Search Results (785)

Amazonian large trees act as central elements of forest ecosystems, storing a disproportionate fraction of aboveground biomass. However, these trees are not randomly distributed across the landscape, and it is expected that edaphic attributes influence floristic composition, forest structure, and vegetation biomass. In this study, we investigated how variation in soil chemical and physical properties affects the diversity and biomass of large trees. Forest inventories were conducted at five sites within protected areas in the states of Pará and Amapá. Aboveground biomass was estimated using allometric equations, while soil samples were analyzed for their physical and chemical properties. Diversity indices, rarefaction, Redundancy Analysis, and Generalized Additive Models were applied. Edaphic variables such as soil pH, organic matter, phosphorus, and aluminum were associated with floristic composition and the biomass of these individuals. Trees with a diameter at breast height greater than or equal to 70 cm accounted for up to 80% of total biomass, revealing a pattern of biomass hyperdominance. The results indicate that the occurrence of large trees is related to edaphic and structural attributes, such as tree density and size distribution, suggesting that these individuals are not randomly distributed along soil gradients. Understanding these patterns is essential for improving ecological models, biomass extrapolations, and management strategies aimed at conserving the Amazon rainforest. Full article

Aerial remote sensing efficiently surveys large areas, but accurate direct object-level measurement remains difficult in complex natural scenes. Advancements in 3D computer vision, particularly radiance field representations such as NeRF and 3D Gaussian splatting, can improve reconstruction fidelity from posed imagery. Nevertheless, direct aerial measurement of important attributes like tree diameter at breast height (DBH) remains challenging. Trunks in aerial forest scans are distant and sparsely observed in image views; at typical operating altitudes, stems may span only a few pixels. With these constraints, conventional reconstruction methods have inaccurate breast-height trunk geometry. TreeDGS is an aerial image reconstruction method that uses 3D Gaussian splatting as a continuous scene representation for trunk measurement. After SfM–MVS initialization and Gaussian optimization, we extract a dense point set from the Gaussian field using RaDe-GS’s depth-aware cumulative-opacity integration and associate each sample with a multi-view opacity reliability score. Then, we isolate trunk points and estimate DBH using opacity-weighted solid-circle fitting. Evaluated on 10 plots with field-measured DBH, TreeDGS reaches 4.79 cm RMSE (about 2.6 pixels at this GSD) and outperforms a LiDAR baseline (7.66 cm RMSE). This shows that TreeDGS can enable accurate, low-cost aerial DBH measurement. Full article

Allometric equations are essential tools for estimating sustainable biomass and carbon dynamics in riparian tree species. This study derived and validated log–log transformation regression equations that relate diameter at breast height (DBH) to the dry weight, stem volume, and total biomass of Salix chaenomeloides Kimura across five river systems in Korea (Byeongcheon, Andong, Boseong, Topyeong, and Yeongdong). DBH was significantly correlated with biomass components and whole-tree biomass, with explanatory power ranging from 0.47 (Byeongcheon-root) to 0.99 (Topyeong-stem) (R²). Model evaluation metrics (RMSE, MAE, MPE) indicated high predictive accuracy across sites. Using the derived allometric equations, net primary productivity (NPP) of individual was 9.40 kg·tree⁻¹·yr⁻¹ and 2.45 ton C·ha⁻¹·yr⁻¹ at the stand level, with site-specific variability reflecting environmental differences. Biomass conversion coefficients, expansion factors, and root-to-aboveground biomass ratios were also obtained, with mean values of 0.29 (branches/stem), 0.10 (leaves/stem), and 0.25 (roots/AGB), a wood density of 0.63 g·cm⁻³, and a biomass expansion factor of 1.37. Independently derived NPP estimates based on stem analysis were comparable (9.02 kg tree⁻¹ yr⁻¹ and 2.43 t C ha⁻¹ yr⁻¹ at individual and stand levels, respectively), supporting the robustness of the approach. These findings provide robust, site-calibrated allometric models for S. chaenomeloides, supporting accurate biomass estimation, carbon accounting, and the evaluation of riparian ecosystems in climate change mitigation and restoration contexts. From a sustainability perspective, these results highlight the development of tools for evaluating the carbon budget of riparian vegetation, which are not yet incorporated into the Korean national IPCC report. They also demonstrate progress in carbon budget assessment by integrating both allometry and stem analysis. Full article

