Search Results (140)

Acquiring information about tree species has special relevance for the management and preservation of forests. Remote sensing has been widely used for this task, especially at landscape scales. However, less research has been done to acquire this information at the single-tree level. In recent decades, mobile laser scanner (MLS) platforms have progressively attracted interest in the forestry sector in regard to obtaining single-tree information. In this study, we examine the feasibility of differentiating between seven different tree species (Castanea sativa, Eucalyptus globulus, Eucalyptus nitens, Pinus pinaster, Pinus radiata, Quercus robur and Chamaecyparis lawsoniana) using MLS point clouds. The classifications were performed using a machine learning algorithm, random forest (RF), and a deep learning algorithm, namely, a 2D Convolutional Neural Network (CNN). The RF algorithm was used to identify tree species at the pixel level using raster layers of statistics that reflect the vertical distribution of the points within single-tree point clouds. The 2D CNN algorithm constructed multi-view 2D profiles of single-tree point clouds by rotating the point clouds around a single axis. Different 2D image sizes were tested. The 2D CNN algorithm outperformed the RF algorithm and yielded an Overall Accuracy of 93%. We also found that image size affected both the accuracy metrics obtained and the amount of training time needed. Of the tree species studied, Pinus radiata and Quercus robur had the lowest classification accuracies (with F-Scores of 83% and 87%, respectively). According to these results, MLS point clouds can be efficiently used in the forest sector to perform individual-tree species recognition through fully automated procedures. Full article

This manuscript will present an advancement of transformative research that has been conducted at Oak Ridge National Laboratory (ORNL) over a 25-year period (2000–2025) on a variety of environmental and biological matrices. These investigations derived a fundamental understanding of how elemental detection and analysis of these matrices led to the knowledge and discovery of natural processes in plants and the environment. Each project led to the initiation of a new research area which unearthed awesome and novel breakthroughs. Highlights are listed below: 1. The preliminary research at ORNL centered on the detection of aerosols utilizing Laser-induced Breakdown Spectroscopy (LIBS) technology. The Clean Air Act Amendment (CAAA) of 1990 highlighted the importance of identifying hazardous air pollutants (HAPs) due to their impact on environmental and human health, thereby underscoring the need to detect various toxic elements. Research in aerosol chemistry aimed to identify these harmful elements released by factories during periods of increased emissions in their manufacturing processes. LIBS emerged as the most effective method for real-time, in situ measurements of metal species in both gaseous and aerosol phases. 2. An understanding of the presence of total carbon in soils gives perspective on how to develop carbon sequestration strategies. The recognition that carbon sinks can evolve back to carbon sources to emit back to the atmosphere was an important consideration. Also, the concentration of carbon in soil indicates the health of land areas for growing crops successfully. 3. The direct detection of most of the elements in a wood sample in a single emission spectrum, without sample preparation, encouraged the research to use the LIBS technique for preservative treated wood coupled with use of multivariate statistical methodology. Additionally, it encouraged the researchers to try to differentiate natural woods from different parts of the country, and it was successfully demonstrated that LIBS coupled with MVA analysis could differentiate wood of different species from each other and of similar species grown in different environments based on their elemental spectra. This was a breakthrough since it revealed a systematic approach to connect elemental scarcity and abundance to either drought or typical rainfall conditions for the hardwood trees grown in specific areas. 4. Furthermore, the research progressed to reveal physiological and developmental processes contributing to biomass production such that the variation in leaf elemental composition increases our understanding of terrestrial nutrient cycles, as well as tracking the transfer of toxic elements from soils to living organisms. 5. Recently another breakthrough viz., ionomics initiated the correlation of elements to specific genes, uncovering the function that the element performed in the plant. More recently, this has been extended from plants to fungi as well as fungi growing in symbiotic relations with plants. Full article

