Search Results (245)

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments. Full article

Degraded rangelands in semi-arid pastoral systems are widely associated with declining vegetation, soil carbon loss, and worsening household livelihoods. However, the mechanisms linking rangeland degradation to household income remain poorly understood, particularly in a panel-data context. This study examines how rangeland condition, vegetation dynamics, and livestock by-product underutilization are related to household income in Öndörshireet Soum, Töv Aimag, Mongolia. The analysis is based on a multi-source panel dataset covering 2018 to 2024, combining Sentinel-2 NDVI time series, soil organic carbon measurements from 120 permanent plots, and a five-wave survey of 114 households. The results indicate widespread and persistent degradation. Nearly 90 percent of monitored plots are at least moderately degraded; NDVI shows a steady decline over time; and average soil carbon levels remain well below those observed at a managed reference site. Over the same period, real household income declined despite a gradual increase in herd size. Econometric estimates show that vegetation condition is positively associated with income, whereas higher levels of by-product waste are associated with lower income, even after accounting for precipitation variability. The interaction results further suggest that the benefits of herd expansion weaken when production losses remains high. Taken together, these findings indicate that ecological decline and low value capture from livestock operate simultaneously to constrain pastoral livelihoods. Improvements in pasture condition alone appear insufficient to offset these pressures when a substantial share of livestock value is not recovered. While the results offer useful insights for rangeland policy, further evidence from multiple sites would be needed to assess causality and the extent to which these patterns apply beyond a single soum. Full article

In structurally complex subtropical evergreen broad-leaved forests with dense understories, conventional remote sensing approaches are often limited by spectral saturation and insufficient structural characterization. This study developed a multi-source data fusion framework integrating airborne laser scanning (ALS), terrestrial laser scanning (TLS), and hyperspectral imagery (HSI), using ground truth data from 34 permanent plots in southern China subtropical evergreen broad-leaved forests. Six key structural parameters from ALS/TLS and six spectral indices from HSI were integrated as input features for adaptive fuzzy C-means clustering to estimate tree species diversity. Variance decomposition was conducted to quantify the independent and interactive contributions of ALS- and TLS-derived parameters. The results showed that: (1) ALS-based multi-scale watershed segmentation achieved high individual-tree segmentation accuracy (R² = 0.873); (2) ALS-derived structural parameters exhibited significant correlations with plot-level species diversity (R² = 0.385–0.824); (3) inter-crown standard deviations of six vegetation indices showed consistent associations with species diversity (R² = 0.361–0.479), capturing interspecific spectral and functional variation; (4) combined ALS, HSI, and TLS predictors explained approximately 83% of diversity variation, with TLS contributing minimal unique information beyond ALS; (5) adaptive fuzzy C-means clustering estimated Shannon–Wiener indices with high accuracy (R² = 0.725), though plot-level aggregated metrics outperformed individual-tree aggregates; (6) TLS inclusion reduced estimation accuracy (R² = 0.653), likely due to understory liana interference, while silhouette analysis confirmed that clustering stability remained unchanged. These findings demonstrate that ALS–HSI fusion enables robust regional-scale tree species diversity estimation, while TLS may introduce confounding structural signals rather than complementary information in dense understory conditions. Full article

This study quantifies how different land-use types influence surface soil characteristics (0–5 cm) and the dynamics of soil organic carbon (SOC) and nitrogen in the mountainous ecosystems of the Rila Mountains. Across 54 forest and agricultural plots, pH, bulk density, coarse fraction, C:N ratio, SOC, total nitrogen (TN), and their respective stocks were assessed using standard analytical methods and statistical tests (Shapiro–Wilk, ANOVA, Kruskal–Wallis, correlation and regression analysis). Land use significantly affected all soil parameters except pH. Forest soil showed lower bulk density and lower SOC stocks compared with grasslands. Unmown meadows exhibited the highest SOC and TN concentrations and stocks, while potato fields recorded the highest bulk density and elevated TN stocks, reflecting intensive management impacts on surface soil properties. Forest soils displayed species-specific patterns, with Scots pine and Silver fir showing comparatively lower SOC and TN stocks attributable to historical degradation and site limitations. As the study focused on the uppermost soil layer (0–5 cm), the results should be interpreted more as indicators of surface soil dynamics rather than as estimates of total topsoil carbon and nutrient storage. Correlation analysis revealed strong positive relationships among SOC, TN, and the C:N ratio, and strong negative relationships between SOC and both bulk density and coarse fraction in managed agricultural lands. The findings demonstrate that minimizing soil disturbance and maintaining permanent vegetation cover—particularly through conservation of unmanaged grasslands—offer great capacity for enhancing the soil organic matter accumulation in mountainous ecosystems. Full article

