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Tree allometric models, essential for monitoring and predicting terrestrial carbon stocks, are traditionally built on global databases with forest inventory measurements of stem diameter (D) and tree height (H). However, these databases often combine H measurements obtained through various measurement methods, each with distinct error patterns, affecting the resulting H:D allometries. In recent decades, terrestrial laser scanning (TLS) has emerged as a widely accepted method for accurate, non-destructive tree structural measurements. This study used TLS data to evaluate the prediction accuracy of forest inventory-based H:D allometries and to develop more accurate pantropical allometries. We considered 19 tropical rainforest plots across four continents. Eleven plots had forest inventory and RIEGL VZ-400(i) TLS-based D and H data, allowing accuracy assessment of local forest inventory-based H:D allometries. Additionally, TLS-based data from 1951 trees from all 19 plots were used to create new pantropical H:D allometries for tropical rainforests. Our findings reveal that in most plots, forest inventory-based H:D allometries underestimated H compared with TLS-based allometries. For 30-metre-tall trees, these underestimations varied from -1.6 m (-5.3%) to -7.5 m (-25.4%). In the Malaysian plot with trees reaching up to 77 m in height, the underestimation was as much as -31.7 m (-41.3%). We propose a TLS-based pantropical H:D allometry, incorporating maximum climatological water deficit for site effects, with a mean uncertainty of 19.1% and a mean bias of -4.8%. While the mean uncertainty is roughly 2.3% greater than that of the Chave2014 model, this model demonstrates more consistent uncertainties across tree size and delivers less biased estimates of H (with a reduction of 8.23%). In summary, recognizing the errors in H measurements from forest inventory methods is vital, as they can propagate into the allometries they inform. This study underscores the potential of TLS for accurate H and D measurements in tropical rainforests, essential for refining tree allometries.

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Based on data collected from 2054 saplings of Larix gmelinii forest in 55 fixed plots in 2018-2019 in Cuigang Forestry Station, Daxing'anling area, we classified the stand density index (SDI) into four classes, i.e., Class Ⅰ (SDI1<1863 plants·hm-2), Class Ⅱ (1863 plants·hm-2≤SDI2<2155 plants·hm-2), Class Ⅲ (2155 plants·hm-2≤SDI3<2459 plants·hm-2) and Class Ⅳ (SDI4≥2459 plants·hm-2) by using the quartile method. We constructed a dummy variable model and quantile regression model for the height-breast diameter of saplings of L. gmelinii with dummy variable method introduced SDI. The results showed that among the five selected representative non-linear tree height curve models, the Richards model fitted the best, with Ra2, RMSE and MAE of 0.7637, 0.8250 m and 0.5696 m. The dummy variable model including the SDI constructed based on the Richards model showed a 1.3% increase in Ra2 compared with the base model, while RMSE, MAE, and AIC decreased by 2.1%, 1.5%, and 11.2%, respectively. When the quantile τ was 0.5, Ra2 of quantile regression model was the maximum, and RMSE, MAE, AIC was the minimum, being 0.7612, 0.8294 m, 0.5657 m, and -767.19, respectively. Compared with SDI1, sapling height in SDI2-SDI4 was increased by 5.6%, 5.6%, and 11.3%, suggesting reasonable that regulation of stand density was conducive to increase the height growth of saplings in regeneration.

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Atmospheric conditions are expected to become warmer and drier in the future, but little is known about how evaporative demand influences forest structure and function independently from soil moisture availability, and how fast-response variables (such as canopy water potential and stomatal conductance) may mediate longer-term changes in forest structure and function in response to climate change. We used two tropical rainforest sites with different temperatures and vapour pressure deficits (VPD), but nonlimiting soil water supply, to assess the impact of evaporative demand on ecophysiological function and forest structure. Common species between sites allowed us to test the extent to which species composition, relative abundance and intraspecific variability contributed to site-level differences. The highest VPD site had lower midday canopy water potentials, canopy conductance (gc ), annual transpiration, forest stature, and biomass, while the transpiration rate was less sensitive to changes in VPD; it also had different height-diameter allometry (accounting for 51% of the difference in biomass between sites) and higher plot-level wood density. Our findings suggest that increases in VPD, even in the absence of soil water limitation, influence fast-response variables, such as canopy water potentials and gc , potentially leading to longer-term changes in forest stature resulting in reductions in biomass.

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Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2-17 m, meeting the accuracy requirements of forest survey.

