Search Results (1,064)

Accurate stem-volume estimation supports inventory, valuation and carbon accounting, but Pressler’s single-section formula has never been tested in the highly productive European-beech forests of the Central Rhodope Mountains, Greece. We quantified the bias of Pressler estimates and developed size-specific correction factors. Sixty Fagus sylvatica L. trees felled in 2023–2024 were measured destructively at 1-m intervals. Pressler standing volumes were compared with Smalian-plus-cone reference volumes (hereafter referred to as true volumes) and analysed with generalized additive models. Pressler underestimated true volume (mean bias = −0.088 m³; RMSE = 0.204 m³; MAPE = 21%). Under-estimation increased with diameter. A GAM with DBH and height explained 96.7% of the variance in true volume. We also fit a Random Forest as a complementary check. Multipliers of 1.30 (<25 cm DBH), 1.20 (25–45 cm), 1.30 (45–55 cm) and ≥1.35 (≥55 cm) cut residual error to ≤20% overall and <10% inside the well-sampled 35–45 cm class. A simple DBH-class correction table restores Pressler’s speed while meeting modern accuracy standards for inventory and carbon reporting. Full article

Tree diameter at breast height (DBH) and tree height are important quantitative attributes in forestry surveys. They serve as essential data for calculating forest stock, growth, and carbon sequestration, and are of significant research value for forest health assessments and other research outcomes. To improve the efficiency of forest resource inventories and to reduce labor costs, a forestry survey device integrating multiple sensors has been developed. Based on the principles of laser ranging and the tunnel magnetoresistance effect, this device integrates both the DBH and tree height measurements. Compared to traditional measurement methods, it boasts a compact size, low cost, and high measurement accuracy. Experimental applications have shown that the average root mean square error (RMSE) of tree height measurements ranges from 31 to 55 cm, while the DBH measurement accuracy reaches 98%, We acknowledge that, although this accuracy meets the requirements for general forestry surveys, it still falls short of the accuracy required for high-precision forest resource surveys (<20 cm), which points to a direction for future improvement. Full article

Teak (Tectona grandis L.f.) is a leading tropical plantation species valued for high-quality timber and carbon (C) storage. This study assessed stand growth across ages and sites, quantified biomass and C by tree component and stand, and developed DBH-based allometric equations for biomass and C estimation. Six stand ages (5, 6, 9, 11, 14, and 17 years) were assessed in three municipalities of Nayarit, Mexico. Dendrometric inventories in permanent plots and destructive sampling of 35 trees provided calibration data for leaves, branches, stem, and roots. C concentration was determined with an elemental analyzer, and nonlinear regression models were adjusted and validated. Stand biomass and C increased with age, peaking at ages 11–14 (>130 Mg ha⁻¹; >60 Mg C ha⁻¹), with lower values at age 17. San Blas and Rosamorada accumulated significantly more than Tuxpan, reflecting site quality. C concentration was stable across sites and ages, with stem and roots consistently ranging between 48% and 50%, and leaves and branches averaging 45%–46%. Allometric equations were most accurate for stem and total biomass/C (R² = 0.73–0.79), while foliage showed higher variability. On average, 60%–70% of biomass was allocated to the stem and 15%–20% to roots. Indicators were stable, with an aboveground-to-belowground ratio (A:B) ≈ 4.9 and a biomass expansion factor (BEF) ≈ 1.5. The current annual increment (CAI) presented two main peaks: ~20 Mg ha⁻¹ yr⁻¹ at ages 5–6 and ~11 Mg ha⁻¹ yr⁻¹ at ages 9–11, followed by a decline after age 14. Teak in western Mexico reaches peak productivity at ages 6–11, with belowground biomass essential for accurate C accounting. Full article

Accurate measurement of tree height and diameter at breast height (DBH) is essential for forest inventory, biomass estimation, and habitat assessment but remains challenging in structurally complex, multi-layered forests. This study evaluates the accuracy and operational feasibility of Unmanned Aerial Vehicle (UAV) LiDAR and Mobile Laser Scanning (MLS) for estimating tree height and DBH in such stands with a diverse structure in the Romanian Carpathians. Field measurements from six plots encompassing mixed-species (Fagus sylvatica L., Abies alba Mill., Picea abies (L.) H.Karst.) and single-species (Picea abies) stands were compared against UAV- and MLS-derived metrics. MLS delivered near-inventory-grade DBH accuracy across all species (R² up to 0.98) and reliable height estimates for intermediate and suppressed trees, while UAV LiDAR consistently underestimated tree height, especially in dense, multi-layered stands (R² < 0.2 in mixed plots). Voxel-based occlusion analysis revealed that over 93% of area under canopy and interior crown volume was captured only by MLS, confirming its dominance below the canopy, whereas UAV LiDAR primarily delineated the outer canopy surface. Species traits influenced DBH accuracy locally, but structural complexity and canopy layering were the main drivers of height underestimation. We recommend hybrid UAV–MLS workflows combining UAV efficiency for canopy-scale mapping with MLS precision for stem and sub-canopy structure. Future research should explore multi-season acquisitions, improved SLAM robustness, and automated data fusion to enable scalable, multi-layer forest monitoring for carbon accounting, biodiversity assessment, and sustainable forest management decision making. Full article

