Search Results (68)

Swirl spray combustion has attracted significant attention due to its common usage in gas turbines. However, the high pressure in many practical applications remains a major obstacle to the deep understanding of flame stability and pollutant formation. To address this concern, this study investigated a swirl spray flame fueled with n-decane at elevated pressure. Planar laser-induced fluorescence (PLIF) of OH and polycyclic aromatic hydrocarbons (PAHs) were used simultaneously, enabling the distinction of the locations of OH, PAHs, and mixtures of them, providing detailed information on flame structure and evolution of PAHs. The effects of swirl number and ambient pressure on reaction zone characteristics and PAHs’ formation were studied, with the swirl number ranging from 0.30 to 1.18 and the pressure ranging from 1 to 3 bar. The data suggest that the swirl number changes the flame structure from V-shaped to crown-shaped, as observed at both atmospheric and elevated pressures. Additionally, varying swirl numbers lead to the initiation of flame divergence at distinct pressure levels. Moreover, PAHs of different molecular sizes exhibit significant overlap, with larger PAHs able to further extend downstream. The relative concentration of PAH increased with pressure, and the promoting effect of pressure on producing larger PAHs was significant. Full article

Purpose: This pilot study aimed to evaluate how deviations in X-ray tube head angulation and digital sensor alignment affect the radiographic measurement of the profile angle in CAD-CAM abutments. Materials and Methods: A mandibular model was used with five implant positions (central, buccal, and lingual offsets). Custom CAD-CAM abutments were designed with identical bucco-lingual direction contours and varying mesio-distal asymmetry for the corresponding implant positions. Periapical radiographs were acquired under controlled conditions by systematically varying vertical tube angulation, horizontal tube angulation, and horizontal sensor rotation from 0° to 20° in 5° increments for each parameter. Profile angles, interthread distances, and proximal overlaps were measured and compared with baseline STL data. Results: Profile angle measurements were significantly affected by both X-ray tube and sensor deviations. Horizontal tube angulation produced the greatest profile angle distortion, particularly in buccally positioned implants. Vertical x-ray tube angulations beyond 15° led to progressive underestimation of profile angles, while horizontal tube head rotation introduced asymmetric mesial–distal variation. Sensor rotation also caused marked interthread elongation, in some cases exceeding 100%, despite vertical projection being maintained. Profile angle deviations greater than 5° occurred in multiple conditions. Conclusions: X-ray tube angulation and sensor alignment influence the reliability of profile angle measurements. Radiographs with > 10% interthread elongation or crown overlap may be inaccurate and warrant re-acquisition. Special attention is needed when imaging buccally positioned implants. Full article

Individual tree crown detection (ITCD) and tree species classification are critical for forest inventory, species-specific monitoring, and ecological studies. However, accurately detecting tree crowns and identifying species in structurally complex forests with overlapping canopies remains challenging. This study was conducted in a complex mixed conifer–broadleaf forest in northern Japan, aiming to improve ITCD and species classification by employing two machine learning models and different combinations of metrics derived from very high-resolution (2.5 cm) UAV red–green–blue (RGB) and multispectral (MS) imagery. We first enhanced ITCD by integrating different combinations of metrics into multiresolution segmentation (MRS) and DeepForest (DF) models. ITCD accuracy was evaluated across dominant forest types and tree density classes. Next, nine tree species were classified using the ITCD outputs from both MRS and DF approaches, applying Random Forest and DF models, respectively. Incorporating structural, textural, and spectral metrics improved MRS-based ITCD, achieving F-scores of 0.44–0.58. The DF model, which used only structural and spectral metrics, achieved higher F-scores of 0.62–0.79. For species classification, the Random Forest model achieved a Kappa value of 0.81, while the DF model attained a higher Kappa value of 0.91. These findings demonstrate the effectiveness of integrating UAV-derived metrics and advanced modeling approaches for accurate ITCD and species classification in heterogeneous forest environments. The proposed methodology offers a scalable and cost-efficient solution for detailed forest monitoring and species-level assessment. Full article

