruction of the laser scanning data, statistical measure of similarity, and a plant growth model with simple stochastic rules. The algorithm is designed to produce tree forms, i.e., morphological clones, similar (and not identical) in respect to tree-level structure, but varying in fine-scale structural detail. Although we opted for certain choices in our algorithm, individual parts may vary depending on the application, making it a general adaptable pipeline. Namely, we showed that a specific multipurpose procedural stochastic growth model can be algorithmically adjusted to produce the morphological clones replicated from the target experimentally measured tree. For this, we developed a statistical measure of similarity (structural distance) between any given pair of trees, which allows for the comprehensive comparing of the tree morphologies by means of empirical distributions describing the geometrical and topological features of a tree. Finally, we developed a programmable interface to manipulate data required by the algorithm. Our algorithm can be used in a variety of applications for exploration of the morphological potential of the growth models (both theoretical and experimental), arising in all sectors of plant science research., (© The Authors 2017. Published by Oxford University Press.)