Search Results (415)

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

With the increasing application of the Vertical Shaft Machine (VSM) method in ultra-deep shafts, accurate prediction of construction-induced structural stresses is vital for engineering safety. Currently, VSM is predominantly used in soft soils, where structural response analysis still relies on finite element (FE) simulations that are computationally intensive and complex to model. To improve analysis efficiency and understand the structural behavior of VSM shafts in granite composite strata, this study takes the first VSM shaft project in South China—the Guangzhou–Huadu Intercity Railway Shield Shaft—as a case study. A “monitoring-driven, large-sample data, machine learning substitution” framework is proposed for predicting structural stresses during construction. The framework calibrates an FE model using monitoring data. Through full factorial design, key design parameters—including main reinforcement diameter, stirrup diameter, concrete strength grade, and steel plate thickness—are systematically varied. Parametric FE simulations are then conducted to construct large-sample response databases (540 sets for ring 0 and 864 sets for the cutting edge ring). Genetic algorithm is introduced to optimize the hyperparameters of Random Forest, XGBoost, and Neural Network models, and their predictive performances are systematically compared. Results show that the proposed framework effectively substitutes traditional FE analysis and enables rapid multi-parameter comparison. Among the models, GA-XGBoost achieves the highest prediction accuracy across all stress indicators (R² > 0.999, where R² is the coefficient of determination, with values closer to 1 indicating better predictive performance), demonstrating the superiority of its gradient boosting and regularization mechanisms in handling tabular data with strong physical correlations. Moreover, the method exhibits good extensibility to other engineering response predictions beyond construction stresses. Full article

Forest structural complexity underpins habitat quality, microclimate buffering, and resilience, yet it remains poorly characterized across the Hindu Kush Himalaya (HKH) where field inventories and airborne LiDAR are difficult to scale across rugged terrain. Conservation planning and protected-area evaluation in the HKH therefore often rely on canopy height or cover proxies that do not directly represent vertical structural organization. Here we develop a repeatable, uncertainty-aware indicator of forest structural complexity from GEDI waveform LiDAR using the Waveform Structural Complexity Index (WSCI) and its prediction intervals. We first define a conservative analysis footprint (“trustable pixels”) by combining a woody-vegetation screen with minimum GEDI sampling support and canopy-stature plausibility, and by excluding the highest-uncertainty tail using a relative prediction-interval criterion. To separate complexity from canopy height, we model the HKH-wide expected WSCI–RH98 relationship and map height-normalized excess complexity (observed minus expected), identifying structural complexity hotspots and coldspots as the upper and lower tails of the excess distribution. Anomaly patterns are strongly organized along elevation and treeline-relevant belts and show coherent departures among ecoregions that persist after stratified adjustment for elevation and mean annual precipitation, indicating additional controls beyond broad environmental gradients. Protected areas exhibit systematically lower hotspot prevalence than surrounding landscapes, and within-elevation comparisons suggest this association is not explained by elevation alone, highlighting the need to interpret protected-area signals in the context of placement and land-use pressure. Overall, the anomaly atlas provides an operational indicator framework to stratify monitoring, prioritize field validation, and support the landscape-scale assessment of structural conditions beyond canopy height across one of the world’s most critical mountain forest systems. Full article

