Search Results (1,357)

Ash dieback, together with altered hydrological conditions and limited natural regeneration, threatens narrow-leaved ash (Fraxinus angustifolia Vahl) forests in Croatian floodplains. This study evaluated whether individual-tree attributes and stand structure were associated with crown damage and short-term radial-growth resilience. Ten stands in the middle Sava River floodplain were sampled using 41 circular plots. Stand density, basal area, and growing stock were estimated at the plot level. Diameter, height, crown dimensions, and crown damage were assessed on a subsample of 154 trees, from which increment cores were also collected. Ring-width measurements were used to calculate the Resilience index (Rs_3/6). Relationships among variables were evaluated using linear correlation and one-way analysis of variance was used for testing differences in crown damage and resilience index between categorized tree and stand variables. Mean crown damage was 24.4%, while the mean Rs_3/6 value of 0.90 indicated that radial growth had generally not returned to its previous level. Crown damage was negatively correlated with growth resilience. Trees in the 15–30 cm diameter class and the 21–40-year age class showed the lowest crown damage and highest resilience. The most favorable values of both indicators occurred at stand densities of 800–1200 trees ha⁻¹, while crown damage was lowest at basal areas below 15 m² ha⁻¹. Although the observed associations were moderate and should not be interpreted as causal effects, the combined use of crown condition and radial-growth recovery can support the identification of vigorous trees and inform adaptive, site-specific management of declining narrow-leaved ash stands. Full article

Object detection and instance-segmentation methods have been widely used to locate trees and delineate individual crowns from aerial imagery, providing valuable tree-level information for estimating tree abundance, density, and spatial distribution. These variables form important inputs for quantitative assessments of forests and urban greenery, including analyses of thermal environments, pollutant dispersion and removal, and carbon storage and sequestration. Yet, existing approaches remain centered on individual trees or crowns and lack a spatially continuous representation of forest structural conditions, leaving a gap between discrete tree-level objects and continuous forest structural patterns. To address this gap, this study develops a geospatial multilayer supervised-classification framework that jointly estimates visible tree-top abundance and maps continuous forest structural zones (FSZs), thereby integrating tree-count information with spatially explicit structural characterization. The FSZ workflow was evaluated against YOLO11n and FastViT-T8-FPN Faster R-CNN in Domain A and independently evaluated in Domain B (an urban-fringe planted landscape). It achieved an OA of 0.825 and absolute TCRE of 14.29% in Domain A and an OA of 0.856 and absolute TCRE of 19.15% in Domain B, demonstrating good applicability across both scenarios. By linking tree-top abundance with landscape-scale structural patterns, the FSZ framework provides a complementary spatial data product for forest and urban-greenery quantification and offers a foundation for subsequent assessments of microclimate regulation, air-pollution management, carbon accounting, and green-infrastructure performance. Full article

Understanding how climate, altitude and evolutionary diversity affect regeneration is crucial for predicting species persistence in changing environments. Yet, in tropical forests, most research has focused on timber trees. This limits our understanding of how small-statured yet socio-ecologically important species respond to environmental gradients and evolutionary diversity. We investigated the regeneration and structural patterns of Pavetta crassipes K. Schum, a widely used shrub, across the Sudanian and Sudano-Guinean zones of West Africa. We used generalized mixed-effects models to assess the effects of climate, altitude, and phylogenetic diversity on seedling occurrence, density, and adult structure. Although altitude exerted contrasting effects depending on climatic zone, seedling probability increased with altitude in the Sudano-Guinean zone but decreased with altitude in the Sudanian zone. Across both climatic zones, higher evolutionary diversity enhanced the occurrence and density of seedlings. Adult structure also varied with environment. Phylogenetic diversity further promoted crown development, suggesting long-term structural advantages in diverse communities. These findings reveal that regeneration in P. crassipes is not solely governed by climate or altitude but is strongly reinforced by evolutionary breadth. Conservation strategies that maintain phylogenetic diversity will therefore be central to sustaining regeneration dynamics and ensuring the persistence of P. crassipes in fragmented landscapes. Full article

