Search Results (219)

Urban expansion intensifies population exposures to fine particulate matter (PM_2.5). Trees mitigate pollution by dry deposition, in which particles settle on plants. However, city-scale models frequently overlook differences in tree species and structure. This study assesses PM_2.5 removal by individual city-owned street trees in Mississauga, Canada, throughout the 2019 leaf-growing season (May to September). Using a modified i-Tree Eco framework, we evaluated the removal of PM_2.5 by 200,560 city-owned street trees (245 species) in Mississauga from May to September 2019. The model used species-specific deposition velocities (V_d) from the literature or leaf morphology estimates, adjusted for local winds, a 3 m-resolution satellite-derived Leaf Area Index (LAI), field-validated, crown area modelled from diameter at breast height, and 1 km² resolution PM_2.5 data geolocated to individual trees. About twenty-eight tons of PM_2.5 were removed from 200,560 city-owned trees (245 species). Coniferous species (14.37% of trees) removed 25.62 tons (92% of total), much higher than deciduous species (85.63%, 2.18 tons). Picea pungens (18.33 tons, 66%), Pinus nigra (3.29 tons, 12%), and Picea abies (1.50 tons, 5%) are three key species. Conifers’ removal efficiency originates from the faster deposition velocities, larger tree size, and dense foliage, all of which enhance particle deposition. This study emphasizes species-specific approaches for improving urban air quality through targeted tree planting. Prioritizing coniferous species such as spruce and pine can improve pollution mitigation, providing actionable strategies for Mississauga and other cities worldwide to develop green infrastructure planning for air pollution. Full article

Chinese fir, as a crucial fast-growing tree species in the hilly regions of southern China, exhibits spatial structure characteristics that directly influence both the ecological functionality and productivity of its stands. This study focused on Chinese fir plantations in the Yangkou State-Owned Forest Farm, Fujian Province. Using UAV-LiDAR point cloud data, individual tree parameters such as height and crown width were extracted, and a DBH inversion model was constructed by integrating machine learning algorithms. Spatial structure parameters were quantified through weighted Voronoi diagrams. A comprehensive evaluation system was established based on the combined weighting method and fuzzy evaluation model to systematically analyze spatial structure characteristics and their evolutionary patterns across different age classes. The results demonstrated that growth environment indicators (openness and openness ratio) progressively declined with the stand’s age, reflecting deteriorating light conditions due to increasing canopy closure. Growth superiority (size ratio and angle competition index) exhibited a “V”-shaped trend, with the most intense competition occurring in the middle-aged stands before stabilizing in the over-mature stage. The resource utilization efficiency (uniform angle and forest layer index) showed continuous optimization, reaching optimal spatial configuration in over-mature stands. This study developed a spatial structure evaluation system for Chinese fir plantations by combining UAV data and cloud modeling, elucidating structural characteristics and developmental patterns across different growth stages, thereby providing theoretical foundations and technical support for close-to-nature management and the precision quality improvement of Chinese fir plantations. Full article

English walnut (Juglans regia) is a species that is highly valued for the quality of its wood and the nutritional and nutraceutical properties of its fruit. A severe dieback of J. regia trees was observed recently in orchards located in three geographically distinct areas of Tuscany, central Italy. Symptoms included root and collar rot, necrosis of the under-bark tissue, bleeding cankers, stunted growth, and crown dieback. Four Phytophthora species were obtained from 239 isolates found on symptomatic J. regia individuals. They were identified, on the basis of macro-morphological (colony shape and texture), micro-morphometric (shape and size of oogonia, antheridia, oospores, sporangia, and chlamydospores) and molecular (ITS sequencing) characters, as P. gonapodyides, P. cactorum, P. citricola, and P. plurivora. Among these species, P. plurivora was the species isolated with overwhelming frequency from symptomatic tissue and rhizosphere soil, suggesting it to be the putative etiological agent. Pathogenicity assays were conducted on 20 cm long detached J. regia branches for a definitive establishment of disease causation. Severe symptoms (extended necroses) were exhibited by branches infected with P. plurivora, proving its pathogenicity and high virulence on this host. The other Phytophtora species produced negligible necroses around the inoculation site. P. plurivora was recovered from all the investigated orchards, providing evidence that it is quite widespread. This study highlights the growing threat posed by the polyphagous P. plurivora to walnut cultivation and the sustainable business it fuels in Europe, underscoring the need for integrated management strategies to mitigate its economic and ecological impacts. Full article