Quercus suber bark, known as cork, is an important fire-adaptive trait of this Mediterranean species. However, the increased frequency of wildfires and poor forest management practices can be significant challenges in managing the sustainable exploitation of cork oak stands. This study evaluates cork’s thermal behavior and organoleptic quality for commercial applications under three experimental fire scenarios: prescribed burn, low-intensity wildfire, and high-intensity wildfire. Bench-scale tests were conducted using a vertical mass loss calorimeter to simulate heat exposure levels, measuring temperature changes at four cork depths and quantifying heat-induced damage. Morphological traits—cork thickness, corkback thickness, and relative humidity—were recorded as predictor variables. Additionally, organoleptic and aromatic analyses were performed to assess the suitability of fire-exposed cork for wine stopper production. Results were consistent with the available literature, confirming that cork thickness significantly reduces the maximum temperature at the phellogen level. Specifically, mean cork thickness showed a significant negative effect on Tmax4 (β = −0.02, p < 0.001), indicating a consistent decrease in internal temperatures with increasing thickness across all heat flux levels. By contrast, cork consumption (mass loss) was primarily driven by heat flux intensity rather than cork structural traits. Aromatic profiling and organoleptic analysis revealed the presence of smoke-related compounds in cork samples exhibiting external carbonization. This effect was observed under higher heat flux exposure (particularly at 25 and 50 kW m⁻²), where visible charring occurred. Under these conditions, commercial quality may be partially compromised, whereas samples without external carbonization did not show comparable aromatic alteration. Further field validation is recommended. Full article

The impact of environmental disturbances and sensor deployment variations on damage identification represents a critical bottleneck that constrains the practical effectiveness of structural health monitoring. Existing methods addressing these challenges often suffer from poor interpretability due to information loss during feature extraction or exhibit insufficient sensitivity in identifying early-stage minor damage. This paper proposes a damage identification method based on the Shapelet Transform and Random Forest classifier, which extracts highly interpretable local shape features from vibration response signals to achieve robust identification of structural state changes. The study utilizes measured random vibration response data from a timber truss bridge. The dataset comprises four reference states collected on different dates and five damage states simulated by additional masses ranging from +23.5 g to +193.7 g, with sensors deployed in both vertical and horizontal directions. The Shapelet Transform selects local subsequences with high information gain from the original time series as features, which are subsequently classified using the Random Forest algorithm. The experimental design systematically investigates the influence of different damage severities, sensor locations, and environmental variations on method performance. The results demonstrate that with a Shapelet extraction time of 10 min, the method achieves 100% identification accuracy across multiple operating conditions comprehensively considering environmental variations, sensor location differences, and varying damage severities. When the extraction time is reduced to 5 min, 3 min, and 1 min, the average accuracies are 93.98%, 89.51%, and 58.48%, respectively. The method effectively identifies the minimum simulated damage (+23.5 g), which represents only 0.07% of the total structural mass, while maintaining stable performance under varying sensor locations and environmental conditions. Compared to traditional methods based on global frequency-domain features or statistical characteristics, the proposed method extracts physically meaningful local Shapelet features, offering significant advantages in interpretability. In contrast to deep learning approaches, this method demonstrates greater robustness under limited sample conditions. This study confirms that the combined framework of the Shapelet Transform and Random Forest can effectively address multiple real-world challenges in structural health monitoring, delivering high accuracy, strong robustness, and excellent interpretability, thereby providing a novel approach for developing practical real-time damage identification systems. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Understanding why cities diverge in motor vehicle ownership trajectories is critical for designing differentiated and sustainable transport policies. This study develops an integrated national–city analytical framework to examine heterogeneous urban motorization processes in China. A national Gompertz curve is first estimated to represent the benchmark income–ownership relationship. City-specific deviations are then decomposed into two interpretable dimensions: a horizontal stage parameter (h), capturing relative advancement or delay in motorization timing, and a vertical scaling parameter (s), reflecting persistent ownership intensity differences conditional on income. Results show substantial and multi-dimensional heterogeneity across cities. Stage timing (h) and ownership intensity (s) are only weakly correlated, indicating that earlier transition into higher motorization stages does not necessarily imply above-benchmark ownership intensity. Random forest models with time-forward validation demonstrate strong explanatory power (R² ≈ 0.88 for h and 0.80 for s). SHAP-based interpretation reveals that stage deviation is primarily associated with transport supply and urban structural characteristics, whereas ownership intensity deviation is more strongly linked to urban spatial scale and economic structure. Regulatory measures, including purchase and driving restrictions, exhibit comparatively smaller and heterogeneous effects. By disentangling timing and intensity dimensions of urban motorization, this study refines conventional income-based diffusion models and provides quantitative evidence that structural urban characteristics play a more fundamental role than regulatory interventions in shaping inter-city motorization differences. Full article