The study aims to analyze comprehensive effects of site quality class (SQC), stand density index (SDI), and species composition (SC) on biomass growth. Based on 5872 observations from 2040 permanent sample plots of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) plantations from successive national forest resource inventories, five classical growth equations were employed and nonlinear regression and dummy variables were used for modeling. A dominant height (DH) growth model was first developed to determine SQC, followed by a series of stand biomass (SB) growth models incorporating SQC, SDI, and SC (pure vs. mixed stands). Growth differences among different classes or categories were analyzed using inflection age and optimal rotation age. The results show that Korf equation performed best for both DH and SB growth models; SDI contributed the most to SB growth, followed by SQC, with their interaction accounting for over half of the total contribution. Mixed stands grew faster than pure stands; higher SQC was associated with faster growth and earlier attainment of inflection age and optimal rotation age. The productivity increased with rising SDI, but the rate of increase gradually diminished. Different optimal rotation ages should be determined for pure and mixed stands across different SQCs. Reasonable adjustment of harvesting age and control of stand density represent the greatest potential for improving forest productivity. Full article

Wooden, nature-based barrier structures are widely implemented after wildfire in Mediterranean forests to reduce runoff connectivity and trap sediment, yet their ecological footprint on early plant recovery remains poorly quantified in Greece. We assessed two-year vascular plant recovery in forest landscapes burned during the 2021 wildfire season (Parnitha, Attica; Mavrolimni, Corinthia/Peloponnese) using repeated field surveys in 2022 and 2023. Sixteen permanent plots were established within operational rehabilitation works and assigned to the dominant structure types: wattles (brush/branch piles), contour-oriented hillslope log barriers, and channel log dams. In each year, vascular plant composition and recovery endpoints (species richness and diversity indices, density, cover, and aboveground biomass) were quantified using standardized quadrat sampling. Vegetation cover and biomass increased strongly from 2022 to 2023 at both sites, indicating rapid early reassembly. Against this dominant year effect, structure type was associated with pronounced biodiversity and compositional differences, most clearly in Parnitha where log barriers exhibited markedly reduced diversity in 2022 and community turnover patterns differed among structures. Plot-level PERMANOVA on Bray–Curtis dissimilarities calculated from log(x + 1)-transformed abundances did not detect a statistically significant structure type effect in either year (p > 0.05), whereas descriptive Bray–Curtis heatmaps suggested compositional contrasts among structure type × year combinations. Indicator–species analysis further identified a limited set of taxa associated with specific structures, suggesting provisional structure-linked microsite filtering during early assembly. By quantifying community composition and indicator taxa alongside structural recovery, this study provides operational-scale evidence that common wooden post-fire measures may be associated with early biodiversity signals in the first two years after fire, although these patterns should be regarded as provisional given the short monitoring period and limited replication. Incorporating these signals into post-fire land management can improve intervention design and placement, aligning risk reduction with biodiversity recovery in Mediterranean landscapes. Full article

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

A multitemporal floristic study was conducted on the aquatic and riparian plant communities of the lower stretch of the Tiber River (central Italy) to identify any floristic changes in response to possible environmental pressures that have occurred locally over time. This investigation was carried out by comparing α- and temporal β-diversity, as well as biological, chorological, and ecological traits of plant assemblages present in permanent plots (n = 24) and sampled at two different time points (2005, 2025). Although both aquatic and riparian plant communities showed an increase in α-diversity over time (+94.1% and +56.5%, respectively), they generally exhibited different temporal patterns. The aquatic community showed a more stable floristic structure compared to the riparian one, with a persistent dominance of eutrophic and pollution-tolerant species, although local disappearance/rarefaction of some species was recorded. On the contrary, the riparian community showed greater species turnover, mainly due to an increase in generalist, ruderal and alien species, which over time have partially replaced those typically associated with riparian habitats. Ecological trait-based analyses indicated an increase over time in the percentage of thermophilous, heliophilous and nitrophilous species in both plant communities; the riparian community also showed an increase in xerophilous ones. Overall, the results indicate that aquatic and riparian communities exhibit distinct temporal dynamics within the same river system and highlight how long-term, permanent plot-based floristic monitoring is a useful tool in environmental studies. Full article