The accurate delineation of shrub crown boundaries is critical for ecological monitoring, land management, and understanding vegetation dynamics in fragile ecosystems such as semi-arid shrublands. While traditional image processing techniques often struggle with overlapping canopies, deep learning methods, such as convolutional neural networks (CNNs), offer promising solutions for precise segmentation. This study employed high-resolution imagery captured by unmanned aircraft systems (UASs) throughout the shrub growing season and explored the effectiveness of transfer learning for both semantic segmentation (Attention U-Net) and instance segmentation (Mask R-CNN). It utilized pre-trained model weights from two previous studies that originally focused on tree crown delineation to improve shrub crown segmentation in non-forested areas. Results showed that transfer learning alone did not achieve satisfactory performance due to differences in object characteristics and environmental conditions. However, fine-tuning the pre-trained models by unfreezing additional layers improved segmentation accuracy by around 30%. Fine-tuned pre-trained models show limited sensitivity to shrubs in the early growing season (April to June) and improved performance when shrub crowns become more spectrally unique in late summer (July to September). These findings highlight the value of combining pre-trained models with targeted fine-tuning to enhance model adaptability in complex remote sensing environments. The proposed framework demonstrates a scalable solution for ecological monitoring in data-scarce regions, supporting informed land management decisions and advancing the use of deep learning for long-term environmental monitoring. Full article

Accurate individual tree segmentation (ITS) in dense rubber plantations is a challenging task due to overlapping canopies, indistinct tree apexes, and intricate branch structures. To address these challenges, we propose a bottom-up, multi-feature fusion method for segmenting rubber trees using UAV-LiDAR point clouds. Our approach first involves performing a trunk extraction based on branch-point density variations and neighborhood directional features, which allows for the precise separation of trunks from overlapping canopies. Next, we introduce a multi-feature fusion strategy that replaces single-threshold constraints, integrating geometric, directional, and density attributes to classify core canopy points, boundary points, and overlapping regions. Disputed points are then iteratively assigned to adjacent trees based on neighborhood growth angle consistency, enhancing the robustness of the segmentation. Experiments conducted in rubber plantations with varying canopy closure (low, medium, and high) show accuracies of 0.97, 0.98, and 0.95. Additionally, the crown width and canopy projection area derived from the segmented individual tree point clouds are highly consistent with ground truth data, with R² values exceeding 0.98 and 0.97, respectively. The proposed method provides a reliable foundation for 3D tree modeling and biomass estimation in structurally complex plantations, advancing precision forestry and ecosystem assessment by overcoming the critical limitations of existing ITS approaches in high-closure tropical agroforests. Full article

Accurate detection and delineation of individual tree crowns (ITCs) are essential for sustainable forest management and ecosystem monitoring, providing key biophysical attributes at the individual tree level. However, the complex structure of mixed-wood forests, characterized by overlapping canopies of various shapes and sizes, presents significant challenges, often compromising accuracy. This study presents a two-stage deep learning framework that integrates Canopy Height Model (CHM)-based treetop detection with three-dimensional (3D) ITC delineation using high-resolution airborne LiDAR point cloud data. In the first stage, Mask R-CNN detects treetops from the CHM, providing precise initial localizations of individual trees. In the second stage, a 3D U-Net architecture clusters LiDAR points to delineate ITC boundaries in 3D space. Evaluated against manually delineated reference data, our approach outperforms established methods, including Mask R-CNN alone and the lidR itcSegment algorithm, achieving mean intersection-over-union (mIoU) scores of 0.82 for coniferous plots, 0.81 for mixed-wood plots, and 0.79 for deciduous plots. This study demonstrates the great potential of the two-stage deep learning approach as a robust solution for 3D ITC delineation in mixed-wood forests. Full article

