Tree Dimensions Research Articles

Matching Trees to Streets by Street Type: A Case Study of Street Tree Suitability and Services in a Highly Urbanized City

Street trees are a representative form of urban green space that play an important role in mitigating the environmental impact of urbanization. Planting the right tree in the right place in urban streetscapes can improve tree health and ecosystem services. Here, we propose a novel approach to selecting appropriate street trees using street type classifications. In the highly urbanized area of Uijeongbu City, South Korea, 221.9 km of streets with 19,717 street trees were classified into 12 types based on road width, aspect ratio, land use, and the presence of power lines. Appropriate tree species were selected for each street type, taking into account tree traits and functions as well as street environments. Then, we analyzed the structure and ecosystem-regulating services of street trees by type, also comparing the services of appropriate and non-appropriate trees. As a result, all 12 street types were identified, but their distribution was uneven. Tree dimension was the key factor in determining appropriate species, and, for the second most common street type, characterized by narrow roads, low aspect ratios, and power lines, only four appropriate species were identified, indicating an urgent need for more options. Additionally, the most dominant species accounted for over 20%, averaging 44% across the 12 street types, further highlighting the necessity of introducing more diverse tree species. Overall, appropriate street trees generally provided higher service efficiency compared to non-appropriate trees across four ecosystem regulating services. These findings emphasize the need for policies and guidelines that promote street tree diversity and enhance the ecological benefits of street trees. This study provides a foundation for developing sustainable street tree management strategies that contribute to healthier and more resilient urban streetscapes.

Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer

Traditional forest inventory supplies essential data for forest monitoring and management, including tree species, but obtaining individual tree-level information is increasingly crucial. Airborne Light Detection and Ranging (LiDAR) with multispectral observation offers rich information for improved forest inventory mapping with reliable individual tree attributes. Although deep learning techniques have shown promise in tree species classification, they are not sufficiently explored for individual tree-level classification using low-density (less than 30 point/m2) Airborne Multispectral LiDAR (AML) data. This study therefore explores the feasibility of using a deep learning (DL) framework for processing low-density AML point clouds to enhance tree species classification in challenging forest environments. A point-based deep learning network with a dual-branch mechanism combined Cross-Branch Attention modules named Attribute-Aware Cross-Branch (AACB) Transformer is designed for AML data to better differentiate tree species from delineated individual trees. In addition, a channel merging approach is introduced, which is suited to prepare the training samples of deep learning networks and reduces the computational costs. This study was tested with an average 9 points/m2 AML point cloud for 6 tree species including Populus tremuloides, Larix laricina, Acer saccharum, Picea abies, Pinus resinosa, and Pinus strobus from a Canadian mixed forest. The overall accuracies achieved 83.1 %, 85.8 %, and 95.3 % at species, genus, and leaf-type levels, respectively. The comparison between the proposed method and other widely used tree species classification methods demonstrates the effectiveness of the proposed approach in enhancing tree species classification accuracy. We discuss potentials and remaining challenges, and our findings allow to further improve tree species classification of low-density AML point clouds by DL technology.

Characterizing the Structure of Online Conversations Across Reddit

The proliferation of social media platforms has afforded social scientists unprecedented access to vast troves of data on human interactions, facilitating the study of online behavior at an unparalleled scale. These platforms typically structure conversations as threads, forming tree-like structures known as ''discussion trees.'' This paper examines the structural properties of online discussions on Reddit by analyzing both global (community-level) and local (post-level) attributes of these discussion trees. We conduct a comprehensive statistical analysis of a year's worth of Reddit data, encompassing a quarter of a million posts and several million comments. Our primary objective is to disentangle the relative impacts of global and local properties and evaluate how specific features shape discussion tree structures. The results reveal that both local and global features contribute significantly to explaining structural variation in discussion trees. However, local features, such as post content and sentiment, collectively have a greater impact, accounting for a larger proportion of variation in the width, depth, and size of discussion trees. Our analysis also uncovers considerable heterogeneity in the impact of various features on discussion structures. Notably, certain global features play crucial roles in determining specific discussion tree properties. These features include the subreddit's topic, age, popularity, and content redundancy. For instance, posts in subreddits focused on politics, sports, and current events tend to generate deeper and wider discussion trees. This research enhances our understanding of online conversation dynamics and offers valuable insights for both content creators and platform designers. By elucidating the factors that shape online discussions, our work contributes to ongoing efforts to improve the quality and effectiveness of digital discourse.