Urban wetlands serve as vital spatial platforms for ecological restoration, carbon sequestration enhancement, and public environmental education; however, systematic research on their carbon sequestration benefits and the mechanisms linking these benefits to spatial design and public participation remains limited. Taking Nanjing Yuzui Wetland Park as a case study, this paper conducts a comprehensive assessment of plant community carbon sequestration benefits, spatial structural variations, and public ecological awareness by integrating official tree species data, field measurement data, the i-Tree Eco model, and questionnaire surveys. The results indicate that Yuzui Wetland Park already possesses a solid foundation for carbon sequestration, with a total carbon sequestration benefit of $48,777.38 and an average carbon sequestration benefit per tree of $24.63. Salix babylonica and Ulmus pumila L. are the primary contributors to current carbon sequestration, while Ginkgo biloba demonstrate strong long-term carbon storage potential, suggesting a clear division of labor among different tree species in short-term carbon fixation and long-term carbon storage. At the same time, significant differences exist between the native areas within the wetland and the peripheral roadside areas in terms of tree species composition, community structure, and carbon sequestration performance, indicating that spatial zoning is a key factor influencing the carbon sequestration benefits of urban wetlands. By integrating i-Tree Eco-based carbon assessment with questionnaire data, this study identifies a perceptual gap between the carbon-sink performance of Yuzui Wetland Park and public recognition of its ecological functions. The model results show measurable carbon-sequestration benefits and differentiated contribution patterns among tree species: Salix babylonica and Ulmus pumila L. contribute mainly to current carbon storage, while Ginkgo biloba shows long-term carbon-storage potential. The questionnaire results indicate that respondents recognized the wetland primarily through recreational and landscape functions, with lower recognition of carbon sequestration and water purification. These findings provide the basis for translating carbon-sink assessment into spatial visualization, environmental interpretation, and participatory landscape design. The study indicates that the optimization of urban wetlands should not be limited to plant selection and ecological calculations but should further integrate carbon sink assessment with spatial design and public participation to enhance the perceptibility, communicability, and practicability of wetland ecological value. Full article

Allium kazim-kosei sp. nov. (Amaryllidaceae, sect. Codonoprasum) is described as a new species from Central Anatolia, Türkiye. The new species is morphologically similar to A. pseudoflavum but differs in several diagnostic characters, including bulb structure, scape height, leaf morphology, spathe venation, inflorescence and pedicel dimensions, tepal shape, presence of interstaminal teeth, capsule shape, and seed size. SEM observations reveal distinct micromorphological differences in seed testa ornamentation and pollen exine structure between the two species. Molecular phylogenetic analyses based on nuclear ITS and chloroplast trnL intron sequences support the recognition of A. kazim-kosei as a distinct species. The Kimura 2-parameter (K2P) genetic distance between A. kazim-kosei and A. pseudoflavum (8.54% for ITS) is considerably higher than typical interspecific divergences within sect. Codonoprasum. In the Maximum Likelihood phylogenetic tree, the two species form well-separated sister branches with high bootstrap support. The species is known only from gypseous soils around Kavuncu village (Eskişehir province, Türkiye), with an estimated Area of Occupancy (AOO) of 8 km² and Extent of Occurrence (EOO) of 45 km². Based on IUCN criteria, A. kazim-kosei is assessed as Endangered (EN) [B1ab(iii) + B2ab(iii)]. This discovery increases the total number of Allium species in Türkiye to 239 and the number of sect. Codonoprasum taxa to 74. The molecular results are fully congruent with the macro- and micromorphological characters, providing robust multi-evidence support for the recognition of the new species. Full article

Crown-level tree species semantic segmentation enables fine-grained forest inventory and management. Current high-precision tree species classification typically relies on multi-source remote sensing data, the acquisition and processing of which remain costly for large-area applications, making low-cost unmanned aerial vehicle (UAV) RGB imagery an attractive option for large-scale forest mapping. However, in heterogeneous forests, complex canopy structures and the limited spectral discriminability of low-cost UAV RGB imagery make 2D appearance cues alone insufficient for reliable species discrimination, crown delineation, and accurate separation of adjacent crowns. This often leads to inter-class confusion, blurred crown boundaries, and poor recognition of small crowns. To address these limitations, this paper proposes MonoCrown (MCrown), which strengthens geometric and contextual representation for distinguishing visually similar species and delineating crowns from single-temporal UAV RGB imagery. To compensate for the insufficiency of appearance cues, MCrown introduces monocular depth inferred offline from the same RGB image as a frozen geometric prior, and integrates cross-window global–local attention (CW-GLA), bidirectional cross-modal attention (BiCoAttn), and depth-adaptive injection (DAI) to capture long-range dependencies and promote complementary use of appearance and geometric features, especially for small crowns with similar visual patterns in complex scenes. To validate the method’s effectiveness, a crown-level UAV RGB dataset covering approximately 40 km² was constructed. Systematic comparative experiments were conducted on the proposed dataset and on public benchmarks, supporting the effectiveness of the proposed approach across ten dominant classes, especially for small crowns and visually similar categories. Its mean Intersection over Union (mIoU) and overall accuracy (OA) reached 74.1% and 87.3%, respectively. The method achieves high-precision crown-level tree species semantic segmentation using single-temporal UAV RGB as the sole acquired modality, while monocular depth inferred from the same RGB image serves only as a frozen geometric prior, without requiring multispectral, multi-temporal, or active-sensor acquisitions. This offers a practical solution for crown-level tree species mapping in heterogeneous forests. Full article

