Search Results (406)

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

This study shows that the structural features of humic acids reflect the specific characteristics of organic matter in permafrost soils of the southern Vitim Plateau. The region’s extracontinental climate determines the rate of decomposition, the depth of humification, and the chemical structure of humic acids. Brown forest soils (Haplic Cambisols) and sod-brownzems (Leptic Cambisols Skeletic) contain high amounts of organic carbon and total nitrogen in their upper horizons but differ in their vertical distribution. Brown forest soils are characterized by a sharp decrease in organic carbon content with depth and the presence of humus pockets enriched in carbon and exchangeable bases. Sod-brownzems contain more organic carbon with increase in acidity and base loss with depth. Both soil types retain satisfactory natural fertility. ¹³C nuclear magnetic resonance spectroscopy data reveal marked differences in the structural maturity of humic acids. Humic acids from the A horizons of brown forest soils contain an equilibrium combination of aliphatic and aromatic structures, a well-developed system of oxygen-containing groups, and moderate condensation, indicating an intermediate stage of humification. Humic acids from humus pockets are more aromatic and highly humified. They reflect an advanced stage of humification and possess high chemical stability. Humic acids from sod-brownzems also exhibit high aromaticity, which facilitates the formation of stable organomineral complexes. A comparison of the samples reveals a consistent increase in aromaticity, condensation, and stability from the A horizons of brown forest soils to the A horizons of sod-brownzems and further to humus pockets. This progression corresponds to an increase in humification and a decrease in the mobility and bioavailability of organic matter. These results confirm that the structural characteristics of humic acids are determined by soil type and formation conditions. Elemental composition revealed that humic acids from brown forest soils are characterized by the highest aromaticity and maturity, while humic acids from HA-brown forest soils-A have a less condensed structure. Humic acids from sod-brownzems occupy an intermediate position, combining high aromatization with a moderate degree of humification. Overall, the obtained elemental composition data are fully consistent with the results of ¹³C NMR spectroscopy, mutually confirming the identified structural features and the degree of transformation of soil organic matter. Full article

Mosquitoes (Diptera: Culicidae) exhibit vertical stratification patterns in forest environments, a fundamental ecological aspect for understanding niche occupation patterns, host-seeking behavior, and consequently arbovirus transmission mechanisms. Despite the relevance of this topic, available studies mostly focus on genera such as Aedes, Haemagogus, and Sabethes which are traditionally associated with arbovirus transmission. There are still important gaps regarding stratification and seasonality in the Mansoniini tribe, whose biology and epidemiological role remain underexplored, especially in highly biodiverse ecosystems such as the Atlantic Forest. This study evaluated the influence of seasonality and vertical stratification on the mosquito community, with a detailed focus on the Mansoniini tribe, in an Atlantic Forest fragment in Brazil, between May 2023 and December 2024. Captures were performed monthly using CDC light traps positioned at 1.5 m and 10 m heights, and specimens were morphologically identified. A total of 880 mosquitoes from nine genera and 24 species were captured, of which 91 (10.3%) belonged to the Mansoniini tribe. The most abundant species were Coquillettidia fasciolata and Mansonia titillans, recorded in both strata. Our results indicate no marked vertical segregation for the studied mosquito community in this specific location, but a strong influence of seasonality, particularly for the Mansoniini tribe, reinforcing the role of meteorological data on the population structure of these species. These site-specific findings offer a foundational ecological portrait and a robust methodological template for a neglected taxon. They generate critical, testable hypotheses about niche partitioning in fragmented forests and underscore the necessity for broader spatial replication to disentangle the relative influence of seasonal versus vertical drivers in similar ecosystems. Full article

Passive protection is widely assumed to preserve biodiversity and ecological integrity, yet the evidence for long-term vegetation stability in protected temperate forests remains inconclusive. We resurveyed two deciduous forests in SW Poland after 30 years of strict protection to assess temporal changes in their understory vegetation, functional structure, and habitat conditions. Using paired phytosociological relevés (n = 40), collected using the Braun-Blanquet method, we compared baseline (1989–1991) and recent (2022) data with respect to species frequency, Ellenberg indicator values, basic functional traits, and functional diversity. Species composition proved highly stable: only 10% of vascular plant species exhibited significant changes in frequency in particular layers, largely reflecting the vertical redistribution of woody species rather than species turnover. Habitat conditions showed no significant temporal changes. In contrast, the functional structure of the herb layer changed markedly, with significant increases in community-weighted means of seed mass, plant height, and specific leaf area, accompanied by a significant rise in functional diversity. These shifts were partly driven by the increasing abundance of woody species and some opportunistic and invasive species. Our results demonstrate that functional traits may reveal directional ecological changes in passively protected forests even when species composition and habitat indicators remain unchanged, highlighting the importance of trait-based approaches for long-term forest surveys. Full article