Utilizing unsupervised machine learning to uncover novel phenogroups within the broad QRS complex

Abstract Background Conduction disease can manifest as a broad QRS complex on the ECG. Broad QRS is conventionally categorised as left bundle branch block (LBBB) and right bundle branch block (RBBB) or non-specific intraventricular conduction delay (IVCD), based on QRS morphology. These subgroups were coined over a century ago and have been relevant for heart failure with reduced ejection fraction and cardiac resynchronization therapy (CRT). However, there may be more precise phenogroups underlying broad QRS complexes. Purpose Using unsupervised machine learning to define novel broad QRS phenogroups and characterise them using clinical variables and disease outcomes. Methods We extracted 51 relevant features using a Variational Autoencoder from 47,456 median-beat ECGs with broad QRS intervals (QRS ≥ 130ms) from a secondary care cohort. These were input into Discriminative Dimensionality Reduction via Learning a Tree (DDRTree), a clustering method that models the continuum of heterogeneity by projecting the underlying distribution as a tree structure in a low-dimensional space, while assigning distinct branch/cluster labels. Results Six distinct phenogroups were identified within broad QRS morphologies (Figure-1). Two branches were RBBB dominant (Branch 1: more males, high-comorbidity; Branch 6: low-comorbidity/healthy-RBBB). Two branches were LBBB dominant (Branch 4: mixed IVCD with CHB burden; Branch 5: more females, CRT responders). Two branches were IVCD/mixed-phenotype (Branch 2: IVCD-dominant, high risk of future cardiovascular events; Branch 3: average/core branch). Within LBBB, Branch 4 and 5 showed similar trends with improving echocardiography profiles and CRT response when adjusted for covariates, each compared against the core branch. Branch 5 was distinct due its high risk of future HF (HR 1.19 [1.07-1.33] p < 0.01), cardiovascular death (HR 1.35 [1.20-1.50] p < 0.0001), all-cause mortality (HR 1.08 [1.01-1.16] p < 0.05) and CHB (HR 1.35 [1.16-1.56] p < 0.0001) (Figure-2). Conversely within RBBB, Branch 1 and 6 showed stark differences across various traits. Branch 1 captured a high-risk profile associated to higher risk of cardiovascular death (HR 1.37 [1.24-1.52] p < 0.0001), all-cause mortality (HR 1.20 [1.12-1.29] p < 0.0001) and CHB (HR 1.15 [1.01-1.32] p < 0.05) (Figure-2), while Branch 6 retained a low-risk phenotype. The tree dimensions also revealed relevant trends in cardiac anatomy and valvular dysfunction; the top-left region in Figure-1 associated with higher left ventricular ejection fraction and lower left ventricular end-diastolic dimension, lower left ventricular end-systolic dimension and a greater CRT response. Conclusion Through unsupervised methods we have identified conduction disease phenogroups with additive value for disease prognosis and CRT response prediction beyond traditional LBBB and RBBB labels. These novel phenogroups may help guide precision care for patients with a broad QRS complex.Figure-1Figure-2

Archival Aerial Photographs of Africa: Present Potential and Imagining a Machine-Learning Future