Accurate identification of tree species is important for forest management, biodiversity studies, and precision forestry. Near-range LiDAR point clouds provide detailed three-dimensional information about individual trees. However, the complex structure of the point clouds and the unbalanced distribution of species make automatic classification difficult. To address these issues, this study presents a Transformer model with geometric enhancement. The model combines local geometric features and global attention to improve species recognition in forest environments. It uses geometric information with biological meaning, including point cloud normals, local density, vertical structure, and growth direction. A focal loss with class balance is also introduced to reduce the impact of species distributions with long tails. Experiments on the ForSpecial20K dataset show that the proposed method performs better than representative models based on convolution, graph methods, and Transformer architectures. It achieves higher overall accuracy (78.20%), higher mean class accuracy (73.48%), and a higher Macro-F1 score (73.21%). Results from confusion matrices and visual analysis of similar species further verify the effectiveness of the geometric features and the loss design. These results suggest that modeling structural information of forests helps improve robustness and generalization. The proposed method offers a practical solution for tree-level species mapping, fusion of LiDAR data from multiple sources, and fine-scale forest inventory. It also shows the value of combining high-resolution LiDAR data with deep learning for forestry applications. Full article

The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable practices. This study addresses this by classifying images of timber logs by tree species and market value using the Orange data mining software, which leverages pre-trained convolutional neural networks (Inception v3 and SqueezeNet) to generate embeddings from a dataset of 5549 images collected at a real timber auction in Slovenia, followed by logistic regression image classification. Results show high accuracy for tree species classification (up to 92.6%), but substantially lower accuracy for market value classification (40%–55%), reflecting the greater complexity of value determination from visual features. These findings underscore the promise of deep learning for species identification while indicating the need for further methodological advancements to enhance value classification reliability, which offers the practical impact for operational forestry and bioeconomy value chains. Full article

The Morus genus comprises several tree species whose fruits are used in human nutrition, while the leaves and roots are used in traditional medicine. The aim of this study was to highlight the antioxidant, cholinesterase inhibitory, and neuroprotective effects of hydroalcoholic extracts from Morus alba (MAE) and Morus nigra (MNE) leaves. RP-UHPLC-PDA analysis of extracts revealed the presence of polyphenols in higher quantities in MNE extract compared to MAE. Both extracts demonstrated antioxidant properties in the hydroxyl radical scavenging and lipid peroxidation inhibition assays. MNE exhibited a superior antioxidant capacity compared to MAE; the IC₅₀ values for the inhibition of plasma lipid oxidation assay were 25.31 ± 2.54 µg/mL for MNE and 29.85 ± 0.97 µg/mL for MAE. Both extracts showed cholinesterase inhibitory activity. The IC₅₀ values for acetylcholinesterase inhibition were 24.34 ± 0.86 µg/mL for MNE and 46.87 ± 2.16 µg/mL for MAE. The inhibitory potency of MNE was comparable to that of galantamine, which was used as standard. Both extracts reversed, in a dose-dependent manner, the scopolamine-induced cognitive impairment and behavioural alterations in scopolamine-treated zebrafish (Danio rerio) as evaluated by the Y-maze test, novel tank diving test, and novel object recognition test. Full article