Precise estimation of aboveground biomass in urban forests is crucial for quantifying urban carbon stocks and supporting climate change mitigation efforts. However, the availability of allometric equations tailored for urban trees is limited. Existing equations often rely on data from harvested trees with restricted sample sizes and small diameters, thereby introducing substantial uncertainty into biomass assessments. This study utilized terrestrial laser scanning (TLS) in conjunction with a leaf-wood separation algorithm and a tree quantitative structure model (TreeQSM) as a non-destructive approach to develop new species-specific allometric models for four predominant evergreen broadleaved tree species in the urban forests of Shanghai, based on 10 sample plots and 303 trees. The results showed that TLS-derived multivariate models, which incorporated diameter at breast height (DBH), tree height, and crown diameter, consistently outperformed models that only included DBH. Compared with the TLS-derived biomass, the previously published models showed varying degrees of deviation. Notably, there was a substantial overestimation for Camphora officinarum, with a bias of +31.5%. In contrast, Elaeocarpus decipiens, Ligustrum lucidum, and Magnolia grandiflora demonstrated smaller underestimations, with biases of −9.4%, −0.9%, and −4.5%, respectively. These discrepancies were primarily attributed to the extrapolation beyond the calibration diameter at DBH ranges of the published equations. These findings highlight the critical need for urban-specific models. Because destructive harvesting was not feasible in the urban environment, the TLS-derived biomass estimates were not validated against destructively measured biomass. The equations developed in this study provide improved tools for estimating urban forest biomass and carbon accounting for the four studied species under the sampled conditions in Shanghai. Full article

Silver birch (Betula pendula Roth) is an ecologically important pioneer tree species widely distributed throughout Europe. Despite numerous reports of birch decline, long-term nationwide assessments integrating multiple categories of damage remain limited. The present study investigated temporal patterns of damage occurrence, severity, distribution, and presumed causal agents affecting silver birch in Poland using nationwide ICP Forests Level I monitoring data collected between 2007 and 2025. The analyses were based on 75,609 damage observations recorded for 7039 trees on 945 monitoring plots distributed across the country. Damage frequency increased markedly after 2014 and reached a maximum during 2018–2019, when approximately 90% of monitored trees exhibited at least one damage symptom. Foliage damage represented the most frequent category throughout the study period and was overwhelmingly associated with insect activity. Partially or completely eaten leaves accounted for approximately 80% of all foliage-damage observations, although damage severity was generally low. Stem damage increased continuously during the monitoring period and was dominated by deformations and stem inclination. Decay occurred less frequently but was associated with the highest severity classes and showed a strong relationship with fungal agents. An increasing proportion of trees were affected by multiple groups of damaging factors simultaneously, indicating the growing complexity of decline processes. The results suggest that silver birch decline may be driven by interacting biotic and abiotic stressors rather than by a single causal agent. The observed patterns are consistent with a decline-spiral model in which long-term reductions in water availability and increasing environmental stress may predispose trees to insect damage, root pathogens, stem and branch decay, structural instability, and ultimately mortality. The nationwide scale and long-term character of the ICP Forests dataset provide important insights into contemporary drivers of birch decline under changing environmental conditions. Full article

Early detection of pine wilt disease (PWD) before visible crown discoloration is important for field inspection, confirmatory sampling, and timely management. However, because individual trees reach visible discoloration at different times, calendar-based analyses can blur the development of pre-visual spectral responses. Weekly UAV multispectral data from two Pinus massoniana forest sites were therefore aligned to the first visible crown discoloration of each tree. Crown-level band reflectance and vegetation indices (VIs) were examined from eight to one weeks before first visible discoloration, and healthy reference observations at each relative week were used to define 5th–95th percentile reference ranges for detection-rate (DR) analysis. Several VIs showed significant group-level differences from approximately seven weeks before first visible discoloration, whereas mean VI-based DR increased markedly to 0.52 at two weeks and 0.68 at one week before discoloration; single-band DRs were less stable. These results indicate a progressive pre-visual spectral response, with an exploratory early group-level indication emerging approximately seven weeks before discoloration and a stronger, more consistent spectral departure during the final two weeks. This onset-aligned framework provides a practical basis for scheduling repeated UAV surveillance and prioritizing field verification before obvious symptoms emerge. Full article