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Frequent observations of higher mortality in larger trees than in smaller ones during droughts have sparked an increasing interest in size-dependent drought-induced mortality. However, the underlying physiological mechanisms are not well understood, with height-associated hydraulic constraints often being implied as the potential mechanism driving increased drought vulnerability. We performed a quantitative synthesis on how key traits that drive plant water and carbon economy change with tree height within species and assessed the implications that the different constraints and compensations may have on the interacting mechanisms (hydraulic failure, carbon starvation and/or biotic-agent attacks) affecting tree vulnerability to drought. While xylem tension increases with tree height, taller trees present a range of structural and functional adjustments, including more efficient water use and transport and greater water uptake and storage capacity, that mitigate the path-length-associated drop in water potential. These adaptations allow taller trees to withstand episodic water stress. Conclusive evidence for height-dependent increased vulnerability to hydraulic failure and carbon starvation, and their coupling to defence mechanisms and pest and pathogen dynamics, is still lacking. Further research is needed, particularly at the intraspecific level, to ascertain the specific conditions and thresholds above which height hinders tree survival under drought.

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The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r 2 = .35 [.12, .51] (mean [min, max]) than for tree density r 2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous-broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.

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Increasing tropical cyclone (TC) pressure on temperate forests is inevitable under the recent global increase of the intensity and poleward migration of TCs. However, the long-term effects of TCs on large-scale structure and diversity of temperate forests remain unclear. Here, we aim to ascertain the legacy of TCs on forest structure and tree species richness by using structural equation models that consider several environmental gradients and use an extensive dataset containing >140,000 plots with >3 million trees from natural temperate forests across eastern United States impacted by TCs. We found that high TC activity (a combination of TC frequency and intensity) leads to a decrease in maximum tree sizes (height and diameter), an increase in tree density and basal area, and a decline in the number of tree species and recruits. We identified TC activity as the strongest predictor of forest structure and species richness in xeric (dry) forests, while it had a weaker impact on hydric (wet) forests. We highlight the sensitivity of forest structure and tree species richness to impacts of likely further increase of TC activity in interaction with climate extremes, especially drought. Our results show that increased TC activity leads to the homogenization of forest structure and reduced tree species richness in U.S. temperate forests. These findings suggest that further declines in tree species richness may be expected because of the projected increase of future levels of TC activity.

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Biomass allometric relations are necessary for precise estimations of biomass forest stocks, as well as for the quantification of carbon sequestered by forest cover. Therefore, we attempted to create allometric models of total biomass in young silver birch (Betula pendula Roth) trees and their main components, i.e., leaves, branches, stem under bark, bark, and roots. The models were based on data from 180 sample trees with ages up to 15 years originating from natural regeneration at eight sites in the Western Carpathians (Slovakia). Sample trees represented individuals with stem base diameters (diameter D0) from about 4.0 to 113.0 mm and tree heights between 0.4 to 10.7 m. Each tree component was dried to constant mass and weighed. Moreover, subsamples of leaves (15 pieces of each tree) were scanned, dried, and weighed. Thus, we also obtained data for deriving a model expressing total leaf area at the tree level. The allometric models were in the form of regression relations using diameter D0 or tree height as predictors. The models, for instance, showed that while the total tree biomass of birches with a D0 of 50 mm (and a tree height of 4.06 m) was about 1653 g, the total tree biomass of those with a D0 of 100 mm (tree height 6.79 m) reached as much as 8501 g. Modeled total leaf areas for the trees with the above-mentioned dimensions were 2.37 m2 and 8.54 m2, respectively. The results prove that diameter D0 was a better predictor than tree height for both models of tree component biomass and total leaf area. Furthermore, we found that the contribution of individual tree components to total biomass changed with tree size. Specifically, while shares of leaves and roots decreased, those of all other components, especially stems with bark, increased. The derived allometric relations may be implemented for the calculation of biomass stock in birch-dominant or birch-admixed stands in the Western Carpathians or in other European regions, especially where no species- and region-specific models are available.

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Factors shaping the interspecific variations in herbivory have puzzled ecologists for decades and several hypotheses have been proposed to explain interspecific variation in leaf herbivory. In a tropical rainforest in Yunnan Province, China, we collected 6732 leaves from 129 species with canopy heights ranging from 1.6 to 65.0 m above the ground. We tested the role of canopy height, the diversity, composition and structural heterogeneity of neighbors and leaf traits in shaping the interspecific variations in herbivory. Results show that leaf herbivory decreased with canopy height and specific leaf area (SLA) and increased with leaf size. However, neighboring species' diversity, composition, and structural heterogeneity showed no association with herbivory. Therefore, neither the visual apparency effect nor the associational resistance effect was detected in this hyperdiverse tropical rainforest. These findings highlight the importance of vertical structure in shaping herbivory patterns in natural communities.

