Search Results (336)

The study evaluated the effects of nitrogen fertilization on the morphogenetic, structural, productive, and nutritional characteristics of Brachiaria brizantha cv. Paiaguás subjected to two stockpiling periods in a pot experiment. The experiment was conducted using a randomized block design in a 4 × 2 factorial arrangement, with four nitrogen doses (0, 25, 50, and 75 mg N dm⁻³, applied as urea) and two stockpiling periods (80 and 120 days). Increasing nitrogen doses promoted linear increases in leaf appearance, elongation, and senescence rates, as well as tiller population density, while reducing phyllochron and leaf lifespan. Forage mass increased linearly with nitrogen, ranging from 96.25 to 113.00 g of dry matter per pot, and leaf blade mass showed a similar response. Root mass exhibited a quadratic response, with a maximum estimated value of 49.33 g pot⁻¹ at 60.18 mg N dm⁻³, this quadratic equation explained 96% of the variation in the results. No significant interaction was observed between nitrogen doses and stockpiling periods for dry matter, crude protein, mineral matter, or neutral detergent fiber contents. However, nitrogen fertilization increased crude protein content across plant fractions, with leaf crude protein rising from about 70 to over 110 g kg⁻¹ dry matter. Nitrogen fertilization at 75 mg N dm⁻³ combined with an 80-day stockpiling period improves canopy structure, forage production, and nutritional quality of Paiaguás grass, highlighting the importance of synchronizing nitrogen supply with deferment duration in deferred pasture management. Full article

Accurate quantification of forest aboveground biomass (AGB) is essential for monitoring terrestrial carbon stocks. While total AGB estimation is widely practiced, resolving component biomass such as canopy, branches, leaves, and trunks enhances the precision of carbon sink assessments and provides critical structural parameters for ecosystem modeling. Most studies rely on a single SAR sensor or a limited range of SAR features, which restricts their ability to represent vegetation structural complexity and reduces biomass estimation accuracy. Here, we propose a phased fusion strategy that integrates backscatter intensity, interferometric coherence, texture measures, and polarimetric decomposition parameters derived from dual-frequency ALOS-2, GF-3, and Sentinel-1A SAR data. These complementary multi-dimensional SAR features are incorporated into a Random Forest model optimized using an Adaptive Genetic Algorithm (RF-AGA) to estimate forest total and component estimation. The results show that the progressive incorporation of coherence and texture features markedly improved model performance, increasing the accuracy of total AGB to R² = 0.88 and canopy biomass to R² = 0.78 under leave-one-out cross-validation. Feature contribution analysis indicates strong complementarity among SAR parameters. Polarimetric decomposition yielded the largest overall contribution, while L-band volume scattering was the primary driver of trunk and canopy estimation. Coherence-enhanced trunk prediction increased R² by 13 percent, and texture improved canopy representation by capturing structural heterogeneity and reducing saturation effects. This study confirms that integrating coherence and texture information within the RF-AGA framework enhances AGB estimation, and that the differential contributions of multi-dimensional SAR parameters across total and component biomass estimation originate from their distinct structural characteristics. The proposed framework provides a robust foundation for regional carbon monitoring and highlights the value of integrating complementary SAR features with ensemble learning to achieve high-precision forest carbon assessment. Full article

Flow dynamics in strongly curved channels with submerged vegetation play a crucial role in riverine ecological processes and morphodynamics, yet the combined effects of sharp curvature and rigid submerged vegetation remain inadequately understood. This study presents a comprehensive experimental investigation into the influence of rigid submerged vegetation on the flow characteristics within a 180° strongly curved channel. Laboratory experiments were conducted in a U-shaped flume with varying vegetation configurations (fully vegetated, convex bank only, and concave bank only) and two vegetation heights (5 cm and 10 cm). The density of vegetation ϕ was 2.235%. All experimental configurations exhibited fully turbulent flow conditions (R_e > 60,000) and subcritical flow regimes (F_r < 1), ensuring gravitational dominance and absence of jet flow phenomena. An acoustic Doppler velocimeter (ADV) was employed to capture high-frequency, three-dimensional velocity data across five characteristic cross-sections (0°, 45°, 90°, 135°, 180°). Detailed analyses were performed on the longitudinal and transverse velocity distributions, cross-stream circulation, turbulent kinetic energy (TKE), power spectral density, turbulent bursting, and Reynolds stresses. The results demonstrate that submerged vegetation fundamentally alters the flow structure by increasing flow resistance, modifying the velocity inflection points, and reshaping turbulence characteristics. Vegetation height was found to delay the manifestation of curvature-induced effects, with taller vegetation shifting the maximum longitudinal velocity to the vegetation canopy top further downstream compared to shorter vegetation. The presence and distribution of vegetation significantly impacted secondary flow patterns, altering the direction of cross-stream circulation in fully vegetated regions. TKE peaked near the vegetation canopy, and its vertical distribution was strongly influenced by the bend, causing the maximum TKE to descend to the mid-canopy level. Spectral analysis revealed an altered energy cascade in vegetated regions and interfaces, with a steeper dissipation rate. Turbulent bursting events showed a more balanced contribution among quadrants with higher vegetation density. Furthermore, Reynolds stress analysis highlighted intensified momentum transport at the vegetation–non-vegetation interface, which was further amplified by the channel curvature, particularly when vegetation was located on the concave bank. These findings provide valuable insights into the complex hydrodynamics of vegetated meandering channels, contributing to improved river management, ecological restoration strategies, and predictive modeling. Full article