On university campuses, trees and green spaces shape how students and staff move and use outdoor spaces. To support planning, tree species information is needed at the level of individual trees. Tree species classification from UAV RGB imagery remains difficult in complex campus scenes because roads, buildings, shadows and subtle inter species differences degrade recognition. To address background interference, the loss of subtle fine-grained cues before tokenization, and insufficient local structure modeling in lightweight transformer-based classification, we propose TreeSpecViT for tree species classification. It uses a MobileViT backbone and a Background Suppression Module (BSM) to reduce clutter from non-canopy regions. A Fine-Grained Feature Guidance (FGF) module is inserted before the unfold operation to enhance canopy details and guide tokenization toward key regions. $1\times 1$ convolutional neck layers align channels, and a Global and Local Fusion (GLF) module jointly models overall crown semantics and local textures for species recognition. From the predicted masks and species labels, we build an individual tree digital archive. The archive stores per tree geometric attributes and can be linked with grids of campus activity intensity to analyze how activity patterns relate to vegetation structure. TreeSpecViT achieves an Accuracy of 87.88% (+6.06%) and an F1 score of 76.48% (+5.08%) on the SZUTreeDataset. On our self constructed NJFUDataset, it reaches 76.30% (+5.10%) in Accuracy and 70.10% (+7.20%) in F1. These results surpass mainstream models. Ablation experiments show that the modules jointly reduce background clutter and enhance canopy features. Overall, TreeSpecViT supports campus scale analyses that link human activity intensity to vegetation patterns and provides a practical basis for planning and adjusting campus green spaces. Full article

Sustained abiotic stress severely impairs fruit crop growth and development. As plants’ primary environmental sensing organ, fruit tree roots experience disrupted morphogenesis and physiological functions, reducing yield, lowering fruit quality, and threatening orchard ecosystem stability. Abiotic stress is diverse: water deficit from drought, extreme temperature fluctuations, and salinization-induced ion imbalance, heavy metal accumulation, or nutrient disorders. Its complexity requires synergistic and crosstalk regulation of multiple root-specific signaling modules and pathways in root stress perception and transduction. When responding to stress, roots activate hormone, reactive oxygen species (ROS), and calcium ion (Ca²⁺) signaling. These pathways mediate early stress recognition and regulate downstream gene expression and physiological metabolic reprogramming via transcription factors (TFs) and other regulators, determining stress tolerance and adaptability. Using typical abiotic stresses as models, this review outlines the composition, activation mechanisms, specificity, and synergistic effects of root-specific signaling modules/pathways, along with modern biotechnologies for decoding these modules and current research limitations, aiming to reveal the root signal network’s integration mode. Full article

During a late-winter field survey in Baihuashan National Nature Reserve, Beijing, several noctuid moths were observed flying during the daytime at low temperatures and being actively preyed upon by Marsh tits, which removed the heads and wings of captured individuals. These observations indicate that adults of this noctuid lineage are active in late winter, providing a critical nutritional resource for insectivorous birds during the ecologically constrained, food-limited winter period. Here, we formally describe this lineage as a new genus, Shoudus gen. nov., based on a new species, S. baihuashanus sp. nov., collected from Baihuashan reserve, including three specimens retrieved during active interception of tit predation, along with detached wings and heads recovered from the snow. The new genus is placed in the tribe Orthosiini Guenée, 1837, primarily based on adult external morphology, including large compound eyes with long interfacetal hairs and bipectinate male antennae, as well as forewing patterning similar to certain orthosiine genera such as Perigrapha and Clavipalpula. Notably, the dark reddish-brown forewings with sharply contrasting pale markings, as seen in the new genus and these related genera, appear well adapted for camouflage against bark, leaf litter, and exposed soil in their habitats—potentially functioning as both background matching and disruptive coloration. To further assess its phylogenetic placement, we conducted a molecular analysis based on mitochondrial COI sequences (13 newly generated and 6 retrieved from BOLD/NCBI). The resulting maximum likelihood and Bayesian trees consistently support the monophyly of the new genus and reveal a close phylogenetic relationship with Orthosia, the type genus of Orthosiini. This integrative evidence strongly supports the recognition of Shoudus as a distinct lineage within Orthosiini. Full article

Wild ginkgo, as an endangered species, holds significant value for genetic resource conservation, yet its practical applications face numerous challenges. Traditional field surveys are inefficient in mountainous mixed forests, while satellite remote sensing is limited by spatial resolution. Current deep learning approaches relying on single-source data or merely simple multi-source fusion fail to fully exploit information, leading to suboptimal recognition performance. This study presents a multimodal ginkgo crown dataset, comprising RGB and multispectral images acquired by an UAV platform. To achieve precise crown segmentation with this data, we propose a novel dual-branch dynamic weighting fusion network, termed dual-branch cross-modal attention-enhanced UNet (DCA-UNet). We design a dual-branch encoder (DBE) with a two-stream architecture for independent feature extraction from each modality. We further develop a cross-modal interaction fusion module (CIF), employing cross-modal attention and learnable dynamic weights to boost multi-source information fusion. Additionally, we introduce an attention-enhanced decoder (AED) that combines progressive upsampling with a hybrid channel-spatial attention mechanism, thereby effectively utilizing multi-scale features and enhancing boundary semantic consistency. Evaluation on the ginkgo dataset demonstrates that DCA-UNet achieves a segmentation performance of 93.42% IoU (Intersection over Union), 96.82% PA (Pixel Accuracy), 96.38% Precision, and 96.60% F1-score. These results outperform differential feature attention fusion network (DFAFNet) by 12.19%, 6.37%, 4.62%, and 6.95%, respectively, and surpasses the single-modality baselines (RGB or multispectral) in all metrics. Superior performance on cross-flight-altitude data further validates the model’s strong generalization capability and robustness in complex scenarios. These results demonstrate the superiority of DCA-UNet in UAV-based multimodal ginkgo crown recognition, offering a reliable and efficient solution for monitoring wild endangered tree species. Full article