In many hardwood forests, resprouting is a common response to disturbance and basal resprouts may represent a substantial component of the forest understory, especially post-wildfire. Despite this, resprouts are often overlooked in biomass assessments and drivers of resprouting responses in certain species are still unknown. These knowledge gaps are problematic as the contribution of resprouts to understory fuel loads are needed for wildfire risk modeling and effective forest stewardship. Here, we validated the handheld mobile laser scanning (HMLS) of basal resprout volume and field measurements of stem count and clump height as methods to estimate the mass of California Bay Laurel (Umbellularia californica) basal resprouts at Pepperwood and Saddle Mountain Preserves, Sonoma County, California. In addition, we examined the role of tree size and wildfire severity in predicting post-wildfire resprouting response. Both field measurements (clump height and stem count) and remote sensing (HMLS-derived volume) effectively estimated dry mass (total, leaf and wood) of U. californica resprouts, but underestimated dry mass for a large resprout. Tree size was a significant factor determining post-wildfire resprouting response at Pepperwood Preserve, while wildfire severity significantly predicted post-wildfire resprout size at Saddle Mountain. These site differences in post-wildfire basal resprouting predictors may be related to the interactions between fire severity, tree size, tree crown topkill, and carbohydrate mobilization and point to the need for additional demographic and physiological research. Monitoring post-wildfire changes in U. californica will deepen our understanding of resprouting dynamics and help provide insights for effective forest stewardship and wildfire risk assessment in fire-prone northern California forests. Full article

Coniferous forests in Canada play a vital role in carbon sequestration, wildlife conservation, climate change mitigation, and long-term sustainability. Traditional methods for classifying and segmenting coniferous trees have primarily relied on the direct use of spectral or LiDAR-based data. In 2024, we introduced a novel data representation method, pseudo tree crown (PTC), which provides a pseudo-3D pixel-value view that enhances the informational richness of images and significantly improves classification performance. While our original implementation was successfully tested on urban and deciduous trees, this study extends the application of PTC to Canadian conifer species, including jack pine, Douglas fir, spruce, and aspen. We address key challenges such as snow-covered backgrounds and evaluate the impact of training dataset size on classification results. Classification was performed using Random Forest, PyTorch (ResNet50), and YOLO versions v10, v11, and v12. The results demonstrate that PTC can substantially improve individual tree classification accuracy by up to 13%, reaching the high 90% range. Full article

Unmanned aerial vehicle (UAV) remote sensing has become an important tool for high-resolution tree species identification in orchards and forests. However, irregular spatial distribution, overlapping canopies, and small crown sizes still limit detection accuracy. To overcome these challenges, we propose YOLOv11-OAM, an enhanced one-stage object detection model based on YOLOv11. The model incorporates three key modules: omni-dimensional dynamic convolution (ODConv), adaptive spatial feature fusion (ASFF), and a multi-point distance IoU (MPDIoU) loss. A class-balanced augmentation strategy is also applied to mitigate category imbalance. We evaluated YOLOv11-OAM on UAV imagery of six fruit tree species—walnut, prune, apricot, pomegranate, saxaul, and cherry. The model achieved a mean Average Precision (mAP@0.5) of 93.1%, an 11.4% improvement over the YOLOv11 baseline. These results demonstrate that YOLOv11-OAM can accurately detect small and overlapping tree crowns in complex orchard environments, offering a reliable solution for precision agriculture and smart forestry applications. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