AbstractArchival aerial photographs are a unique but underused and potentially game-changing source to study twentieth-century environmental and climate change dynamics. While satellite imagery with comparable high resolution appeared only in the early twenty-first century, archival aerial imagery with native sub-1-meter resolution became ubiquitous in the 1940s. Archival aerial photography therefore quadruples the time depth of high-resolution analysis to eighty years, allowing for a more reliable identification of structural trends. Moreover, the greater time-depth brings into focus the Great Acceleration that started in the 1940s, and virtually in real time. The article uses a human manual analysis of a sample from two time series (1943 and 1971) of archival photographs of the Oshikango area of Namibia (see Figure 1) to demonstrate how aerial photography complements conventional datasets. Namibia was one of the first places in colonial Africa where what subsequently became the standard protocol for “aerial mapping” was used and for which the imagery and the “flight plans” have survived. The standard protocol makes the imagery compatible with any archival aerial photography from the 1940s to 1990s and the flight plans contain key information to identify, interpret, and combine the individual photographs into orthomosaics. Although the use of manual analysis of aerial photography is not new, unlocking the full explanatory potential of high-resolution mass data requires machine reading and analysis. Current machine reading methods, however, are based on the pixel method, which identifies such features as farms, water holes, and trees only as low-resolution pixel aggregates. In contrast, the object method of machine analysis, combined with Geographical Information Systems (GIS) technology to unlock the sub-1-meter native resolution of historical aerial photography, renders visible individual trees and other features, including their precise location and size, allowing for the dimensions of trees and other features to be measured between different time series of images. The interrelationships between different features in the environment can thus be assessed more precisely in space and over time, for example comparing tree growth and surface water sources. A major challenge is that the object method used for high resolution geospatial imagery cannot be easily applied to monochromatic archival aerial photography because it has been designed for analyzing multispectral satellite imagery. As discussed in the article, using the manual sample as a training data set for an experimental machine-learning protocol demonstrates proof of concept for automatically extracting such features as farms, water holes and trees as individual objects from archival aerial photography. This increases the time depth of available high-resolution land use, environmental, and climate data from 2000 back to the 1940s and provides a base line for the Great Acceleration and brings the massive changes from the 1940s through the 1990s in focus as captured in aerial photography.

Tree growth‐forms reveal dominant browsers shaping the vegetation

Abstract Plants adopt particular growth‐forms when they are exposed to extreme environmental conditions. In this study, we describe a unique woody plant growth‐form induced by large mammalian herbivores and discuss that this growth‐form could have evolved as a strategy for escaping the browser zone in herbivore driven ecosystems. We analysed responses of key architectural and morphological attributes (branching and thorn density, tree dimensions, presence of flowers and fruits) of three Eurasian spiny tree species (Malus sylvestris, Prunus cerasifera and Pyrus communis) to different levels of browsing by large herbivores in the temperate Białowieża Forest, Poland. Under high browsing pressure, studied trees displayed two distinct forms of the crown: a bottom sterile part developing into a densely branched structure with high density of thorns (‘cage‐form’), and an upper reproductive part that escaped from herbivore control (‘escaped‐form’). The size of cage‐form influenced the feeding behaviour of red deer (Cervus elaphus) by increasing the time deer spend foraging and increasing the bite rate. The height at which cages started to escape and their diameter matched with foraging reach of red deer. Synthesis. We argue that the frequency and cage dimensions of this woody growth‐form in the landscape could inform on the type and intensity of recent herbivory. Moreover, its distinctive inducibility suggests that this growth‐form did not emerge recently under anthropogenic pressure but could be the legacy of ancient herbivory effects. Observational evidence suggests that this growth‐form emerged in several herbivore‐driven systems around the globe and may be used to identify the dominant herbivores that control vegetation structure in these ecosystems. Read the free Plain Language Summary for this article on the Journal blog.

Phylogenetic Effect on Tree Radial Growth Depends on Drought and Tree Sizes

AbstractTree radial growth is one of the most direct measures of tree performance and is also sensitive to climate. Growth performance is the consequence of the interplay between ecological and evolutionary processes. However, the effect of the evolutionary relatedness among species (i.e., phylogeny) on tree radial growth, especially under stressful conditions, remains largely unknown. Furthermore, there is still no ecological evidence for the influence of phylogeny on tree growth across different tree attributes (i.e., tree diameter variation and tree canopy height) and topographic habitat types. We used Blomberg's K to quantify the tree growth phylogenetic signal (TGPS) using two long‐term dendrometer data sets: one a continuous census of 225 tree species at 3‐month intervals in a tropical forest in southwest China from 2009 to 2017; the other, 12 tree species measured at 6‐month intervals in a temperate forest in Washington State, USA from 2013 to 2019. We found that TGPS values were higher in the temperate forest than in the tropical forest. Precipitation, tree diameter, canopy strata, and habitat types all influenced TGPS values. TGPS values were significantly (p < 0.05) and negatively related to precipitation in Xishuangbanna, and the three of four tree diameter classes in the temperate forest, respectively. Stressful growing conditions arose from either based on low precipitation or among large‐diameter trees competing with each other in the upper canopy led to phylogenetic conservatism in trees' radial growth performance. We conclude that phylogeny is pivotal to understanding the growth response differences among species and their responses to climate variability.