Globally, coffee-based agroforestry systems are recognized for their capacity to integrate agricultural production with biodiversity conservation, particularly in tropical landscapes under intense anthropogenic pressure. However, significant knowledge gaps remain regarding floristic composition, arboreal structure, and the ecological importance of woody species in Andean agroforestry systems of the Peruvian Amazon, especially along altitudinal gradients. The objective of this study was to characterize the diversity, floristic composition, arboreal structure, and ecological value of woody species in coffee-based agroforestry systems in the Department of Amazonas, Peru. Forest inventories were conducted in twelve one-hectare plots, recording dasometric variables, estimating diversity indices, analyzing floristic affinity, and calculating the Importance Value Index of species. A total of 57 tree species belonging to 41 genera and 25 families were recorded, with moderate diversity levels and a marked dominance of species from the Fabaceae family. The structure showed a predominance of young individuals, concentrated in low and intermediate diameter and height classes, and a moderate shade cover suitable for coffee cultivation. The species with the highest ecological and productive value were Pinus tecunumanii, Colubrina glandulosa, Clitoria juninensis, Inga edulis, and Inga mendozana, which perform key functions related to shade provision and soil fertility. These results are transferable to other coffee agroforestry systems in tropical montane regions and provide relevant evidence for sustainable forest management, biodiversity conservation, and productive optimization, issues of international interest in the agricultural and agroforestry sectors. Full article

Form factor is a key parameter for describing tree taper and provides a simple yet effective method for estimating wood volume. However, applying a single form factor across all tree sizes and ages may lead to substantial errors in volume estimation. This study aimed to determine form factors and their ability to estimate the wood volume of Khaya grandifoliola trees across a wide range of ages (1 to 18 years) and diameters at breast height (2 to 92 cm). Using an electronic dendrometer, a total of 733 trees were scaled across Brazil to derive total and stem form factors. Wood volume was computed using Smalian’s formula, and form factors were determined and stratified for 10 diameter and 7 age classes. The form factor values ranged from 0.30 to 1.75. Mean total and stem form factors were 0.42 and 0.83, respectively, when grouped by diameter class, and increased to 0.50 and 0.93 when grouped by age class. Results revealed a consistent decrease in form factor with increasing diameter and age, indicating a gradual change in tree shape as trees mature. These findings highlight that using diameter-class specific form factors enhances the accuracy of volume estimation while maintaining the easiness of traditional methods. Full article

Handheld personal laser scanning (PLS) systems are increasingly being tested in forest inventory as an efficient alternative to labor-intensive, time-consuming field-based methods. However, comparative evaluations across different PLS instrument classes and the influence of operator experience on estimation accuracy remain insufficiently explored. This study presents a controlled comparison of three handheld PLS instruments representing different performance and cost classes, namely professional-grade (high-end) and lower-grade (entry-level and open-source) systems, and evaluates the influence of operator experience on the accuracy of diameter at breast height (DBH) and tree height estimation. Data were collected in even-aged European beech stands using consistent acquisition and processing workflows. Tree attributes were independently estimated by operators with high, medium, and low experience and validated against reference measurements obtained from diameter tape and multi-scan terrestrial laser scanning. Accuracy was assessed using mean difference (bias) and root mean square error, and the effects of instrument type and operator experience were analyzed using one-way and two-factor repeated-measures ANOVA. Results show that instrument type is the dominant factor determining estimation accuracy. The high-end system produced the most accurate DBH and tree height estimates across all operator experience levels, whereas the entry-level and open-source systems yielded acceptable DBH accuracy but consistently underestimated tree height, particularly for taller trees. Operator experience had a secondary effect, improving DBH estimates when lower-grade instruments were used, but had little influence on tree height accuracy. Significant interaction effects indicate that operator influence depends on instrument class. These findings demonstrate that PLS can support operational forest inventory when instrument capabilities align with inventory objectives. High-end systems are currently optimal when reliable tree height estimation is required, whereas lower-grade systems may provide cost-effective solutions for inventories focused primarily on DBH. Full article

This study tests the performance of 30 tree-level models of literature to predict the aboveground biomass (AGB) of trees in 200 1 ha simulated plots representing the following two successional stages of Amazonian forests: Advanced Secondary Forest (ASF) and Mature Forest (MF). This matters because reliable biomass estimates are fundamental to carbon quantification and climate policy. Ensuring consistency between tree-level and plot-level accuracy strengthens transparency and credibility in global reporting. The aim was twofold: (i) to recommend accurate models to predict biomass in the Amazon and (ii) to detect what characteristics of the model calibration dataset can affect the accuracy of the AGB predicted at the plot level. We considered the characteristics of datasets, sample size, minimum, maximum, and range of tree diameters, as well as the coefficient of determination, root mean square error (RMSE), and number of predictors of the 30 models consulted in the literature. These characteristics were correlated with the biomass error per unit area. We listed 11 models based on their acceptable (overall ± 10%) accuracy, whereas four models overestimated and 15 models underestimated the biomass per unit area beyond the acceptable limit. Our analysis pointed out that the strongest (but moderate) correlation (r) was observed for the RMSE of the models, i.e., precision of model predictions. These correlations were r = 0.60 (p = 0.40) for ASF (kg) and r = 0.40 (p = 0.60) for MF (kg) and r = 0.57 (p = 0.18) for ASF (log) and r = 0.21 (p = 0.64) for MF (log), which means that models with greater uncertainty (higher RMSE) tend to produce larger errors in AGB estimation. As a main conclusion, this study cautions that selecting one model among several based on the lowest RMSE is a misleading practice because the precision of predictions at the tree level is not in agreement with the precision at the plot level. Full article