Tropical Andean forests are biodiversity hotspots that have been transformed by anthropogenic activities, making ecosystem regeneration and restoration essential for their recovery. This study evaluated floristic composition, forest structure, and diversity in three land cover types within tropical Andean ecosystems: riparian forest (RF), natural regeneration (NR), and ecological restoration areas (RE). Vegetation was inventoried using standardized plots, recording species composition, diameter, and height. Basal area, size class distribution, and vertical structure were estimated. The Shannon Wiener and Simpson indices were evaluated. RF showed the highest structural complexity and basal area among the evaluated cover types, followed by ER, whereas NR showed the lowest values. NR showed the highest diversity values and a predominance of individuals in lower diameter and height classes, reflecting active recruitment and intermediate successional stages. Segment ER exhibited lower diversity and intermediate structural development, consistent with shorter recovery periods and limitations in restoration design. Overall, the integration of floristic, structural, and diversity attributes indicates distinct successional trajectories, conditioned by land-use history, disturbance intensity, and environmental heterogeneity. These findings highlight the great potential for natural regeneration under reduced anthropogenic pressure and emphasize the need to integrate passive and active restoration strategies to enhance biodiversity and resilience in Andean tropical forests. Full article

Accurate Digital Terrain Models (DTMs) are essential for managing forest drainage networks as a crucial element of water management, yet dense canopies and complex micro-topography challenge Airborne Laser Scanning (ALS) precision. This study evaluates the vertical accuracy of an ALS-derived DTM specifically within forest drainage ditches, utilizing 706 GNSS and total station measurements for validation. The results indicate a positive elevation bias, with a mean elevation error of 0.415 m and an RMSE of 0.464 m, 54.7% higher than the 0.3 m declared in the DTM technical report. Forest height, acting as a proxy for forest structural density and canopy closure, was significantly associated with a reduction in ground reflection density and an increase in the distance to the nearest ground reflection (p < 0.05). Mixed-effects ANOVA confirmed that there are significantly more ground reflections in low vegetation (0–1 m). Crucially, multiple regression analysis revealed that forest height was not the primary driver of elevation error; instead, ditch geometry was the most significant predictor. Narrower ditches exhibited substantially higher errors than wider ones, regardless of the canopy height. Furthermore, while ground reflection density decreased in mature stands, this reduction did not significantly diminish DTM vertical accuracy, suggesting that some of the LiDAR reflections of low vegetation could be misclassified as ground reflections, decreasing accuracy. These findings suggest that while ALS is effective for general forest topography and mapping drainage infrastructure, its application may require corrections for ditch dimensions rather than vegetation height alone to mitigate systematic overestimation of ditch bed elevations. Full article

Accurate estimation of Total Phosphorus, referred to as “Phosphorus, Total” (PPUT; µg/L) in the sourced monitoring data, is essential for understanding eutrophication dynamics and guiding water-quality management in inland lakes. However, lake-wide PPUT mapping at high resolution is challenging to achieve using conventional in-situ sampling, and nearshore gradients are often poorly resolved by medium- or low-resolution satellite sensors. This study exploits multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance, short-term meteorological descriptors (3 to 7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure), and in-situ Secchi depth (SSD) were used to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes that progressively extend from bands-only, to combinations with spectral indices and day-of-year (DOY), and finally to SSD-inclusive full-feature configurations. The inclusion of SSD led to a strong and systematic performance gain, with mean R² increasing from about 0.67 (SSD-free) to 0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on PPUT retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, a knowledge-distillation strategy was implemented in which an SSD-aware teacher transfers its learned representation to a student using only satellite and meteorological inputs. The optimal student model, based on a compact subset of 40 predictors, achieved R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L, retaining approximately 88% of the teacher’s explanatory power. Application of the student model to PlanetScope scenes from 2020 to 2025 produces meter-scale PPUT maps; a 26 July 2024 case study shows that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but coherent hotspots above 20 µg/L align with tributary mouths and narrow channels. The results demonstrate that combining commercial high-resolution imagery with physics-informed feature engineering and knowledge transfer enables scalable and operationally relevant monitoring of lake phosphorus dynamics. These high-resolution PPUT maps enable lake managers to identify nearshore nutrient hotspots, tributary plume structures. In doing so, the proposed framework supports targeted field sampling, early warning for eutrophication events, and more robust, lake-wide nutrient budgeting. Full article