Exploring Urban Greenery: A Case Study of Roadside Trees in Pokhara Metropolitan City

Roadside trees play a crucial role as important components of urban greenery, significantly enhancing the overall quality of cities and ultimately contributing to the quality of life for residents and visitors alike. This study examines the condition, maintenance, and proximity of roadside trees to electric wire corridors in Pokhara Metropolitan City, Nepal. This study employs field surveys, categorization based on tree attributes, and mapping techniques to analyze the distribution and characteristics of roadside trees. Out of approximately 1500 trees initially collected, 1435 were processed for further analysis. Trees were classified by species, height, condition, and the presence of fencing or resting structures (Chautaro), with proximity to electric wire corridors also assessed. Findings reveal a significant presence of trees in good condition, predominantly Dhupi, Kapur and Ashoka species, with a notable percentage requiring maintenance. A substantial number of trees were found in close proximity to electric wires, posing potential hazards. Mapping techniques facilitated visualizing the spatial distribution of trees, aiding in urban planning and management strategies. The study underscores the importance of sustainable urban greening practices and the need for proactive measures to address challenges related to tree maintenance and safety in urban environments like Pokhara Metropolitan City. This study emphasizes the pivotal role of roadside trees in enhancing the aesthetic appeal and quality of city life, positioning them as decisive factors in shaping urban environments and fostering a welcoming atmosphere for residents and visitors.

Accurate Calculation of Upper Biomass Volume of Single Trees Using Matrixial Representation of LiDAR Data

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection and Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, each of which is further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. For a tree dimensions estimation, the study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees. The maximum absolute quantitative accuracy equals 0.49 m, and the maximum relative absolute error equals 0.29%.

Benchmarking Under- and Above-Canopy Laser Scanning Solutions for Deriving Stem Curve and Volume in Easy and Difficult Boreal Forest Conditions

The use of mobile laser scanning for mapping forests has scarcely been studied in difficult forest conditions. In this paper, we compare the accuracy of retrieving tree attributes, particularly diameter at breast height (DBH), stem curve, stem volume, and tree height, using six different laser scanning systems in a managed natural boreal forest. These compared systems operated both under the forest canopy on handheld and unmanned aerial vehicle (UAV) platforms and above the canopy from a helicopter. The complexity of the studied forest sites ranged from easy to difficult, and thus, this is the first study to compare the performance of several laser scanning systems for the direct measurement of stem curve in difficult forest conditions. To automatically detect tree stems and to calculate their attributes, we utilized our previously developed algorithm integrated with a novel bias compensation method to reduce the overestimation of stem diameter arising from finite laser beam divergence. The bias compensation method reduced the absolute value of the diameter bias by 55–99%. The most accurate laser scanning systems were equipped with a Velodyne VLP-16 sensor, which has a relatively low beam divergence, on a handheld or UAV platform. In easy plots, these systems found a root-mean-square error (RMSE) of below 10% for DBH and stem curve estimates and approximately 10% for stem volume. With the handheld system in difficult plots, the DBH and stem curve estimates had an RMSE under 10%, and the stem volume RMSE was below 20%. Even though bias compensation reduced the difference in bias and RMSE between laser scanners with high and low beam divergence, the RMSE remained higher for systems with a high beam divergence. The airborne laser scanner operating above the forest canopy provided tree attribute estimates close to the accuracy of the under-canopy laser scanners, but with a significantly lower completeness rate for stem detection, especially in difficult forest conditions.