Swida wilsoniana is an important oil-producing tree species whose fruits are rich in unsaturated fatty acids with high nutritional and medicinal value. Lipases are involved not only in lipid mobilization but also potentially in the regulation of fatty acid composition and oil accumulation in plants. In this study, the fatty acid composition of S. wilsoniana fruits was analyzed using gas chromatography–flame ionization detection (GC-FID), and the three most abundant fatty acids were selected as molecular docking ligands. Based on overall multi-ligand docking performance (including mean affinity across the three ligands), three key lipases—SwL5, SwL8, and SwL12—were identified as having the strongest interactions with these fatty acids. Phylogenetic analysis revealed that SwL5 and SwL12 belong to lipase family II, while SwL8 is classified into family VI. Molecular dynamics simulations were further performed to evaluate the binding stability and to characterize the structural basis of substrate recognition, including key interacting residues. This study provides theoretical insights into the molecular regulation of fatty acid composition in S. wilsoniana, and offers potential gene targets for the genetic improvement of oil quality traits. Full article

During the development of the gametophyte in angiosperms, a series of processes occurs, including pollination, pollen recognition, adhesion, hydration, germination, pollen tube growth, and the guidance of the pollen tube toward the ovule for the delivery of sperm cells to the female gametophyte. These processes require a substantial energy supply, which is provided by cellular respiration in the plant. Throughout this sequence, the generation of reactive oxygen species (ROS) is concomitantly observed. At present, the mechanisms underlying ROS production remain incompletely understood, especially in plant trees such as Salix linearistipularis. In this study, pistils of S. linearistipularis were used as experimental materials, and pistils were divided according to their development into three stages—S1, S2, and S3. Transcriptome sequencing (RNA-Seq) was performed for the three developmental stages, and the results indicated that metabolic pathways associated with oxidoreductase activity were highly significant during pistil development in S. linearistipularis. During pistil development, the levels of ROS accumulated rapidly. After pollination, with the adhesion and germination of pollen, the levels of ROS decreased significantly. Moreover, bidirectional regulation of ROS levels revealed that treatment with ROS inducers and scavengers led to increased and decreased ROS accumulation, which were accompanied by the inhibition and promotion of pollen tube number and length. These two opposite results indicate that ROS are the key factor regulating pistil development and pollen tube germination in S. linearistipularis. Full article

The species, quantity, and tree diameter at breast height (DBH) are important indicators for assessing species distribution, individual growth status, and overall health in the forest. The existing tree information collection mainly relies on manual labor, which results in low efficiency and high labor intensity. To address these issues, we propose a method for tree species identification and diameter estimation by combining deep learning algorithms with binocular vision. First, an image acquisition platform is designed and integrated with a weeding machine to capture images during weeding operation. Images of seven types of trees are captured to develop a dataset. Second, a tree species identification model is established based on the YOLOv8n network, achieving 98.5% accuracy, 99.0% recall, and 99.2% mAP. Then, an improved YOLOv8n-seg model is proposed. It simplifies the network by introducing VanillaBlock in the backbone. FasterNet with a CCFM structure is added at the neck to enhance the model’s multi-scale expression capability. The mIoU of the improved model is 93.7%. Finally, the improved YOLOv8n-seg model is combined with binocular vision. After obtaining the segmentation mask of the tree, the spatial position of the two measurement points is calculated, allowing for the measurement of tree diameter. Verification experiments show that the average error for tree diameter ranges from 4.40~6.40 mm, and the proposed error compensation method can reduce diameter errors. This study provides a theoretical foundation and technical support for intelligent collection of tree information. Full article