Forest inventories are essential for monitoring and managing forest ecosystems, relying on accurate measurements of tree attributes such as tree detection, Diameter at Breast Height (DBH), and Tree Height (TH). Nowadays, advances in LiDAR technology have enabled increasingly effective and reliable solutions for 3D mapping and tree feature extraction. However, the performance of this method is strongly influenced by point cloud density, which can be limited for technological and/or economic reasons. This study therefore aims to investigate and quantify the effect of density on the accuracy of measured parameters. Starting from high-density datasets, these are progressively downsampled, and the extracted features are compared. Results indicate that DBH estimation requires densities of 600–700 points/m³ for errors below 1 cm (5% RMSE), while accurate tree height estimation (RMSE < 1 m—5% error) can be achieved with densities exceeding 300 points/m³. These findings provide guidance for balancing measurement accuracy and operational efficiency in automated forest surveys using laser scanner technology. Full article

Tree height is a key indicator in forest resource inventories, playing a vital role in evaluating forest resources, carbon stocks, and biomass. However, conventional tree height measurement methods often suffer from limitations such as inadequate accuracy and low efficiency. This paper proposes a portable tree height measurement device based on the integration of ultra-wideband (UWB) technology and an accelerometer, enabling high-precision, low-cost, and rapid tree height measurements. The device adopts a modular design, integrating a UWB ranging sensor, a triaxial accelerometer, a main control unit, and wireless communication modules. It acquires precise distance information via the double-sided two-way ranging (DS-TWR) algorithm and computes tree height by incorporating the pitch angle measured by the accelerometer. Through measurements on 80 trees of various species, compared to results from Total Station, the root mean square error (RMSE) was 0.621 m, with an overall bias of 0.104 m (0.79%) and an overall device accuracy of 95.75%. Additionally, the device features real-time data transmission and cloud storage capabilities, offering an efficient and convenient technical solution for the digital management of forest resources. It holds promising application prospects in areas such as forest resource inventories, ecological monitoring, and forestry production management. Full article

This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses on two 50 × 50 m primary tropical rainforest plots in Hainan Island, China, characterized by dense and vertically stratified vegetation. Key steps include multi-source point cloud registration and noise removal, individual tree segmentation using the Comparative Shortest Path (CSP) algorithm, extraction of diameter at breast height (DBH) and tree height, and 3D reconstruction and volume estimation via cylindrical fitting and convex polyhedron decomposition. Results demonstrate high accuracy in parameter extraction, with DBH estimation achieving R² = 0.89–0.90, RMSE = 2.93–3.95 cm and RMSE% = 13.95–14.75%, while tree height estimation yielded R² = 0.89–0.94, RMSE = 1.26–1.81 m and RMSE% = 9.41–13.2%. Timber volume estimates showed strong agreement with binary volume models (R² = 0.90–0.94, RMSE = 0.10–0.18 m³, RMSE% = 32.33–34.65%), validated by concordance correlation coefficients (CCC) of 0.95–0.97. The fusion of HLS (ground-level trunk details) and UAV-LS (canopy structure) data significantly improved structural completeness, overcoming occlusion challenges in dense forests. This study highlights the efficacy of multi-source LiDAR fusion and 3D modeling for precise forest inventory in complex ecosystems. The ABM framework provides a scalable, non-destructive alternative to traditional methods, supporting carbon stock assessment and sustainable forest management in tropical rainforests. Future work should refine individual tree segmentation and wood-leaf separation to further enhance accuracy in heterogeneous environments. Full article

The application of artificial intelligence (AI) techniques has improved the accuracy of forest species identification, particularly in timber inventories conducted under Sustainable Forest Management (SFM). This study developed and evaluated machine learning models to recognize 16 Amazonian timber species using digital images of tree bark. Data were collected from three SFM units located in Nova Maringá, Feliz Natal, and Cotriguaçu, in the state of Mato Grosso, Brazil. High-resolution images were processed into sub-images (256 × 256 pixels), and two feature extraction methods were tested: Local Binary Patterns (LBP) and pre-trained Convolutional Neural Networks (ResNet50, VGG16, InceptionV3, MobileNetV2). Four classifiers—Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Linear Discriminant Analysis (LDA)—were used. The best result (95% accuracy) was achieved using ResNet50 with SVM, confirming the effectiveness of transfer learning for species recognition based on bark texture. These findings highlight the potential of AI-based tools to enhance accuracy in forest inventories and support decision-making in tropical forest management. Full article