The vertical distribution of atmospheric aerosol layers plays a fundamental role in understanding their climatic and environmental effects. Using one year of lidar observations in Jinhua, together with ground-based meteorological measurements and ERA5 reanalysis data, this study develops an integrated analytical framework to investigate the structural characteristics of aerosol layers in Mie-scattering profiles and their meteorological driving factors. K-means clustering identifies three representative aerosol layer structure types: single-layer concave, single-layer convex, and multi-layer profiles. By combining the Boruta algorithm with a random forest model, the dominant meteorological factors associated with each structure type are quantified across four boundary-layer stages (00–06, 06–12, 12–18, 18–24 LT). Temperature, humidity, wind speed, wind direction, divergence, and vertical velocity exhibit distinct influences across different boundary-layer conditions, revealing differentiated regulatory mechanisms governing aerosol layer structure change. The proposed framework establishes a coupled perspective between atmospheric dynamic/thermodynamic processes and aerosol layer structure formation, providing a basis for refined modeling of aerosol evolution and improved understanding of aerosol–meteorology interactions. Full article

Secondary succession in tropical forests is traditionally described as a linear process driven by time since disturbance. However, growing evidence suggests that recovery pathways depend strongly on historical and environmental contexts. We evaluated how disturbance legacies and edaphic constraints interact to shape successional trajectories in a tropical karst landscape of the Maya Forest, Mexico. We sampled 100 plots along a chronosequence, quantifying vegetation structure, floristic diversity, biomass (NDVI), disturbance legacies, and soil properties. Using unsupervised clustering (K-means) and multivariate ordination, we identified four contrasting ecological typologies that represent distinct successional states rather than transient stages. Our results show a pronounced dichotomy in vegetation dynamics following the abandonment of land-use practices: while some sites are experiencing diverse development due to positive forest legacies (Typology B), others remain stalled (Typology C), dominated by lianas, where biotic barriers inhibit tree regeneration despite decades of abandonment. Additionally, we documented an asynchronous recovery between floristic recovery and vertical development; in sites with edaphic constraints, forests reach high diversity and biomass but exhibit stunted growth (Typology D). This suggests that severe abiotic constraints—specifically high rockiness and shallow soils—limit the dominance of highly competitive species, thereby acting as a filter that maintains high levels of diversity despite structural limitations. Edaphic analysis confirmed that chemical fertility and physical constraints (rockiness and shallow depth) act as orthogonal filters. This explains the persistence of structurally constrained yet functionally mature forests as stable, edaphically determined outcomes. Overall, secondary succession in tropical karst is nonlinear and path-dependent, governed by a hierarchical filtering model where historical land use dictates community identity and physical substrate limits structural architecture. These findings highlight the need for trajectory-specific management and the abandonment of uniform expectations of forest recovery in karst landscapes. Full article

To the authors’ knowledge, this is the first industrial machine learning (ML) study focused on wet vertical stirred media milling. The study develops and validates machine learning (ML) models to predict the key operating parameters, namely mill discharge product size, mill feed slurry flow rate, mill power draw, and the specific energy consumption of an industrial wet vertical stirred media mill operating at a copper plant. A physics-guided workflow was adapted, combining relief coefficient-based variable screening with fundamental stirred milling principles to define 20 different structured model input scenarios. In the scope, six regression approaches, linear regression (LR), fine tree regression (FTR), support vector regression (SVR), random forest regression (RFR), artificial neural network regression (ANN), and Gaussian process regression (GPR), were trained and validated using plant sensor data and evaluated using R² and RMSE. Overall performance was reasonable, with GPR providing the highest predictive accuracy, followed by RFR/ANN, while LR, SVR, and FTR performed lower. The potential benefit of feed size was also assessed conceptually through an upper-bound sensitivity analysis, representing a best-case scenario where an online feed size measurement would be available. Because the feed size descriptor (F₈₀) was not independently measured but derived from an energy–size relationship, the associated accuracy gains are reported as theoretical upper-bound indications rather than independent predictive capability. Overall, the findings support ML-based decision support in stirred milling operations and motivate future work using independently measured feed size (or reliable proxy sensing). Full article