Early detection of tree-seedling establishment is essential for monitoring regeneration success in coastal-dune plantations, where conventional field assessments remain labour-intensive and spatially limited. This study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds. Field surveys in the Quiaios National Forest, Portugal, mapped approximately 1500 seedlings using RTK GNSS positioning, biometric measurements, and field photographs. Multispectral imagery acquired with a DJI Mavic 3 Multispectral platform was processed through Structure-from-Motion to generate calibrated orthomosaics, terrain products, and dense point clouds. Training-data preparation combined pine-centred buffers, spectral conditioning, manual refinement and point-cloud class assignment. Point Transformer V3 models were trained in ArcGIS Pro and evaluated using field-mapped buffers withheld from model training within plantation-line areas. The Baseline high-recall model achieved 88% object-level recall at the operational threshold of at least three classified Pine-Seedling points per buffer. The refined hard-negative model retained 84% recall while reducing off-buffer detections from 243 to 41. False-negative analysis showed that omissions were associated with reduced crown diameter and limited branch development under the adopted buffer-based retrieval framework. These results support transformer-based multispectral point-cloud classification for scalable monitoring of early-stage pine regeneration in heterogeneous coastal environments. Full article

Urban London plane (Platanus × acerifolia) street trees require efficient and interpretable methods for identifying individuals that warrant closer inspection within large roadside inventories. This study developed a two-layer screening framework for 2022 London plane trees along six urban roads in Nanjing, China. First, wind-tunnel tests of five standardized crown geometries were used to derive geometry-specific drag-moment coefficients and calculate aerodynamic crown-loading potential (ACLP), a relative indicator of crown-related aerodynamic demand. ACLP was calculated only for trees within the tested crown height-to-width range. Second, measurements from 19 windthrown London plane trees after Typhoon Bebinca were used to derive a local lower-quartile root-plate reference and calculate a root-plate reference aperture ratio (RPAR) from recorded tree-pit width. Of the 2022 inventoried trees, 1273 (63.0%) fell within the tested crown-geometry range, and 128 were identified as high-ACLP candidates using a 90th-percentile screening threshold. These candidates were strongly concentrated on two roads, and the principal road-level pattern remained stable across alternative percentile thresholds. The post-storm analysis provided a separate local reference for identifying tree-pit apertures that were comparatively small relative to observed root-plate dimensions. ACLP and RPAR should therefore be interpreted as distinct first-stage screening indicators rather than as predictors of windthrow probability or anchorage capacity. The framework provides a transparent basis for prioritizing follow-up field inspection and allocating limited urban tree-management resources. Full article

Urban-forest monitoring increasingly requires intelligent sensor-driven systems capable of characterizing short-term tree responses while operating efficiently within Internet of Things (IoT) and edge-computing environments. This study proposes a fusion-based artificial intelligence framework that integrates Quantization-Aware Training (QAT)-optimized PointNet++ models with machine-learning regression to predict a short-term dendrometer-derived stem-diameter response expressed in biomass-equivalent units. The framework combines 1024-point LiDAR tree representations, geometric measurements, and environmental sensor data through three components: QAT-optimized PointNet++ models for 34-species classification and trunk–crown part segmentation, frozen model-based prediction and geometric feature extraction, and MLP and XGBoost regression models for prediction of the short-term target. The dataset contained 2694 trees from five regions of South Korea, with the target derived from dendrometer-based stem-diameter measurements recorded over a 14-day interval between 8 September 2022 and 22 September 2022. Importantly, this short-term signal reflects both structural and reversible water-status-related stem dynamics and is therefore not interpreted as direct dry-biomass accumulation or carbon sequestration. The QAT-optimized models retained 92.52% segmentation accuracy (82.67% mIoU) and 80.46% species-classification accuracy, while the regression model reached R² = 0.9663 and RMSE = 0.4437 kg for the defined biomass-equivalent target. Quantization reduced the saved model size of both encoders by approximately 10.5× (21 MB → 2 MB) and accelerated CPU inference by up to 4.1×. These efficiency measurements were obtained on an ×86 desktop CPU and therefore characterize computational compression benefits rather than completed deployment or field validation on a low-power embedded device. These results demonstrate the computational feasibility of combining compressed point-cloud perception with multimodal prediction of short-term dendrometer-derived stem dynamics. Validation over seasonal and multi-year periods using independent biomass-reference measurements would be required before extending the framework to long-term biomass accumulation or carbon-sequestration assessment. Full article