Accurate detection of individual tree crowns is a critical prerequisite for precisely extracting forest structural parameters, which is vital for forestry resources monitoring. While unmanned aerial vehicle (UAV)-acquired RGB imagery, combined with deep learning-based networks, has demonstrated considerable potential, existing methods often rely exclusively on RGB data, rendering them susceptible to shadows caused by varying illumination and suboptimal performance in dense forest stands. In this paper, we propose integrating LiDAR-derived Canopy Height Model (CHM) with RGB imagery as complementary cues, shifting the paradigm of tree crown detection from unimodal to multimodal. To fully leverage the complementary properties of RGB and CHM, we present a novel Multimodal learning-based Tree Crown Detection Network (MTCDNet). Specifically, a transformer-based multimodal feature fusion strategy is proposed to adaptively learn correlations among multilevel features from diverse modalities, which enhances the model’s ability to represent tree crown structures by leveraging complementary information. In addition, a learnable positional encoding scheme is introduced to facilitate the fused features in capturing the complex, densely distributed tree crown structures by explicitly incorporating spatial information. A hybrid loss function is further designed to enhance the model’s capability in handling occluded crowns and crowns of varying sizes. Experiments conducted on two challenging datasets with diverse stand structures demonstrate that MTCDNet significantly outperforms existing state-of-the-art single-modality methods, achieving AP50 scores of 93.12% and 94.58%, respectively. Ablation studies further confirm the superior performance of the proposed fusion network compared to simple fusion strategies. This research indicates that effectively integrating RGB and CHM data offers a robust solution for enhancing individual tree crown detection. Full article

Trogopterus xanthipes (Sciuridae, Rodentia) is a medium-sized flying squirrel species in the monotypic genus Trogopterus, and is endemic to China. It is distinguishable from other squirrels by the long black hairs on the inner and outer sides at the base of the ears and numerous ridges on the crowns of the upper and lower cheek teeth. Mitogenomes have been widely used in phylogenetic studies. We described T. xanthipes morphological features and successfully sequenced its mitogenome for the first time. The T. xanthipes mitogenome was conserved in number and order of genes. We analyzed codon usage patterns, evolutionary mutation rates, K2P distance, and genetic diversity of protein-coding genes. We reconstructed the phylogeny of Sciuridae (94 species and 21 genera in 4 subfamilies). All phylogenetic trees shared the same topologies and consistently supported the monophyly of Sciuridae, and the supported subfamilies relationship as follows: ((Xerinae + Callosciurinae) + Sciurinae) + Ratufinae. The relationship within the Sciurinae clade was ((Glaucomys + Hylopetes) + ((Trogopterus+Pteromys) + Petaurista) + Sciurus). The relationship within the Callosciurinae clade was Exilisciurus + ((Tamiops + Dremomys) + ((Lariscus+Sundasciurus) + Callosciurus)). The relationship within the Xerinae clade was Sciurotamias + (Tamias + (Callospermophilus + (Marmota + (Spermophilus + (Urocitellus + (Ictidomys + Cynomys)))))). The phylogenetic position among different subfamilies of Sciuridae was consistently recovered with high support across different datasets (PCGRNA and PCG12RNA) and supported the monophyletic lineage of each genus of Sciuridae. Trogopterus xanthipes was sister species to Pteromys volans. Species within the genus formed different minor clades, suggesting relatively high interspecific divergences. The tribe Pteromyini was sister taxon of the tribe Sciurini, which was not supported by the traditional division of Sciuridae into subfamilies Pteromyinae and Sciurinae. Hence, our data supported a division of the Sciuridae into five subfamilies. 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

Climate change worsens abiotic stresses, primarily due to high temperatures, which have a negative impact on avocado productivity, leading to reduced crop yields, affecting fruit set and abscission. To tackle these challenges, antioxidants such as glycine, choline, and proline can enhance plant tolerance to these stressors and minimize plant cell damage. This work aimed to use these antioxidants to improve avocado commercial yield and quality under challenging environmental conditions. This study was conducted at the experimental farm of the Polytechnic University of Valencia, Spain, to evaluate the effects of glycine, choline, and proline on ‘Hass’ Persea americana plants. The research took place during the 2022–2023 and 2023–2024 seasons in a 2.0 ha orchard, using a randomized design with two treatments: one with antioxidants and the other without. Substances were applied at specific phenological phases, as the BBCH code indicated. Tree growth parameters, including trunk diameter, height, crown diameter, and tree canopy volume, were measured using geometric formulas. Leaf samples were collected to analyze the nutrient concentrations of N, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn using atomic emission spectrometry. Marketable fruit yield and quality parameters such as fat, fiber, and protein content were evaluated using the Association of Official Agricultural Chemists (AOAC) methods. The results showed that antioxidants did not significantly affect tree growth but altered leaf mineral nutrient composition. N and P concentrations were reduced, while K and Ca concentrations were increased. Mn and Zn levels were higher in the treated plants, whereas Cu levels were higher in the control plants. Productivity significantly improved, with a 49% increase in fruit yield, larger fruit size, and a 7% increase in fat content, though fiber and protein remained unchanged. These results show the selective benefits of antioxidants in optimizing avocado yield and quality under stress. Full article