Forest structural parameters, such as canopy closure, stand density, diameter at breast height, tree height, leaf area index, stand age, and biomass, are fundamental for quantifying forest ecosystem functioning and supporting sustainable forest management. Remote sensing has become an indispensable tool for forest structural parameter estimation. Commonly used data sources include optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), unmanned aerial vehicles (UAVs), and multisource data fusion. Correspondingly, modeling approaches have evolved from empirical and statistical methods to machine learning, deep learning, and hybrid physical-data-driven models, enabling improved characterization of nonlinear and complex forest structures. Each data source and modeling strategy offers unique strengths and limitations with respect to accuracy, scalability, interpretability, and transferability. This review provides a concise synthesis of recent advances in remote sensing data sources and model algorithms for forest structural parameter estimation, evaluates the strengths and limitations of different sensors and algorithms, and highlights key challenges related to uncertainty, scalability, transferability, and model interpretability. Finally, future research directions are discussed, emphasizing cross-scale integration, multisource data fusion, and physically informed deep learning frameworks as promising pathways toward more accurate, robust, and ecologically interpretable forest structural parameter modeling at regional to global scales. Full article

Brazil nut (Bertholletia excelsa Bonpl.) is a major non-timber forest product in the Amazon, supporting extractivist communities in Brazil, Bolivia, and Peru and contribute to forest conservation. Unlike other extractive products, Brazil nut production has not declined under commercial use and is recognized for its socioeconomic and environmental importance. Precision agriculture has been transformed by the use of unmanned aerial vehicles (UAVs) and artificial intelligence (AI), which enable monitoring efficiency and yield estimation in several crops, including the Brazil nut. This study assessed the potential of using UAV-based imagery combined with YOLOv8 object detection model to identify and quantify Brazil nut fruits in a native forest fragment in eastern Acre, Brazil. A UAV was used to capture canopy images of 20 trees with varying diameters at breast height. Images were manually annotated and used to train the YOLOv8 with an 80/20 split for training and validation/testing. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP). The model achieved recall above 90%, with an F1-score of 0.88, despite challenges from canopy complexity and partial occlusion. These results indicate that UAV-based imagery combined with AI detection provides an approach for estimating Brazil nut yield, reducing manual effort and improving market strategies for extractivist communities. This technology supports sustainable forest management and socioeconomic development in the Amazon. Full article

This investigation examines diffusion processes for predicting whole-stand volume, incorporating the variability and uncertainty inherent in regional, operational, and environmental factors. The distribution and spatial organization of trees within a specified forest region, alongside dynamic fluctuations and intricate uncertainties, are modeled by a set of nonsymmetric stochastic differential equations of a sigmoidal nature. The study introduces a three-dimensional system of stochastic differential equations (SDEs) with mixed-effect parameters, designed to quantify the dynamics of the three-dimensional distribution of tree-size components—namely diameter (diameter at breast height), potentially occupied area, and height—with respect to the age of a tree. This research significantly contributes by translating the analysis of tree size variables, specifically height, occupied area, and diameter, into stochastic processes. This transformation facilitates the representation of stand volume changes over time. Crucially, the estimation of model parameters is based exclusively on measurements of tree diameter, occupied area, and height, avoiding the need for direct tree volume assessments. The newly developed model has proven capable of accurately predicting, tracking, and elucidating the dynamics of stand volume yield and growth as trees mature. An empirical dataset composed of mixed-species, uneven-aged permanent experimental plots in Lithuania serves to substantiate the theoretical findings. According to the dataset under examination, the model-based estimates of stand volume per hectare in this region exhibited satisfactory goodness-of-fit statistics. Specifically, the root mean square error (and corresponding relative root mean square error) for the living trees of mixed, pine, spruce, and birch tree species were 68.814 m³ (20.4%), 20.778 m³ (7.8%), 32.776 m³ (37.3%), and 4.825 m³ (26.3%), respectively. The model is executed within Maple, a symbolic algebra system. Full article