The global push to advance smart and digital forestry relies on emerging technologies to support efficient, AI-assisted, and data-driven forest management, but many forest operations occur in remote forests where reliable internet connectivity is unavailable. Low Earth Orbit (LEO) satellite constellations such as Starlink may provide reliable connectivity where cellular networks are unavailable. The performance of LEO-based solutions remains poorly understood under forest canopies, and empirical evaluations linking canopy characteristics to connectivity performance are largely lacking. In this study, the effect of forest vegetation on Starlink performance below the canopy was evaluated by placing a satellite receiver at thirty randomly selected permanent single tree inventory plots on the University of Idaho Experimental Forest and measuring connection success, connection time, and upload and download speeds along 50 m transects in all cardinal directions. LiDAR-derived stand density index (SDI), leaf area index (LAI), rumple index (RI), and vegetation cover (VC) were used to quantify canopy structure. Principal Component Analysis and survival analysis showed that higher values of PC1, primarily driven by SDI, LAI, and RI, reduced the probability of establishing a connection. Linear regression analysis indicated that higher SDI increased connection time, indicating that denser stands slowed or prevented connectivity. Linear mixed-effects models demonstrated that internet speed primarily declined with increasing distance, with download and upload rates dropping beyond 40 m from the router. LAI, RI, and VC did not influence connection time or speed, suggesting that overall stand density rather than leaf area per unit ground area has a greater impact on signal obstruction. Overall, dense forest structure and distance are the main constraints on LEO satellite connectivity and performance, and understanding these limitations supports the development and deployment of satellite-based networking to advance smart forestry operations. These results provide one of the first quantitative assessments of LEO satellite connectivity constraints in operational forest conditions, offering practical guidance for deploying satellite-based networks to support smart forestry applications in remote environments. Full article

Priority habitat 91E0* (alluvial forests with Alnus glutinosa and Fraxinus excelsior) constitutes a key riparian biodiversity hotspot, yet it is increasingly threatened by woody invasions that alter the community composition and reduce the habitat’s heterogeneity. Ten permanent plots (15 m radius) were surveyed in the Nestos River delta (NE Greece) in 2019 and 2023, following a manual control campaign conducted in 2021, targeting Amorpha fruticosa and Acer negundo. Because systematic plot-level vegetation data were collected only in 2019 and 2023, the study evaluates before–after changes rather than continuous annual dynamics. Woody species composition and diversity, community turnover (Bray–Curtis dissimilarites/PCoA; PERMANOVA), invasive dynamics (negative binomial GLMs), and community-weighted Ellenberg-type indicator values and their relationships with the soil properties (0–30 cm) were assessed. Across the surveys, 18 woody taxa were recorded, dominated by native riparian trees and shrubs, together with four established alien species. The total alien abundance declined from 943 to 385 individuals between 2019 and 2023, driven by A. negundo (−68%) and A. fruticosa (−39%). The woody community composition differed significantly between years (R² = 0.12; p = 0.013) and river banks, whereas plot-scale diversity indices changed modestly and evenness increased. The mean community-weighted moisture affinity increased (CWM_F: 6.28 → 7.07), nutrient affinity remained high, and reaction values declined slightly. The soil’s properties did not differ between the treated and control plots; nevertheless, Shannon diversity was positively correlated with organic C, total N, exchangeable Ca and K, and clay content. Permanent plot resurveys thatintegrate soil properties and indicator-based community metrics provide robust baselines to support Article 17 reporting under the EU Habitats Directive and to guide spatially targeted invasive-species management in Mediterranean alluvial forests (habitat 91E0) undergoing restoration actions. Full article

The grey water footprint (GWF) is a critical indicator for assessing the impact of socio-economic activities on the water resources environment. To address the dual challenges of economic growth and water pollution associated with Land Consolidation Projects (LCPs) in the Loess Plateau, this study systematically analyzes the spatiotemporal distribution of GWF in the Yan’an region from 2000 to 2023 and employs the eXtreme Gradient Boosting (XGBoost) model to comprehensively explore its driving mechanisms. The SHapley Additive Explanations (SHAP) method was employed to quantify the dynamic contributions of the driving factors of GWF, while the threshold effects of these factors were assessed using partial dependence plot analysis. Additionally, spatial matching patterns between agricultural GWF (GWF_agr) and economic factors were examined using the Gini coefficient and imbalance index. These findings indicate that the total GWF (TGWF) peaked at 1.347 billion m³ in 2004 and declined due to improvements in water management efficiency. Spatially, TGWF is higher in the central and eastern regions, where GWF_agr is predominant. The permanent population and per capita GDP are the key driving factors, accounting for 21.1% and 15% of the total change in TGWF, respectively. In the spatial coupling relationship between agricultural GDP and GWF_agr, the overall imbalance index has significantly decreased. The synergistic effect between the Grain for Green Project and LCPs is becoming increasingly evident. These insights provide scientific support and policy guidance for the ecological protection and high-quality development of the Yellow River Basin. Full article