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Crucial for the climate adaptation of trees is a xylem anatomical structure capable of adjusting to changing water regimes. Although species comparisons across climate zones have demonstrated anatomical change in response to altered water availability and tree height, less is known about the adaptability of tree vascular systems to increasing water deficits at the intraspecific level. Information on the between-population and within-population variability of xylem traits helps assessing a species' ability to cope with climate change. We investigated the variability of wood anatomical and related hydraulic traits in terminal branches of European beech (Fagus sylvatica L.) trees across a precipitation gradient (520-890 mm year-1) and examined the influence of climatic water balance (CWB), soil water capacity (AWC), neighborhood competition (CI), tree height and branch age on these traits. Furthermore, the relationship between xylem anatomical traits and embolism resistance (P50) was tested. Within-population trait variation was larger than between-population variation. Vessel diameter, lumen-to-sapwood area ratio and potential conductivity of terminal branches decreased with decreasing CWB, but these traits were not affected by AWC, whereas vessel density increased with an AWC decrease. In contrast, none of the studied anatomical traits were influenced by variation in tree height (21-34 m) or CI. Branch age was highly variable (2-22 years) despite equal diameter and position in the flow path, suggesting different growth trajectories in the past. Vessel diameter decreased, and vessel density increased, with increasing branch age, reflecting negative annual radial growth trends. Although vessel diameter was not related to P50, vessel grouping index and lumen-to-sapwood area ratio showed a weak, though highly significant, positive relationship to P50. We conclude that the xylem anatomy of terminal tree-top branches in European beech is modified in response to increasing climatic aridity and/or decreasing soil water availability, independent of a tree height effect.

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Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

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Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University's Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management.

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Tree height is an essential indicator in forestry research. This indicator is difficult to measure directly, as well as wind disturbance adds to the measurement difficulty. Therefore, tree height measurement has always been an issue that experts and scholars strive to improve. We propose a tree height measurement method based on tree fisheye images to improve the accuracy of tree height measurements. Our aim is to extract tree height extreme points in fisheye images by proposing an improved lightweight target detection network YOLOX-tiny. We added CBAM attention mechanism, transfer learning, and data enhancement methods to improve the recall rate, F1 score, AP, and other indicators of YOLOX-tiny. This study improves the detection performance of YOLOX-tiny. The use of deep learning can improve measurement efficiency while ensuring measurement accuracy and stability. The results showed that the highest relative error of tree measurements was 4.06% and the average relative error was 1.62%. The analysis showed that the method performed better at all stages than in previous studies.

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Fine-scale topographic-edaphic gradients are common in tropical forests and drive species spatial turnover and marked changes in forest structure and function. We evaluate how hydraulic traits of tropical tree species relate to vertical and horizontal spatial niche specialization along such a gradient. Along a topographic-edaphic gradient with uniform climate in Borneo, we measured six key hydraulic traits in 156 individuals of differing heights in 13 species of Dipterocarpaceae. We investigated how hydraulic traits relate to habitat, tree height and their interaction on this gradient. Embolism resistance increased in trees on sandy soils but did not vary with tree height. By contrast, water transport capacity increased on sandier soils and with increasing tree height. Habitat and height only interact for hydraulic efficiency, with slope for height changing from positive to negative from the clay-rich to the sandier soil. Habitat type influenced trait-trait relationships for all traits except wood density. Our data reveal that variation in the hydraulic traits of dipterocarps is driven by a combination of topographic-edaphic conditions, tree height and taxonomic identity. Our work indicates that hydraulic traits play a significant role in shaping forest structure across topographic-edaphic and vertical gradients and may contribute to niche specialization among dipterocarp species.

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Landscape effects might impede or increase spore dispersal and disease risk for crops, as trees and hedges buffer winds and can behave as spore traps, therefore limiting diffusion of fungi, or, on the contrary, behave as disease relay once vegetation is infected and become inoculum sources. In this study, we investigated weekly prevalence of the pathogenic fungus Colletotrichum gloeosporioides on guava tree leaves, differentiating impacts of leaf height on tree, age, and location within leaf. We first estimated differences in prevalence for each covariate, and then related infection rates to weather effects during the year. Our results highlighted a great variance of prevalence among individual trees, and a lower contamination of tree tops, as well as a tendency for greater odds of infection in tips of young leaves compared to older ones. Last, we show evidence that individual tree contaminations are associated with different disease dynamics: early and dispersal-based, late and growth-based, as well as with intermediate dynamic ranges. Pathogen infection dynamics will thus be greatly impacted by cover characteristics at local scale, and tree cover should not be perceived as homogeneously driving disease levels.