Although Populus euphratica forests are key components of arid riparian ecosystems, the demographic and competitive processes associated with their population persistence remain insufficiently understood. We investigated population structure, demographic dynamics and intraspecific competition in a natural P. euphratica population within the Tarim River Basin, northwestern China. Field data from 2690 individuals were analyzed using diameter-class structure, population dynamic indices and the Hegyi competition index. The population exhibited an inverse J-shaped diameter distribution: Class I contained 1601 individuals (59.5% of the total population), and Classes I–III together accounted for 83.9%, indicating active recruitment but limited transition into intermediate diameter classes. Competition intensity was negatively correlated with DBH, tree height and crown area (r = −0.42, −0.41 and −0.31, respectively; p < 0.001) but positively correlated with local neighbourhood density (r = 0.39; p < 0.001). The multiple regression model explained 27.1% of the variation in competition intensity, and the DBH power-law model provided the lowest residual standard error (RSE = 4.887). These findings suggest that demographic bottlenecks and size-dependent competition are closely associated with P. euphratica persistence and provide field-based evidence for competitive filtering in arid riparian tree populations. Full article

Individual-tree-level information on crown distribution and morphology underpins forest inventory, biomass estimation, and carbon accounting. High-resolution Unmanned Aerial Vehicle (UAV) imagery resolves single-tree detail, but automated crown extraction remains difficult: fully supervised segmentation depends on costly pixel-level annotation that generalizes poorly, while the Segment Anything Model (SAM), though training-free, cannot locate trees on its own and existing SAM-based methods restore this ability only by adding task-specific training. We present an end-to-end, annotation-free toolkit for tree crown extraction and ecological analysis. A RetinaNet-based DeepForest detector produces coarse boxes; an adaptive module then removes duplicate boxes and non-vegetation false positives using an intersection-over-union rule and a global greenness index, converting noisy boxes into clean prompts; these prompts drive SAM to decode irregular crown masks without task-specific training; and geometric and texture features are extracted and grouped by principal component analysis and K-means clustering to map ecological patterns. We evaluated the toolkit on multi-biome imagery from the public OAM-TCD dataset. Because pixel-exact metrics are unstable at 10 cm resolution, where wind sway, shadow shift, and small labeling offsets are strongly amplified, we assessed accuracy under an absolute physical-distance tolerance. At a 2.0 m tolerance, consistent with the effective radius of a mature crown, the toolkit reached a precision of 91.25%, a recall of 86.40%, and an F1-score of 88.76%; bootstrap and Monte Carlo resampling confirmed these values are stable. Without manual annotation, it characterized more than 4700 individual crowns and recovered distinct vegetation patterns across geographic settings, offering a highly adaptable, low-cost baseline tool that demonstrates robust performance across the diverse multi-biome scenes within the OAM-TCD dataset. Full article

Korean pine (Pinus koraiensis) nut-timber plantations are important for both timber and seed production, yet optimal water and fertilizer management for mature cone-bearing stands remains poorly understood. A two-year field experiment was conducted to evaluate the effects of three fertilization levels (F1, F2, and F3, corresponding to N:P₂O₅:K₂O application rates of 50:75:25, 100:150:50, and 150:225:75 kg ha⁻¹, respectively) and three soil moisture regimes corresponding to 80%, 60%, and 40% of field capacity (W1, W2, and W3, respectively) on tree growth, cone yield, and soil physicochemical properties in approximately 35-year-old Korean pine plantations established on Albeluvisol at Maoer Mountain, northeastern China. Tree growth and cone yield generally followed the order F2 > F3 > F1 and W2 > W1 > W3, with F2W2 (N:P₂O₅:K₂O = 100:150:50kg ha⁻¹ and 60% of field capacity) consistently producing the best performance. Compared with the control (CK, no fertilizer application, rainfed under natural ambient conditions), F2W2 increased height, diameter, and crown width increments by 46.2%, 71.4%, and 65.6%, respectively, in 2023. Per-tree cone number, total cone mass, and total pine nut mass increased progressively across years, reaching increases of 88.5%, 100.6%, and 132.4%, respectively, in 2024. In contrast, thousand-seed weight showed relatively small changes and a delayed water–fertilizer interaction. Water–fertilizer coupling significantly altered soil physicochemical properties by reducing soil pH under the optimal treatment, while also regulating inorganic nitrogen availability and soil nutrient distribution. Nitrate nitrogen was highest under W2, whereas ammonium nitrogen peaked under W1. Total nitrogen was highest under F3W1, while available phosphorus and potassium accumulated under high fertilization combined with non-optimal soil moisture, but were lowest under F2W2, indicating enhanced nutrient uptake under the optimal treatment. Cluster analysis showed that nitrate nitrogen was positively associated with growth and yield variables. Overall, F2W2 provided the most favorable balance between stand productivity and soil nutrient status, representing an effective water–fertilizer management strategy for mature Korean pine nut-timber plantations on Albeluvisol. These findings provide a scientific basis for precision water and nutrient management in northeastern China. Full article