This article describes a field- and laboratory-based framework that can be used to monitor the C balance in Mediterranean pine forest ecosystems under different management practices that determine their structure and function. By jointly monitoring stand structure, gas exchange, litter, and decomposition dynamics, this protocol enables the assessment of how management-driven changes regulate carbon uptake, turnover, and losses, thereby affecting carbon sequestration potential. As an example, we suggest the implementation of the protocol at ten (10) permanent monitoring plots across three study areas located in Greece. The first group of plots represents a post-fire chronosequence in pine stands with no management interventions. The second group includes pine stands that exhibit variation in overstory and understory density driven by differences in microclimate and management history. The third group consists of peri-urban pine stands subjected to thinning of varying intensity. The monitoring protocol is implemented across all plots and the collected data can be classified into three analytical domains: (a) demography, encompassing measurements of tree growth and mortality; (b) litter and decomposition dynamics, involving the quantification of litterfall and its seasonality and the estimation of its decomposition rates; and (c) gas exchange, focusing on measurements of leaf photosynthesis and respiration (including relevant leaf functional traits) and monitoring of soil respiration. These three data domains can be used to comparatively consider the effect of forest management on key ecosystem processes and to constrain local-scale vegetation dynamics models. Full article

This investigation examines diffusion processes for predicting whole-stand volume, incorporating the variability and uncertainty inherent in regional, operational, and environmental factors. The distribution and spatial organization of trees within a specified forest region, alongside dynamic fluctuations and intricate uncertainties, are modeled by a set of nonsymmetric stochastic differential equations of a sigmoidal nature. The study introduces a three-dimensional system of stochastic differential equations (SDEs) with mixed-effect parameters, designed to quantify the dynamics of the three-dimensional distribution of tree-size components—namely diameter (diameter at breast height), potentially occupied area, and height—with respect to the age of a tree. This research significantly contributes by translating the analysis of tree size variables, specifically height, occupied area, and diameter, into stochastic processes. This transformation facilitates the representation of stand volume changes over time. Crucially, the estimation of model parameters is based exclusively on measurements of tree diameter, occupied area, and height, avoiding the need for direct tree volume assessments. The newly developed model has proven capable of accurately predicting, tracking, and elucidating the dynamics of stand volume yield and growth as trees mature. An empirical dataset composed of mixed-species, uneven-aged permanent experimental plots in Lithuania serves to substantiate the theoretical findings. According to the dataset under examination, the model-based estimates of stand volume per hectare in this region exhibited satisfactory goodness-of-fit statistics. Specifically, the root mean square error (and corresponding relative root mean square error) for the living trees of mixed, pine, spruce, and birch tree species were 68.814 m³ (20.4%), 20.778 m³ (7.8%), 32.776 m³ (37.3%), and 4.825 m³ (26.3%), respectively. The model is executed within Maple, a symbolic algebra system. Full article

Background and Objectives: The aim of this study is to evaluate the effect of positive and negative visual aids used during verbal–active oral hygiene education on oral hygiene-related behaviors in children aged 7 to 14 years. Materials and Methods: In this single-blind design, sixty children were randomly assigned to three groups: G1:Positive visual aid, G2:Negative visual aid, and G3:Verbal–active education. Oral hygiene was evaluated using the Silness–Löe Index (plaque) and Rosenberg Organoleptic Scale (halitosis) at baseline, one week, and one month. Measurements were taken at baseline, at the end of the first week, and at the end of the first month. Data were analyzed using split-plot ANOVA. Results: The test groups did not show any statistically significant differences in terms of age (F = 0.530, p = 0.449) or gender (χ² = 1.600, p = 0.449). Additionally, the groups were similar in terms of clinical variables, including dentition stage (permanent or mixed) (χ² = 5.566, p = 0.062), presence of malocclusion (χ² = 3.801, p = 0.150), and presence of anterior dental caries (χ² = 1.250, p = 0.535). Significant reductions in both plaque and halitosis scores were observed over time in all groups (p < 0.001), and there were no statistically significant differences between the types of intervention (p > 0.05). Conclusions: This study demonstrated that both verbal education aided by positive and negative visuals and structured-only verbal education improved children’s oral hygiene and halitosis scores in the short term. Full article