The accurate detection of individual tree crowns and estimation of tree density is essential for effective forest management, biodiversity assessment, and ecological monitoring. The precision of tree crown detection algorithms plays a critical role in providing reliable data for these applications, where even slight inaccuracies can lead to significant deviations in tree population estimates and ecological indicators. Various algorithmic parameters, such as pixel size and crown segmentation thresholds, can substantially impact tree crown detection accuracy. This study aims to explore the influence of tree stand features and parameters on the effectiveness of the individual tree crown detection method based on a watershed algorithm, leading to identifying optimal configurations that enhance the reliability of forest inventories and support sustainable management practices. Our analysis of the algorithm results shows that the features of the tree stand, such as tree height variance and tree crown size variance, significantly impact the algorithm’s output in precisely estimating tree count. Consequently, adjusting the pixel size of a canopy height model in the context of tree stand features is necessary to minimize error. Additionally, our findings show that there is a need to carefully assess the criterion of membership of a detected tree crown in a circular sample plot, which we based on the point cloud. Full article

This study investigates the effect of the spatial distribution of soil water and nutrients on cork oak (Quercus suber) architecture. Fertirrigation is being tested in cork oak plantations to accelerate tree growth up to the production stage. To assess the impact of wet bulb location on tree development, six trees (three subjected to subsurface drip irrigation and three controls) were fully excavated at a sandy soil site, along with a seventh tree subjected to surface drip irrigation at a sandy loam soil site. The aerial parts of the trees were digitized using a Polhemus Fastrak magnetic digitizer and segmented into orders starting from the main trunk. Roots with diameters greater than 0.5 cm were digitized during excavation and segmented by size and order from the root collar. For each segment, length, orientation, and spatial location were calculated. General linear models were then applied to compare total root length across orientation and quadrant classes. Crown architecture was influenced by factors such as light competition. Irrigation treatments did not significantly affect root architecture when wet bulb formation was constrained. However, tree no. 7 had 50% of its total root length located within the wet bulb quadrant. These findings suggest that differences in soil type and irrigation method influence wet bulb formation, potentially reducing the impact of fertirrigation on root architecture. Strategies to minimize tree dependence on wet bulb zones are crucial for enabling future irrigation suppression. 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

The crown area is a critical metric for evaluating tree growth and supporting various ecological and forestry analyses. This study compares three approaches, i.e., unsupervised clustering, region-based, and deep learning, to estimate the crown area of Pinus eldarica Medw. using UAV-acquired RGB imagery (2 cm ground sampling distance) and high-density point clouds (1.27 points/cm³). The first approach applied unsupervised clustering techniques, such as Mean-shift and K-means, to directly estimate crown areas, bypassing tree top detection. The second employed a region-based approach, using Template Matching and Local Maxima (LM) for tree top identification, followed by Marker-Controlled Watershed (MCW) and Seeded Region Growing for crown delineation. The third approach utilized a Convolutional Neural Network (CNN) that integrated Digital Surface Model layers with the Visible Atmospheric Resistance Index for enhanced segmentation. The results were compared against field measurements and manual digitization. The findings reveal that CNN and MCW with LM were the most effective, particularly for small and large trees, though performance decreased for medium-sized crowns. CNN provided the most accurate results overall, with a relative root mean square error (RRMSE) of 8.85%, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a bias score (BS) of 1.00. The CNN crown area estimates showed strong correlations (R² = 0.83, 0.62, and 0.94 for small, medium, and large trees, respectively) with manually digitized references. This study underscores the value of advanced CNN techniques for precise crown area and shape estimation, highlighting the need for future research to refine algorithms for improved handling of crown size variability. Full article