The presence of mistletoe in pine stands has expanded in recent decades, currently threatening Mediterranean forests. Mistletoe outbreaks can make the host trees more vulnerable to intense droughts, which are expected to increase due to climate change. We use multispectral (MS) and LiDAR UAV-derived data to classify Viscum album L. ssp. austriacum infestation levels at individual tree level in Scots pine (Pinus sylvestris L.) forests. First, spectral and structural differences between three infestation levels were assessed employing Kruskal–Wallis test with Benjamini–Hochberg correction and post hoc Dunn’s test for individual tree crowns. Second, classification algorithms were applied to evaluate infestation levels at the individual tree scale by combining UAV-derived datasets. The outcomes revealed significant differences between infestation levels in canopy cover and height based on LiDAR-derived metrics. Significant changes in vegetation vigor were also found through spectral and textural metrics. The highest classification accuracy (0.87) was achieved by the spectral metrics CIRE and NDVI in an SVM model, outperforming models based on the combination of sensor metrics (0.82) or solely LiDAR variables (0.69, MLR). This approach demonstrates their potential for detecting and characterizing morphological changes in up to three levels of mistletoe infestation at individual trees in Mediterranean Scots pine forests, lending support to forest management monitoring. Full article

Accurate stem volume estimation is fundamental to forest resource inventory and carbon stock assessment. Traditional methods rely on destructive sampling, whereas terrestrial laser scanning (TLS) offers a non-destructive alternative. However, the accuracy of individual tree segmentation in structurally complex subtropical natural forests is constrained by crown overlap, species mixing, and vertical stratification. In this study, we developed a semantic voxel-based framework for individual tree segmentation and robust stem volume estimation from plot-level TLS point clouds. The method integrates 3D-CNN-based voxel semantic classification with bottom-up tree growth segmentation, followed by parameter extraction, taper equation fitting, and volume estimation using the sectional measurement method. Evaluation across 18 plots (1451 trees) in Guangxi, Southern China, demonstrated that the proposed method achieved an F-score of 0.881 for individual tree segmentation, significantly outperforming conventional CHM-based (0.533) and geometric voxel-based (0.794) approaches. Optimal taper equations were established for Chinese fir (Zeng Weisheng model, validation R² = 0.943) and Eucalyptus (Yan Ruohai model, validation R² = 0.987). TLS-based volume estimates yielded R² values of 0.94–0.97 and RMSE of 0.022–0.037 m³ per tree, with negligible systematic bias. These findings demonstrate that the proposed semantic voxel framework enables accurate and non-destructive stem volume estimation in complex subtropical forests, providing a practical technological pathway for modernizing forest inventory practices. Full article

Under climate warming, bark beetle outbreaks increasingly threaten forest health. Hylurgops longipillus, a native bark beetle in Northeast China, causes patchy mortality in Pinus koraiensis plantations. We developed an integrated emergency control system based on host selection chemical ecology. Damage characterization revealed that adults concentrated on trees with 10–50% crown needle yellowing, and infestations expanded as discrete patches radiating from multiple epicenters. Electrophysiological and behavioral assays of host volatiles and some of their isomers yielded a highly effective attractant (69.4 adults per five-trap set over 5 d, 1.8× control) and a repellent reducing ethanol-baited trap catches by 83.3%. Emergency control techniques consisted of integrated management combining removal and safe disposal of infested trees, chemical stump sealing, push–pull trapping, and tree vigor enhancement. Three-year monitoring showed ~94% decline in trap catches, no subsequent outbreaks, and stable control. This attractant-centered, chemical ecology-based system can rapidly suppress H. longipillus outbreaks and achieve long-term management, providing a scientific basis and operational framework for similar localized bark beetle emergencies under climate warming. Full article