Leaf nitrogen concentration (LNC) exhibits pronounced vertical heterogeneity across canopy layers, which affects the accuracy of nitrogen diagnosis derived from UAV-based remote sensing imagery. To address the differential contributions of leaf nitrogen from distinct canopy strata and the limitations associated with single-source features, this study proposes an integrated framework that combines cumulative LNC indicators across canopy layers with multi-source feature sets (vegetation indices and texture features). Centered on three core technical innovations—(1) incorporating canopy-layer aggregation logic into LNC modeling, (2) integrating spectral and structural information through CNN-based feature fusion, and (3) combining deep feature extraction with gradient boosting regression to improve robustness under multi-stage conditions—the framework systematically evaluates three machine learning algorithms: Random Forest (RF), a Convolutional Neural Network–Extreme Gradient Boosting hybrid model (CNN_XGBoost), and K-Nearest Neighbor (KNN) for cotton LNC estimation across multiple growth stages. The results demonstrate that cumulative canopy-layer nitrogen indicators more effectively represent overall plant nitrogen status than single-layer measurements. The integration of multi-source features further enhances model performance. Under both single-variable inputs and combined VI–TF feature sets, the CNN_XGBoost model consistently outperforms the other models in calibration accuracy and stability across all growth stages. Its optimal performance occurs during the cotton flowering and boll stage, achieving a calibration R² of 0.921. Overall, the proposed framework substantially improves the estimation accuracy of cotton LNC and provides both a theoretical foundation and technical support for precision nitrogen management and sustainable agricultural development. Full article

Fire suppression in the upper U.S. Midwest has led to the expansion of flammable coniferous ladder fuels, necessitating precise tracking of conifer species basal area (BA) for fire risk management. This study benchmarks four subset-selection pipelines—xPLS, GA-xPLS, RF-xPLS, and SVR-xPLS—to optimize the fusion of high-dimensional, collinear data from Sentinel-2, Landsat-9, and LiDAR sensors. Using 141 field plots in Minnesota’s Kawishiwi Ranger District of the Superior National Forest, we evaluated 175 predictors against eight BA response variables. Results show that RF-xPLS provided the superior accuracy–parsimony trade-off, achieving the highest pooled R² (≈0.86) and lowest error with a compact 27-predictor block. GA-xPLS ranked second, excelling for specific species such as Pinus resinosa. The most effective predictors combined SWIR-based moisture indices, red-edge/NIR structure, and a single LiDAR-derived surface of vertical-structure (quadratic mean height). Our findings demonstrate that integrating machine learning selection engines with multi-sensor fusion substantially enhances the scalability and precision of forest inventory and fuels monitoring. This comparative framework offers practical insights for sustainable management and fire risk mitigation in northern temperate–boreal forests. Full article

Forests play a vital role in influencing wind flow by modifying turbulence intensity and vertical wind shear. Because wind turbines are susceptible to these conditions, accurately characterising wind flow in forested environments is vital to ensuring structural reliability and realistic energy-yield assessments. In Latvia, where approximately 51.3% of the territory is covered by forests; the likelihood of wind turbine deployment in such areas is considerable. However, wind behaviour within and above forests is complex and strongly influenced by canopy effects, which in turn affect wake dynamics, structural fatigue, and power production. Advancing research in this field is therefore crucial for improving the accuracy of wind resource assessment and supporting evidence-based engineering solutions that enable the sustainable development of wind energy. Wind conditions were evaluated using NORA3 reanalysis data. Wake effects were simulated with the Jensen wake model to estimate annual energy production (AEP), which then informed levelised cost of energy (LCOE) calculations at various hub heights. The results indicate clear seasonal variability and show that increasing hub height leads to higher AEP and lower LCOE, owing to higher wind speeds and reduced turbulence. For forest heights of 0–25 m, the AEP loss increases from 7.8% (hub height = 199 m) to 22.9% (hub height = 114 m). Higher hub heights are also less sensitive to canopy-induced variability, reducing the impact of forest-related turbulence on energy production. Full article