Mountain treelines are among the most climate-sensitive ecosystems on Earth, yet their fine-scale structural and species level dynamics remain poorly resolved in the Himalayas. In particular, the absence of three-dimensional, crown level measurements have hindered the detection of structural thresholds and species turnover that often precede treeline shifts. To bridge this gap, we introduce UAV LiDAR—applied for the first time in the Hindu Kush Himalayas—to quantify canopy structure and tree species distributions across a steep treeline ecotone in the Manang Valley of central Nepal. High-density UAV-LiDAR data acquired over elevations of 3504–4119 m was used to quantify elevation-dependent changes in canopy stature and cover from a canopy height model derived from the 3D point cloud, while individual tree segmentation and species classification were performed directly on the 3D, height-normalized point cloud at the crown level. Individual trees were delineated using a watershed-based segmentation algorithm while tree species were classified using a random forest model trained on LiDAR-derived structural and intensity metrics, supported by field-validated reference data. Results reveal a sharply defined treeline characterized by an abrupt collapse in canopy height and cover within a narrow ~60–80 m vertical interval. Treeline “threshold” was quantified as a breakpoint elevation from a piecewise model of tree cover versus elevation, and the elevation span over which modeled cover and height distributions rapidly declined from forest values to near-zero. Segmented regression identified a distinct structural breakpoint near 3995 m elevation. Crown-level species predictions aggregated by elevation quantified an ordered turnover in dominance, with Pinus wallichiana most frequent at lower elevations, Abies spectabilis peaking mid-slope, and Betula utilis concentrated near the upper treeline. Species classification achieved high overall accuracy (>85%), although performance varied among taxa, with broadleaf Betula more difficult to discriminate than conifers. These findings underscore UAV LiDAR’s value for resolving sharp ecological thresholds, identifying elevation-driven simplification in forest structure, and bridging observation gaps in remote, rugged mountain ecosystems. Full article

Coconut coir, a key substrate in soilless cultivation, presents challenges for accurate moisture detection because of its complex internal structure, which limits the understanding of water infiltration and redistribution. This study employed RGB image recognition techniques combined with machine learning algorithms to systematically investigate the effects of initial moisture content (10%, 20%, and 30%), coarse-to-fine coir volume ratio (1:0, 1:1, and 0:1), and emitter discharge rate (1.0, 1.5, and 2.0 L h⁻¹) on wetting front morphology, water transport dynamics, and moisture variation within coir substrates. Morphological features of the wetting front were extracted from images and incorporated into three machine learning models—Support Vector Regression (SVR), Random Forest (RF), and Polynomial Regression—to construct a predictive framework for coir moisture estimation. The results showed that the SVR model achieved the best predictive performance in coarse coir substrates (R² = 0.89, RMSE = 3.37%), whereas Polynomial Regression performed best in mixed substrates (R² = 0.861, RMSE = 4.34%). All models exhibited lower accuracy in fine coir, particularly at high moisture levels. Under the same irrigation volume, increasing the initial moisture content enhanced both the water transport rate and the wetting front extent, with the aspect ratio (AR) decreasing from approximately 2.0 to 1.3, indicating a morphological transition of the wetting front from a “thumb-shaped” to a “hemispherical” pattern. Coarse particles facilitated vertical infiltration, while fine particles exhibited stronger water retention. By integrating RGB image recognition with machine learning approaches, this study achieved reliable prediction of coir moisture content and proposed an optimal management strategy using mixed substrates with an initial moisture content of 20–30% to balance infiltration efficiency and water-holding capacity while minimizing percolation risk. These findings provide a robust technical pathway for precise water management in coir-based cultivation systems. Full article

Severe erosion persists in the red soil region of southern China despite dense vegetation. Stratified vegetation volume (SVV), which integrates horizontal and vertical vegetation density, better captures understory structure than fractional cover. Here, we established and surveyed 75 forest stands (10 m × 10 m) spanning an erosion-intensity gradient in Changting County, Fujian Province, China. Within each stand, soil was sampled by depth (0–20 cm), and living and dead vegetation volumes in the canopy, shrub–herb, and litter layers were quantified to derive SVV. Relative to slightly eroded soils, moderate and severe erosion reduced the soil water content by 38–41% and soil organic matter by 19–34%, while increasing bulk density by 25–30% and pH by 6–8%. Severe erosion increased the sand content by 20–31% and decreased the gravel content by ≤15%. SVV declined sharply with erosion, with the largest loss in the shrub–herb layer (66–97%). Erosion was most strongly associated with shrub–herb SVV, soil water content, organic matter, and bulk density (r = 0.5–0.6, p < 0.05). The shrub–herb layer was the key component resisting surface erosion. Overall, understory degradation accelerates erosion and soil coarsening, reinforcing a constrained vegetation–soil system; restoring native shrubs and grasses, coupled with targeted canopy thinning, may improve soil and water conservation. Full article