This study aims to develop and demonstrate a low-cost LiDAR-based 3D mapping approach for estimating tree carbon stock in university campuses. Unlike conventional field-based measurements, which are labor-intensive and error-prone, the proposed system integrates a 2D LiDAR sensor with a servo motor and odometry data to generate three-dimensional point clouds of trees. From these data, key biometric parameters such as diameter at breast height (DBH) and total height are automatically extracted and incorporated into species-specific and generalized allometric equations, in line with IPCC 2006/2019 guidelines, to estimate above-ground biomass, below-ground biomass, and total carbon storage. The experimental study is conducted over approximately 70,000 m² of green space at Gazi University, Ankara, where six dominant species have been identified, including Cedrus libani, Pinus nigra, Platanus orientalis, and Ailanthus altissima. Results revealed a total carbon stock of 16.82 t C, corresponding to 61.66 t CO₂eq. Among species, Cedrus libani (29,468.86 kg C) and Ailanthus altissima (13,544.83 kg C) showed the highest contributions, while Picea orientalis accounted for the lowest. The findings confirm that the proposed system offers a reliable, portable, cost-effective alternative to professional LiDAR scanners. This approach supports sustainable campus management and highlights the broader applicability of low-cost LiDAR technologies for urban carbon accounting and climate change mitigation strategies. Full article

Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article

Accurate biomass and carbon estimation in tropical plantation forests requires species-specific allometric models. Silver Oak (Grevillea robusta A. Cunn. ex R. Br.), cultivar “AVAONE,” is widely planted in northeastern Thailand, yet locally calibrated equations remain limited. This study developed species- and site-specific allometric models using destructive sampling of eight trees (n = 8) aged 2–9 years from a single plantation in Pak Chong District, Nakhon Ratchasima Province, without independent validation. Each tree was separated into stem, branches, leaves, and roots to determine fresh and dry biomass, and carbon concentrations were measured using a LECO CHN628 analyzer in an ISO/IEC 17025-accredited laboratory. Aboveground biomass increased from 17.49 kg at age 2 to 860.42 kg at age 9, with the most rapid gains occurring between ages 6 and 9. Tree height stabilized at approximately 19–20 m after age 7, while diameter continued to increase. Stems accounted for the largest proportion of dry biomass, followed by branches and roots. Carbon concentrations ranged from 45.561% to 48.704%, close to the IPCC default value of 47%. Power-law models based on D²H showed clear relationships with biomass, with R² values ranging from 0.7365 to 0.9372 for individual components and 0.8409 for aboveground biomass. These locally derived equations provide preliminary, site-specific relationships for estimating biomass and carbon stocks in Silver Oak AVAONE plantations and offer a baseline for future studies with expanded sampling and independent validation. Full article

Accurate modeling of stem taper is essential for forest management decisions, including the definition of cutting cycles, the feasibility of annual harvesting, assortment classification, size and volume estimation, and ensuring sustainable production continuity. This study modeled the stem taper of Araucaria angustifolia (Bertol.) Kuntze stands in southern Brazil using Kozak’s variable-exponent model fitted with nonlinear mixed-effects techniques. Both fixed- and mixed-effects models showed high predictive performance, regardless of calibration. An unstructured (UN) covariance structure was required to reduce autocorrelation. The mixed-effects model improved predictive accuracy by up to 22%, achieved R² values above 0.99 with RMSE < 0.74 cm, and significantly reduced residual autocorrelation in diameter estimates. The most effective calibration of random effects was achieved using diameter measurements taken at heights between 0.3 and 6.3 m above ground (approximately between 1.3% and 28.3% of the total height, considering the tallest tree as a reference). This research improves the accuracy of volume estimation and the definition of timber assortments for A. angustifolia, thereby supporting forest management decision-making in southern Brazil. Full article

Shear connections between concrete structural elements play a vital role in defining performance and overall stability. However, limitations in traditional methods for predicting the shear capacity (Vu) of stud connectors in concrete have been highlighted. Developing strategies that precisely describe the performance of stud-headed connectors requires insight into their failure mechanisms and the corresponding shear transmission. Therefore, leveraging advancements in machine learning, this study aims to predict the Vu of the headed stud connector in concrete structures using various input parameters. A database (1121) of the shear strength collected from the literature was trained using six machine learning (ML) algorithms: extreme learning machine (ELM), decision tree (DT), artificial neural network (ANN), multi-linear regression (MLR), support vector machine (SVM), and hybrid ANN–particle swarm optimization (ANN-PSO). Feature selection methods and system identification were applied to explore the optimal or most relevant input parameters. The feature selection techniques indicated that the geometric properties of the stud connector (diameter and cross-sectional area), the concrete modulus of elasticity (Ec), and the height of the weld collar (h_w) are the most relevant input variables. The ANN-PSO model outperformed the other classical models in estimating the shear capacity at two modeling stages. The hybrid ANN-PSO achieved R² = 0.976, MAE = 7.61 kN, RMSE = 10.8 kN, and MAPE = 8.04%, demonstrating the best predictive accuracy among the classical models. On the other hand, DT is the second-best model, with an R² of 0.958, MAE of 10.27 kN, RMSE of 14.43 kN, and MAPE of 8.53 kN for forecasting the shear capacity of stud connectors in concrete. Full article