Estimating aboveground biomass of tropical urban forests with UAV-borne hyperspectral and LiDAR data

Urban trees and forests can contribute to climate change mitigation by sequestering carbon in their living tissues, with aboveground biomass (AGB) playing a pivotal role. This study explores the capability of UAV-borne hyperspectral and LiDAR data for estimating AGB in tropical urban forests. Structural attributes of trees, such as diameter at breast height (DBH), total height, and wood density, were collected from over 5600 individuals, forming a comprehensive AGB dataset. Our methodology included two primary AGB estimation strategies: an area-based strategy that correlated AGB with hyperspectral and canopy height data across various grid sizes and an individual tree crown (ITC)-based method that integrated canopy height, spectral signatures of individual trees and crown area. The findings indicate that increasing the grid size from 10 m to 50 m improved the R2 from 0.24 ± 0.04–0.61 ± 0.13, mainly due to reduced border effects. Furthermore, integrating canopy height and hyperspectral data enhanced the R2 of AGB estimates from 0.61 ± 0.13–0.70 ± 0.09 for a 50 × 50 m grid. Crucially, wavelengths centered at the green peak and red-edge were identified as key bands in AGB retrieval. Integrating hyperspectral and LiDAR data did not significantly enhance results for individual trees, where AGB was closely linked to tree height and crown area. This study underscores the potential of utilizing integrated UAV-borne sensors for biomass assessment in urban forest settings.

A dataset of 40’000 trees with section-wise measured stem diameter and branch volume from across Switzerland

Estimating growing stock is one of the main objectives of forest inventories. It refers to the stem volume of individual trees which is typically derived by models as it cannot be easily measured directly. These models are thus based on measurable tree dimensions and their parameterization depends on the available empirical data. Historically, such data were collected by measurements of tree stem sizes, which is very time- and cost-intensive. Here, we present an exceptionally large dataset with section-wise stem measurements on 40’349 felled individual trees collected on plots of the Experimental Forest Management project. It is a revised and expanded version of previously unpublished data and contains the empirically derived coarse (diameter ≥7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range and can thus support estimations and verification of volume functions also outside Switzerland including the derivation of whole tree volume in a consistent manner.

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called “pixels weighted differential gradient”, to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction.

Visible and invisible forest: The cultivation of shade in Winnipeg, Manitoba, Canada

A consideration of the site-specific spatial experience created by urban trees and their cultural dimensions can enrich climate adaptive tree planting strategies in Winnipeg, a city of 850,000 in the central prairies of Canada. The paper begins with an introduction to traditional urban tree planting types and analyses the range of tree planting techniques that currently define public spaces in Winnipeg. A review of the recently published Urban Forest Strategy for Winnipeg highlights current strengths and challenges to climate change related tree planting in the city. Urban tree planting strategies and practices demonstrate a focus on quantifiable goals such as canopy coverage, number of trees planted or ecosystem services, with little reference to how trees define places. However, cities are constituted by the visible forest-the form and patterns of how trees are planted and the spaces they create. They are also shaped by the invisible forest, the diverse ways in which trees evoke different functions, values, and modes of occupation to different people at different times. Two basic approaches to urban tree planting will enrich climate-related tree planting initiatives by synthesizing the visible and invisible dimensions of the urban forest: Prioritizing the collective planting of trees as opposed to the single specimen and acknowledging the cultural dimensions of trees. Two design propositions from students at University of Manitoba demonstrate how trees can articulate the diverse ways people interact with trees through their spatial configuration and planting techniques. One draws upon tree types that acknowledge local agricultural tree planting strategies and the second responds to historical and contemporary Indigenous relationships to riparian trees. Acknowledging the planting of trees as a complex interplay between spatial, ecological, and cultural specificity allows for the communication of new values for the design and stewardship of urban trees and the provision of shade in a climate adaptive city.

Effects of early respacing on physico-mechanical properties of naturally regenerated Picea sitchensis in Great Britain

Natural regeneration can reduce costs compared with replanting. However, its use requires knowledge about how either active or passive management will affect the balance between quality and quantity in the timber supply. This study aimed to quantify the effects of respacing on volume recovery and wood properties. Two British forest experiments using Picea sitchensis with various respacing distances and an un-respaced control were assessed 21–22 years after the treatments were applied. Tree dimensions were measured and used to quantify slenderness, merchantable volume, and sawlog volume. Wood properties were assessed on a sub-sample using mechanical testing. Generalised linear mixed models were used to examine differences between treatments and sites. Respacing decreased slenderness and increased relative sawlog volume and branch size. Wider respacing reduced wood strength and the widest respacing reduced wood stiffness. Respacing did not affect wood density. However, at the relatively low productivity sites considered here, respacing to 2.1 m represented the best compromise for current markets. In summary, not respacing improved some wood properties but reduced tree stability and the proportion and volume of sawlogs, which will negatively affect forest value.