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Treeline ecosystems are of great scientific interest to study the effects of limiting environmental conditions on tree growth. However, tree growth is multidimensional, with complex interactions between height and radial growth. In this study, we aimed to disentangle effects of height and climate on xylem anatomy of white spruce [Picea glauca (Moench) Voss] at three treeline sites in Alaska; i.e., one warm and drought-limited, and two cold, temperature-limited. To analyze general growth differences between trees from different sites, we used data on annual ring width, diameter at breast height (DBH), and tree height. A representative subset of the samples was used to investigate xylem anatomical traits. We then used linear mixed-effects models to estimate the effects of height and climatic variables on our study traits. Our study showed that xylem anatomical traits in white spruce can be directly and indirectly controlled by environmental conditions: hydraulic-related traits seem to be mainly influenced by tree height, especially in the earlywood. Thus, they are indirectly driven by environmental conditions, through the environment's effects on tree height. Traits related to mechanical support show a direct response to environmental conditions, mainly temperature, especially in the latewood. These results highlight the importance of assessing tree growth in a multidimensional way by considering both direct and indirect effects of environmental forcing to better understand the complexity of tree growth responses to the environment.

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The height of standing trees is an important index in forestry research. This index is not only hard to measure directly but also the environmental factors increase the measurement difficulty. Therefore, the measurement of the height of standing trees is always a problem that experts and scholars are trying to improve. In this study, improve fuzzy c-means algorithm to reduce the calculation time and improve the clustering effect, used on this image segmentation technology, a highly robust non-contact measuring method for the height of standing trees was proposed which is based on a smartphone with a fisheye lens. While ensuring the measurement accuracy, the measurement stability is improved. This method is simple to operate, just need to take a picture of the standing tree and determine the shooting distance to complete the measurement. The purpose of the fisheye lens is to ensure that the tree remains intact in the photograph and to reduce the shooting distance. The results of different stability experiments show that the measurement error ranged from -0.196m to 0.195m, and the highest relative error of tree measurement was 3.05%, and the average relative error was 1.45%. Analysis shows that compared with previous research, this method performs better at all stages. The proposed approach can provide a new way to obtain tree height, which can be used to analyze growing status and change in contrast height because of high accuracy and permanent preservation of images.

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Forest resource survey is important for the sustainable development of forest ecosystem in China. The average tree height is a main structural parameter of forest resource survey, and also one of the key parameters with greatest difficulty to obtain. The purpose of this study was to explore the potential of joint active and passive remote sensing technology in estimating forest average height. Taking Xixiaoshan Forest Farm in Linjiang City of Jilin Province as the research area, we used Sentinel-1 SAR and Sentinel-2A data, extracted two backscatter coefficients and eight texture information of Sentinel-1, ten spectral bands and texture information of Sentinel-2A and eleven vegetation index variables, constructed five groups of average tree height estimation models based on above variables and fusion of four variables by multiple linear regression method. We further evaluated the influence of each variable on the inversion accuracy. The results showed that the texture information extracted from the Sentinel-2A spectral band of a single data source variable had a better modeling effect and could be used as effective data to estimate the average tree height. The height estimation model of the integrated four variables was optimal, with a R2 vaule of 0.56, a root mean square error of leave-one-out cross-validation of 2.92 m, and a relative root mean square error of leave-one-out cross-validation of 21.5%. The forest average height model based on Sentinel-1 and Sentinel-2a characteristic variables could improve the estimation accuracy of forest height, which could be used for regional forest average height estimation and mapping.

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Fertilization is commonly used to increase growth in forest plantations, but it may also affect tree water relations and responses to drought. Here, we measured changes in biomass, transpiration, sapwood-to-leaf area ratio (As :Al ) and sap flow driving force (ΔΨ) during the 6-year rotation of tropical plantations of Eucalyptus grandis under controlled conditions for throughfall and potassium (K) fertilization. K fertilization increased final tree height by 8 m. Throughfall exclusion scarcely affected tree functioning because of deep soil water uptake. Tree growth increased in K-supplied plots and remained stable in K-depleted plots as tree height increased, while growth per unit leaf area increased in all plots. Stand transpiration and hydraulic conductance standardized per leaf area increased with height in K-depleted plots, but remained stable or decreased in K-supplied plots. Greater Al in K-supplied plots increased the hydraulic constraints on water use. This involved a direct mechanism through halved As :Al in K-supplied plots relative to K-depleted plots, and an indirect mechanism through deteriorated water status in K-supplied plots, which prevented the increase in ΔΨ with tree height. K fertilization in tropical plantations reduces the hydraulic compensation to growth, which could increase the risk of drought-induced dieback under climate change.

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