Carbon sinks are of great significance for mitigating the greenhouse effect and climate change. However, only a few carbon sink measurement methods are suitable for small-scale research, such as at the city-region scale. Methods that can accurately distinguish the high–low gradients of forest carbon sinks within small-scale areas have not yet been established. To fill this gap, we used a tree allometric growth model—the i-Tree Eco model—and applied it to Tai’an, which is a National Forest City in China. By using indicator conversion methods, we innovatively combined the China Forest Resources Inventory Geographic Information Database with i-Tree Eco. The results showed that i-Tree Eco successfully estimated the carbon sinks provided by urban–rural forests (in 2019)—the total carbon storage in Tai’an forest was 5,828,165.90 t; the average carbon storage per hectare was 37.19 tC·ha⁻¹; the total carbon sequestration was 936,789.03 tC·yr⁻¹; and the annual carbon sequestration was, on average, 5.97 tC·ha⁻¹·yr⁻¹. Our method improved the spatial resolution of carbon sequestration and storage compared to the commonly used InVEST model, from about 350 m × 350 m to 195 m × 195 m. Compared to the traditional IPCC method, the i-Tree Eco model provided greater accuracy and timeliness in small-scale carbon sequestration measurements, eliminating the need to wait for the next forest inventory to be published. Our method yielded results that covered the entire city region and better reflected the spatial heterogeneity of carbon sinks. We conclude that the innovative application of the i-Tree Eco model to urban–rural-scale carbon sink measurements provides stronger technical support for urban green space planning, as well as data guidance, in relation to local carbon mitigation strategies. Full article

Forest management planning depends on accurately collecting information on available resources, gathered by forest inventories. However, due to the extent of the planted areas in the world, collecting information traditionally has become challenging. Terrestrial light detection and ranging (LiDAR) has emerged as a promising tool to enhance forest inventory. However, selecting the optimal 3D point cloud density for accurately estimating tree attributes remains an open question. The objective of this study was to evaluate the accuracy of different point densities (points per square meter) in point clouds obtained through portable laser scanning combined with simultaneous localization and mapping (PLS-SLAM). The study aimed to identify tree positions and estimate the diameter at breast height (DBH) and total height (H) of 71 trees in a eucalyptus plantation in Brazil. We also tested a semi-automatic method for estimating total height. Point clouds with densities greater than 100 points/m² enabled the detection of over 88.7% of individual trees. The root mean square error (RMSE) of the best DBH measurement was 1.6 cm (RMSE = 5.9%) and the best H measurement (semi-automatic method) was 1.2 m (RMSE = 4.2%) for the point cloud with 36,000 points/m². When measuring the total heights of the largest trees (H > 31.4 m) using LiDAR, the values were always underestimated considering a reference value, and their measurements were significantly different (p-value < 0.05 by the t-test). For point clouds with a density of 36,000 points/m², the automated DBH and total tree height estimations yielded RMSEs of 5.9% and 14.4%, with biases of 4.8% and −1.4%, respectively. When using point clouds of 10 points/m², RMSE values increased to 18.8% for DBH and 28.4% for total tree height, while the bias was 6.2% and 18.4%, respectively. Additionally, total tree height estimations obtained via a semi-automatic method resulted in a lower RMSE of 4.2% and a bias of 1.5%. These findings indicate that point clouds acquired through PLS-SLAM with densities exceeding 100 points/m² are suitable for automated DBH estimation in the studied plantation. Despite the increased processing time required, the semi-automatic method is recommended for total tree height estimation due to its superior accuracy. Full article