Pacific crown-of-thorns starfish (Acanthaster solaris) outbreaks pose a significant threat to coral reef ecosystems, with climate change potentially exacerbating their distribution and impact. However, there remains only a small number of predictive studies on how climate change drives changes in the distribution patterns of A. solaris, and relevant assessments of the impact of these changes on coral reef areas are lacking. To address this issue, this study investigated potential changes in the distribution of A. solaris under climate change and its impact on Acropora coral habitats. Using a novel two-step framework, we integrated both abiotic and biological (Acropora distribution) predictors into species distribution modeling to project future shifts in A. solaris habitats. We created the first reliable set of current and future global distribution maps for A. solaris using a comprehensive dataset and machine learning approach. The results showed significant distribution shifts under three climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), with expanded ranges under all scenarios, and the greatest expansion occurring near 10° S. Asymmetry in the latitudinal shifts in habitat boundaries suggests that the Southern Hemisphere may face a more severe expansion of A. solaris. Regions previously unsuitable for A. solaris, such as parts of New Zealand, might experience new invasions. Additionally, our findings highlight the potential increase in predatory pressure on coral reefs under SSP2-4.5 and SSP5-8.5 scenarios, particularly in the Western Coral Triangle and Northeast Australian Shelf, where an overlap between A. solaris and Acropora habitats is significant. This study provides critical insights into the ecological dynamics of A. solaris in the context of climate change, and the results have important implications for coral reef management. These findings highlight the need for targeted conservation efforts and the development of mitigation strategies to protect coral reefs from the growing threat posed by A. solaris. Full article

Close-to-nature forest management is a sustainable forest management approach aimed at achieving a balance between ecological and economic benefits. The cultivation of future crop trees in the later successional stages following the removal of competitive trees is crucial for promoting positive development trajectories of succession. Understanding the dynamic process of growth investment strategies in future crop trees facilitates the rational planning of management cycles and scopes, ultimately enhancing the quality of tree cultivation. This study was conducted in a Pinus massoniana secondary forest with close-to-nature forest management in Ningbo City, Zhejiang Province, using handheld mobile laser scanning technology to precisely reconstruct the structure of future crop trees. Over a period of 2–5 years following the initial implementation of close-to-nature forest management, 3D point cloud data were collected annually from both managed and reference (non-managed) plots. Using these multi-temporal data, we analyzed the dynamics of the investment strategies, structural growth components, and crown competition of future crop trees. A linear mixed-effect model was applied to compare the temporal variations in these indices between the managed and control plots. Our results revealed that the height-to-diameter ratio of the future crop trees gradually declined over time, while the crown-to-diameter ratio initially increased and then decreased in the managed plots. These trends were significantly different from those observed in the control plots. Additionally, the height growth rates of the future crop trees in the managed plots were consistently lower than those in the control plots, whereas the crown and diameter at breast height (DBH) growth rates were higher. Furthermore, the crown gap area between the future crop trees and their neighboring trees gradually diminished, and the crown overlap progressively increased. These results suggest that the investment in height growth, initially driven by crown competition, shifted toward crown and DBH growth following close-to-nature forest management. In the initial stage after the removal of competitive trees, future crop trees benefited from ample crown radial space and minimal crown competition. However, as the crown radial space became increasingly limited, the future crop trees shifted their growth investment toward DBH to enhance mechanical stability and achieve a balanced tree structure. Understanding these dynamic processes and the underlying mechanisms of growth investment strategies contributes to predicting future forest community development, improving forest productivity, maintaining structural diversity, and ensuring sustainable forest management. Full article