Applying an Optimum Bucking Method to Comparing the Volume and Value Recovery of Cut-to-Length and Tree-Length Merchandizing Systems in Piedmont and the Coastal Plain

Dynamic prices and markets create value for contractors who can readily evaluate the gross and net income differences for alternative merchandizing systems. The majority of the southern U.S.A. relies on tree-length merchandizing, with occasional identification and merchandizing of logs for a specific market or specific tree dimensions or qualities. Cut-to-length (CTL) merchandizing has generated more value when compared to tree-length (TL) marketing, but these comparisons have been limited to specific stands and markets (specifications and prices). The study objective was to demonstrate a process for evaluating cut-to-length and tree-length merchandizing systems in their production of gross value by applying a dynamic programming stem-level optimum bucking approach that maximizes the stem value given specific market conditions. TL merchandizing resulted in a better volume recovery for both regions, but the value recovery was better for CTL merchandizing. Observing the value recovery by diameter class, DSH classes of up to 100 mm had a similar value in both merchandizing systems, but CTL merchandizing yielded a greater or similar value per cubic meter across the range for larger tree sizes. Access to tree data and merchandizing tools needs to be addressed so wood suppliers and landowners may benefit from stem optimization and sensor technology being embedded into modern harvesters and processors.

Environmental factors differentially influence species distributions across tree size classes in a dry evergreen forest in Sakaerat Biosphere Reserve, northeastern Thailand

ABSTRACT Plant communities in tropical forests are influenced by multiple environmental variables. For trees, soil and topographic characteristics are key factors that impact the establishment of many species. In this study, the impacts of soil properties and topography on forest structure, species composition, α- and β-diversity, and tree species distribution across size classes were investigated in a dry evergreen forest (DEF) in the Sakaerat Biosphere Reserve, northeastern Thailand. Within a 16 ha permanent plot, all trees with a diameter at breast height (DBH) ≥ 1 cm were recorded. Tree species distributions mostly respected gradients of plot elevation and soil nutrient, including available P; these variables were strongly associated for most size classes. Structural attributes (basal area and tree density) of trees with a DBH of 5–10 cm were highly sensitive to these variables. For trees with a DBH of 2–5 cm, α-diversity was negatively associated with available P while β-diversity was associated with topography and available P. These findings demonstrate the strong influence of topographic and edaphic variables in shaping the DEF. Sensitivity to these variables, particularly elevation and increases in available P, increased with increasing tree size among size classes, as small trees were less sensitive than were larger trees. Our study provides an example of how different environments affect many of the features of tropical forest structure, such as tree size class and species composition.

A new method for voxel‐based modelling of three‐dimensional forest scenes with integration of terrestrial and airborne LiDAR data

Abstract Simulating realistic three‐dimensional (3D) forest scenes is useful in understanding the links between forest structure and ecosystem functions (e.g. radiative transfer). Light detection and ranging (LiDAR) technology provides useful 3D data for forest reconstructions since it can characterise 3D structures of individual trees and canopies. High‐density terrestrial LiDAR (terrestrial laser scanning, TLS) is suitable for fine‐scale reconstructions but is limited to smaller forest plots; low‐density airborne LiDAR (airborne laser scanning, ALS) can cover larger areas but is only suitable for coarse‐scale reconstructions. How to take advantage of TLS and ALS to enable fine‐scale forest simulations in large areas needs to be studied. We propose a new voxel‐based method for forest simulations using the integration of TLS and ALS data. TLS data of representative reference trees are used to approximate the detailed architectures of the whole forest scene, with structural information on each individual tree extracted from ALS data. The high‐density point cloud data derived from TLS and ALS data are voxelised using high resolution solid voxels for scene representation. We tested the proposed method using two virtual forests (108 m × 108 m) and a real forest (300 m × 300 m) with conifer and broadleaf species. The physically based ray tracer (PBRT) was used to visualise the true virtual forest scenes, whereas voxel‐based radiative transfer (VBRT) was used to visualise the modelled forest scenes from LiDAR data. For the real forest scene, simulated and real ALS data were compared. Our results demonstrate that the images simulated by VBRT and PBRT are similar in the virtual forest scenes, with average radiance values of 1.02 and 1.72, respectively. In the real forest scene, the distributions of points and individual tree attributes (tree height, crown radius, and tree volume) derived from real and simulated ALS also match well, with Kullback–Leibler divergence ranging from 0.006 to 0.06. We conclude that the new method is capable of modelling fine‐scale 3D forests in large areas (over 1 ha) when TLS and ALS data are available, and it has good potential in studying the process of radiative transfer in conifer and broadleaf forests.