Monitoring multi-temporal forest vertical structure in anthropogenically disturbed and topographically complex landscapes remains a major challenge, particularly when low-cost remote sensing technologies are used. This study aims to quantify forest vertical structure change and to determine whether these changes are systematically regulated by geomorphometric controls rather than occurring randomly. A multi-temporal unmanned aerial vehicle (UAV) photogrammetry workflow based on Structure from Motion (SfM) was applied to generate annual Canopy Height Models (CHMs) for 2023, 2024, and 2025. To ensure temporal robustness, the 95th percentile of canopy height (P₉₅) was adopted as the primary structural metric, and vertical change was quantified using a difference-based indicator (ΔP₉₅). Random Forest (RF) regression was used to model the relationship between canopy height change and terrain-derived predictors, including slope, aspect, and Topographic Wetness Index (TWI). The results reveal a consistent vertical growth signal across the study area, with a mean ΔP₉₅ increase of 0.65 m over the monitoring period, clearly exceeding the photogrammetric vertical error (RMSE = 0.082 m). Positive canopy height changes are concentrated on moisture-favored, moderately sloping and north-facing terrain, whereas negative changes (down to −1.20 m) are mainly associated with mining-disturbed and steep surfaces. The RF model achieved high explanatory performance (training R² = 0.919) and identified aspect (20%), slope (18%), and TWI (18%) as the dominant controls on forest vertical dynamics. These findings demonstrate that forest vertical structure evolution in disturbed landscapes is not stochastic but is systematically governed by terrain-driven hydro-morphological and microclimatic conditions. The main contribution of this study is the development of an interpretable, change-focused UAV–machine learning framework that moves beyond single-epoch canopy height estimation and enables process-oriented analysis of terrain–vegetation interactions. The proposed approach provides a cost-effective and transferable tool for forest monitoring and post-mining restoration planning in complex terrain settings. Full article

The Qinghai–Xizang Plateau, known as the “Asian Water Tower”, hosts numerous lakes that are highly sensitive to climate change. Ciliates, key microbial eukaryotes in aquatic ecosystems, play crucial roles in biogeochemical cycling and food web dynamics. However, their community assembly mechanisms in such extreme habitats remain poorly understood. In July 2020, we investigated the ciliate community in Basomtso Lake. A total of 15 sampling sites were established along the horizontal gradient, and 11 vertical depth samples were collected at a central site (B15), resulting in 75 water samples for eDNA analysis. Using 18S rRNA gene high-throughput sequencing, we identified 610 ciliate amplicon sequence variants (ASVs), with the class Spirotrichea being the dominant taxonomic group. Distance–decay relationships indicated a significantly stronger community turnover rate along the vertical gradient compared to the horizontal gradient. Analyses using the neutral community model and null model revealed that community assembly was primarily stochastic. However, increasing vertical environmental heterogeneity enhanced the role of deterministic, niche-based selection. Random forest modeling identified resistivity (RES) and water temperature (WT) as the key predictors for horizontal and vertical community variation, respectively. Furthermore, Threshold Indicator Taxa Analysis (TITAN) detected specific taxa exhibiting pronounced sensitivity to gradients in RES and WT. Our findings demonstrate that horizontal community structure is governed primarily by dispersal limitation, whereas vertical zonation is shaped by environmental filtering driven primarily by RES and WT gradients under extreme plateau conditions. This study provides new insights into the mechanisms sustaining microbial diversity and ecosystem resilience in climatically vulnerable high-altitude lakes. Full article