Individual tree parameters are essential for forestry decision-making, supporting economic valuation, harvesting, and silvicultural operations. While extensive research exists on uniform and simply structured forests, studies addressing complex, dense, and mixed forests with highly overlapping, clustered, and multiple tree crowns remain limited. This study bridges this gap by combining structural, textural, and spectral metrics derived from unmanned aerial vehicle (UAV) Red–Green–Blue (RGB) and multispectral (MS) imagery to estimate individual tree parameters using a random forest regression model in a complex mixed conifer–broadleaf forest. Data from 255 individual trees (115 conifers, 67 Japanese oak, and 73 other broadleaf species (OBL)) were analyzed. High-resolution UAV orthomosaic enabled effective tree crown delineation and canopy height models. Combining structural, textural, and spectral metrics improved the accuracy of tree height, diameter at breast height, stem volume, basal area, and carbon stock estimates. Conifers showed high accuracy (R² = 0.70–0.89) for all individual parameters, with a high estimate of tree height (R² = 0.89, RMSE = 0.85 m). The accuracy of oak (R² = 0.11–0.49) and OBL (R² = 0.38–0.57) was improved, with OBL species achieving relatively high accuracy for basal area (R² = 0.57, RMSE = 0.08 m² tree⁻¹) and volume (R² = 0.51, RMSE = 0.27 m³ tree⁻¹). These findings highlight the potential of UAV metrics in accurately estimating individual tree parameters in a complex mixed conifer–broadleaf forest. Full article

The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management. Full article

Utilizing LiDAR sensors mounted on unmanned aerial vehicles (UAVs) to acquire three-dimensional data of fruit orchards and extract precise information about individual trees can greatly facilitate unmanned management. To address the issue of low accuracy in traditional watershed segmentation methods based on canopy height models, this paper proposes an enhanced method to extract individual tree crowns in fruit orchards, enabling the improved detection of overlapping crown features. Firstly, a distribution curve of single-row or single-column treetops is fitted based on the detected treetops using variable window size. Subsequently, a cubic spatial region extending infinitely along the Z-axis is generated with equal width around this curve, and all crown points falling within this region are extracted and then projected onto the central plane. The projecting contour of the crowns on the plane is then fitted using Gaussian functions. Treetops are detected by identifying peak points on the curve fitted by Gaussian functions. Finally, the watershed algorithm is applied to segment fruit tree crowns. The results demonstrate that in citrus orchards with pronounced crown overlap, this novel method significantly reduces the number of undetected trees with a recall of 97.04%, and the F1 score representing the detection accuracy for fruit trees reaches 98.01%. Comparisons between the traditional method and the Gaussian fitting–watershed fusion algorithm across orchards exhibiting varying degrees of crown overlap reveal that the fusion algorithm achieves high segmentation accuracy when dealing with overlapping crowns characterized by significant height variations. Full article

Individual tree segmentation is crucial to extract forest structural parameters, which is vital for forest resource management and ecological monitoring. Airborne LiDAR (ALS), with its ability to rapidly and accurately acquire three-dimensional forest structural information, has become an essential tool for large-scale forest monitoring. However, accurately locating individual trees and mapping canopy boundaries continues to be hindered by the overlapping nature of the tree canopies, especially in dense forests. To address these issues, this study introduces CCD-YOLO, a novel deep learning-based network for individual tree segmentation from the ALS point cloud. The proposed approach introduces key architectural enhancements to the YOLO framework, including (1) the integration of cross residual transformer network extended (CReToNeXt) backbone for feature extraction and multi-scale feature fusion, (2) the application of the convolutional block attention module (CBAM) to emphasize tree crown features while suppressing noise, and (3) a dynamic head for adaptive multi-layer feature fusion, enhancing boundary delineation accuracy. The proposed network was trained using a newly generated individual tree segmentation (ITS) dataset collected from a dense forest. A comprehensive evaluation of the experimental results was conducted across varying forest densities, encompassing a variety of both internal and external consistency assessments. The model outperforms the commonly used watershed algorithm and commercial LiDAR 360 software, achieving the highest indices (precision, F1, and recall) in both tree crown detection and boundary segmentation stages. This study highlights the potential of CCD-YOLO as an efficient and scalable solution for addressing the critical challenges of accuracy segmentation in complex forests. In the future, we will focus on enhancing the model’s performance and application. Full article