PM_2.5 is an air pollutant that has a direct link to increased cardiovascular and respiratory morbidity and mortality, which has been demonstrated in numerous studies. Existing research highlights species-specific variations in the capacity of trees to capture and retain particulate matter (PM). However, a critical gap remains regarding sensitivity analyses of i-Tree Eco model assumptions. Such analyses are crucial for validating the model’s PM deposition estimates against empirically derived efficiencies, a deficiency that the present study addresses. The study consisted of two steps: a tree inventory was carried out at three selected sites, based on which, an ecosystem service analysis was performed using i-Tree Eco, and samples were taken from the leaves of trees at the analysed sites, which were the basis for comparing the data from the i-Tree Eco method and laboratory methods. The study focused on comparing PM_2.5 and PM₁₀ removal estimates derived from both the model and laboratory measurements. The results revealed significant discrepancies between the modelled and laboratory values. A comparison of the average annual PM₁₀ accumulation measured using laboratory methods for individual tree species showed that Tilia sp. achieved 24%, Fraxinus sp. 47.6%, Aesculus sp. 50.77%, and Quercus robur 23.4% of the PM₁₀ uptake efficiency estimated by the i-Tree Eco model. For PM_2.5 uptake, the values obtained through both methods were more consistent. Furthermore, trees growing under more challenging environmental conditions exhibited smaller diameter at breast height (DBH) and lower PM₁₀ and PM_2.5 removal efficiency according to both methods. While I-Tree Eco incorporates tree biophysical characteristics and health status, its methodology currently lacks the resolution to reflect site-specific environmental conditions and local pollutant concentrations at the individual tree level. Therefore, laboratory methods are indispensable for calibrating, validating, and supplementing i-Tree Eco estimates, especially when applied to diverse urban environments. Only the combined application of empirical and model-based methods provides a comprehensive understanding of the potential of urban greenery to improve air quality. Full article

Accurate estimation of tree diameter at breast height (DBH) from LiDAR point clouds is essential for forest inventory, biomass assessment, and ecological monitoring. This paper presents a perimeter-based DBH estimation framework that achieves competitive accuracy against geometric fitting methods across three datasets. The proposed approach partitions the trunk cross-section into angular sectors and employs Gaussian Mixture Models (GMMs) to identify representative boundary points in each sector, weighted by radial proximity and statistical confidence. To handle occlusion and partial scans, missing sectors are reconstructed using symmetry-aware proxy generation. The final perimeter is modeled via either convex hull or B-spline interpolation, from which DBH is derived. Extensive experiments were conducted on two public TreeScope datasets and a custom mobile LiDAR dataset. Compared to the Density-Based Clustering Ring Extraction (DBCRE) baseline, our method reduced RMSE by 22.7% on UCM-0523M (from 2.60 to 2.01 cm), 34.3% on VAT-0723M (from 3.50 to 2.30 cm), and 29.6% on the Custom Dataset (from 2.16 to 1.52 cm). Ablation studies confirmed the individual and synergistic contributions of GMM clustering, radial consistency filtering, and proxy synthesis. Overall, the method provides a flexible alternative that reduces dependence on strict geometric assumptions, offering improved DBH estimation performance with moderate occlusion and incomplete, uneven boundary coverage. Full article

Accurate classification of individual tree types is a key component in forest inventory, biodiversity monitoring, and ecological modeling. This study evaluates and compares multiple Machine Learning (ML) and Deep Learning (DL) approaches for tree type classification based on Airborne Laser Scanning (ALS) data. A mixed-species forest in southeastern Austria, Europe, served as the test site, with spruce, pine, and a grouped class of broadleaf species as target categories. To examine the impact of data representation, ALS point clouds were transformed into four distinct structures: 1D feature vectors, 2D raster profiles, 3D voxel grids, and unstructured 3D point clouds. A comprehensive dataset, combining field measurements and manually annotated aerial data, was used to train and validate 45 ML and DL models. Results show that DL models based on 3D point clouds achieved the highest overall accuracy (up to 88.1%), followed by multi-view 2D raster and voxel-based methods. Traditional ML models performed well on 1D data but struggled with high-dimensional inputs. Spruce trees were classified most reliably, while confusion between pine and broadleaf species remained challenging across methods. The study highlights the importance of selecting suitable data structures and model types for operational tree classification and outlines potential directions for improving accuracy through multimodal and temporal data fusion. Full article

Tree diameter at breast height (DBH) is a fundamental metric in forest inventory and management. This paper presents a novel method for DBH estimation using the built-in light detection and ranging (LiDAR) and red, green and blue (RGB) sensors of an iPhone 13 Pro, aiming to improve measurement accuracy and field usability. A single snapshot of a tree, capturing both depth and RGB images, is used to reconstruct a 3D point cloud. The trunk orientation is estimated based on the point cloud to locate the breast height, enabling robust DBH estimation independent of the capture angle. The DBH is initially estimated by the geometrical relationship between trunk size on the image and the depth of the trunk. Finally, a pre-computed lookup table (LUT) is employed to improve the initial DBH estimates into accurate values. Experimental evaluation on 294 trees within a capture range of 0.25 m to 5 m demonstrates a mean absolute error of 0.53 cm and a root mean square error of 0.63 cm. Full article

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article