Variations in the plasticity of functional traits indicate the differential impacts of abiotic and biotic factors on the structure and growth of trees in tropical dry forest fragments

Abiotic and biotic factors have considerable impact on the plasticity of plant functional traits, which influences forest structure and productivity; however, their inter-relationships have not been quantified for fragmented tropical dry forest (TDF) ecosystems. We asked the following questions: (1) what are the variations in the plasticity of functional traits due to soil moisture availability in TDF fragments? (2) what are the roles of soil nutrients and forest disturbances in influencing variations in the plasticity of functional traits in the TDF fragments? and (3) how do the variations in the plasticity of functional traits influence the structure and productivity of TDF fragments? Based on linear mixed-effects results, we observed significant variations among tree species for soil moisture content (SMC) under the canopy and selected functional traits across forest fragments. We categorized tree species across fragments by principal component analysis (PCA) and hierarchical clustering on principal components (HCPC) analyses into three functional types, viz., low wood density high deciduous (LWHD), high wood density medium deciduous (HWMD), and high wood density low deciduous (HWLD). Assemblage of functional traits suggested that the LWHD functional type exhibits a drought-avoiding strategy, whereas HWMD and HWLD adopt a drought-tolerant strategy. Our study showed that the variations in functional trait plasticity and the structural attributes of trees in the three functional types exhibit contrasting affinity with SMC, soil nutrients, and disturbances, although the LWHD functional type was comparatively more influenced by soil resources and disturbances compared to HWMD and HWLD along the declining SMC and edge distance gradients. Plasticity in functional traits for the LWHD functional type exhibited greater variations in traits associated with the conservation of water and resources, whereas for HWMD and HWLD, the traits exhibiting greater plasticity were linked with higher productivity and water transport. The cumulative influence of SMC, disturbances, and functional trait variations was also visible in the relative abundance of functional types in large and small sized fragments. Our analysis further revealed the critical differences in the responses of functional trait plasticity of the coexisting tree species in TDF, which suggests that important deciduous endemic species with drought-avoiding strategies might be prone to strategic exclusion under expected rises in anthropogenic disturbances, habitat fragmentation, and resource limitations.

Propagating Uncertainty in Predicting Individuals and Means Illustrated with Foliar Chemistry and Forest Biomass

Quantifying uncertainty is important to establishing the significance of comparisons, to making predictions with known confidence, and to identifying priorities for investment. However, uncertainty can be difficult to quantify correctly. While sampling error is commonly reported based on replicate measurements, the uncertainty in regression models used to estimate forest biomass from tree dimensions is commonly ignored and has sometimes been reported incorrectly, due either to lack of clarity in recommended procedures or to incentives to underestimate uncertainties. Even more rarely are the uncertainty in predicting individuals and the uncertainty in the mean both recognized for their contributions to overall uncertainty. In this paper, we demonstrate the effect of propagating these two sources of uncertainty using a simple example of calcium concentration of sugar maple foliage, which does not require regression, then the mass of foliage and calcium content of foliage, and finally an entire forest with multiple species and tissue types. The uncertainty due to predicting individuals is greater than the uncertainty in the mean for studies with few trees—up to 30 trees for foliar calcium concentration and 50 trees for foliar mass and calcium content in the data set we analyzed from the Hubbard Brook Experimental Forest. The most correct analysis will take both sources of uncertainty into account, but for practical purposes, country-level reports of uncertainty in carbon stocks can safely ignore the uncertainty in individuals, which becomes negligible with large enough numbers of trees. Ignoring the uncertainty in the mean will result in exaggerated confidence in estimates of forest biomass and carbon and nutrient contents.