Gear modification, which involves the removal of material from the theoretical surface to improve the contact characteristics of the gear face, is widely applied in gear vibration reduction and noise optimization design. This paper establishes a dynamic model of the straight bevel gear (SBG) transmission system to accurately and efficiently evaluate the effects of different modification strategies on the vibrational characteristics of SBGs. Initially, the time-varying meshing stiffness (TVMS) of standard SBGs was calculated, and methods such as the slicing method and deformation coordination equations were used to calculate the TVMS under tooth profile modification (TPM), Lead crown relief (LCR), and comprehensive modification (CM), which were then validated against finite element method (FEM) calculations. Subsequently, taking into account the impact of time-varying meshing point vectors and the degree of contact overlap, a finite element node dynamic model of the SBG transmission system was established. Finally, by comparing the dynamic characteristics under different modification conditions, the study further elucidates that selecting the appropriate modification method and amount according to different service scenarios is an effective means to suppress gear transmission vibration. This research provides a theoretical basis for the design of gear modification and vibration control for SBGs. Full article

Accurately understanding the stand composition of shelter forests is essential for the construction and benefit evaluation of shelter forest projects. This study explores classification methods for dominant tree species in shelter forests using UAV-derived RGB, hyperspectral, and LiDAR data. It also investigates the impact of individual tree crown (ITC) delineation accuracy, crown morphological parameters, and various data sources and classifiers. First, as a result of the overlap and complex structure of tree crowns in shelterbelt forests, existing ITC delineation methods often lead to over-segmentation or segmentation errors. To address this challenge, we propose a watershed and multi-feature-controlled spectral clustering (WMF-SCS) algorithm for ITC delineation based on UAV RGB and LiDAR data, which offers clearer and more reliable classification objects, features, and training data for tree species classification. Second, spectral, texture, structural, and crown morphological parameters were extracted using UAV hyperspectral and LiDAR data combined with ITC delineation results. Twenty-one classification images were constructed using RF, SVM, MLP, and SAMME for tree species classification. The results show that (1) the proposed WMF-SCS algorithm demonstrates significant performance in ITC delineation in complex mixed forest scenarios (Precision = 0.88, Recall = 0.87, F1-Score = 0.87), resulting in a 1.85% increase in overall classification accuracy; (2) the inclusion of crown morphological parameters derived from LiDAR data improves the overall accuracy of the random forest classifier by 5.82%; (3) compared to using LiDAR or hyperspectral data alone, the classification accuracy using multi-source data improves by an average of 7.94% and 7.52%, respectively; (4) the random forest classifier combined with multi-source data achieves the highest classification accuracy and consistency (OA = 90.70%, Kappa = 0.8747). Full article

Individual tree detection and segmentation in broadleaf forests have always been great challenges due to the overlapping crowns, irregular crown shapes, and multiple peaks in large crowns. Unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) is a powerful tool for acquiring high-density point clouds that can be used for both trunk detection and crown segmentation. A hybrid method that combines trunk detection and crown segmentation is proposed to detect individual trees in broadleaf forests based on UAV-LiDAR data. A trunk point distribution indicator-based approach is first applied to detect potential trunk positions. The treetops extracted from a canopy height model (CHM) and the crown segments obtained by applying a marker-controlled watershed segmentation to the CHM are used to identify potentially false trunk positions. Finally, the three-dimensional structures of trunks and branches are analyzed at each potentially false trunk position to distinguish between true and false trunk positions. The method was evaluated on three plots in subtropical urban broadleaf forests with varying proportions of evergreen trees. The F-score in three plots ranged from 0.723 to 0.829, which are higher values than the F-scores derived by a treetop detection method (0.518–0.588) and a point cloud-based individual tree segmentation method (0.479–0.514). The influences of the CHM resolution (0.25 and 0.1 m) and the data acquisition season (leaf-off and leaf-on) on the final individual tree detection result were also evaluated. The results indicated that using the CHM with a 0.25 m resolution resulted in under-segmentation of crowns and higher F-scores. The data acquisition season had a small influence on the individual tree detection result when using the hybrid method. The proposed hybrid method needs to specify parameters based on prior knowledge of the forest. In addition, the hybrid method was evaluated in small-scale urban broadleaf forests. Further research should evaluate the hybrid method in natural forests over large areas, which differ in forest structures compared to urban forests. Full article