Current urban green volume quantity and equity evaluations primarily rely on two-dimensional (2D) indicators that capture the planar distribution characteristics but overlook vertical structure variations. This study constructed a three-dimensional (3D) evaluation system for green volume quantity and equity by introducing Lorenz curves and Gini coefficients. Using multi-source data, including a 10 m global vegetation canopy height dataset, land cover, and population distribution data, an automated calculation workflow was established in ArcGIS Model Builder. Focusing on regional and neighborhood scales, this study calculates and analyzes two-dimensional green volume (2DGV) and three-dimensional green volume (3DGV) indicators, along with the spatial equity for 413 Chinese cities and residential and commercial areas of Wuhan, Suzhou, and Bazhong. Meanwhile, a green volume quantity and equity type classification method was established. The results indicated that 3DGV exhibits regional variations, while Low 2DGV–Low 3DGV cities have the highest proportion. Green volume in built-up areas showed a balanced distribution, while park green spaces exhibited 2DGV Equitable Only. At the neighborhood scale, residential areas demonstrated higher green volume equity than commercial areas, but most neighborhood areas’ indicators showed low and imbalanced distribution. The proposed 2DGV and 3DGV evaluation method could provide a reference framework for optimizing urban space. Full article

Extended droughts are predicted for southwestern North America, including the arid Mojave Desert, which has plant communities dominated by desert scrub vegetation. We conducted a multi-year study in which supplemental water was provided to four native shrub species: the evergreen Larrea tridentata and deciduous Ambrosia dumosa, Ambrosia salsola, and Encelia farinosa. Water treatments included −25% of precipitation (by temporarily deploying large tarps over wooden support structures), actual precipitation, and 100% and 200% of actual precipitation. Water applied occurred within 24 h of actual precipitation events. At the end of a two-year period, we allowed the plots to remain intact, receiving no supplemental water for 3.8 years, which was anomalously dry. During the initial two-year experiment, we examined growth and other physiological responses to the treatments. We also measured soil volumetric water content with depth and calculated a plant water stress index. After the 3.8-year dry period we measured stem elongation, canopy volume, leaf xylem water potential and harvested roots and shoots for biomass estimates. Supplemental water led to higher soil water content and water use, leading to increased aspects of growth which were species dependent, whereas the −25% treatment resulted in greater stress and reduced growth, but only in some species. After the 3.8-year dry period, survival in all treatments was between 97 and 100%. However, a distinct legacy effect was observed, as plants growing under the wetter treatments during the 2-year supplemental water period had more negative leaf xylem water potentials after the 3.8-year dry period than plants that were grown under the drier treatments. In addition, canopy volumes were shown to decrease if plants were grown under the wetter treatment imposed during the supplemental water period but increased if grown under the drier treatments. Our results would suggest that the impact of climate change on Mojave Desert shrubs will be linked to how they respond to wet/dry cycles, which will be linked to drought severity and the time between wet periods. The four shrub species studied have unique morphological and physiological characteristics that allow them to grow and not just survive under arid conditions, but if extended drought events occur on a more frequent basis, these shrub species may not be able to adapt and thus avoid higher mortality rates. Full article

Against the backdrop of accelerating population aging, urban green spaces have become primary venues for elderly daily activities, with their winter thermal comfort emerging as a critical determinant of senior wellbeing. However, existing studies lack quantitative guidelines on how plant characteristics affect thermal comfort, limiting age-friendly design. Thirty representative landscape space sites (waterfront, square, dense forest, and sparse forest) in Beijing’s Zizhuyuan and Taoranting Parks were analyzed through microclimate measurements, 716 questionnaires, and scoring evaluations, supplemented by PET field data and ENVI-met simulations. A scoring system was developed based on tree density, plant traits (height, crown spread), and spatial features (canopy closure, structure, enclosure, and evergreen coverage). Key findings: (1) Sparse forests showed the best overall thermal comfort. Square building spaces were objectively comfortable but subjectively poor, while waterfront spaces showed the opposite. Dense forests performed worst in both aspects. (2) Wind speed and humidity were key drivers of both subjective and objective thermal comfort, and differences in plant configurations and landscape space types shaped how these factors were perceived. (3) Differentiated optimal scoring thresholds exist across the four landscape space types: waterfront (74 points), square building (52 points), sparse forest (61 points), and dense forest (88 points). (4) The landscape space design prioritizes sparse forest spaces, with moderate retention of waterfront and square areas and a reduction in dense forest zones. Optimization should proceed by first controlling enclosure and shading, then adjusting canopy closure and evergreen ratio, and finally refining tree traits to improve winter thermal comfort for the elderly. This study provides quantitative evidence and optimization strategies for improving both subjective and objective thermal comfort under diverse plant configurations. Full article

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R² = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations. Full article

Apple fruit quality is primarily determined by Vitamin C (VC), Soluble Saccharides (SSs), Titratable Acid (TA), and the Soluble Saccharides/Titratable Acid (SSs/TA). This study aims to establish a prediction model based on the Back Propagation (BP) neural network by analyzing the intrinsic relationships between these quality indicators and the photosynthetic physiological characteristics of fruit trees, providing a new method for the precise prediction and regulation of fruit quality. Using ‘Fuji’ apple as the material, fruit quality indicators, leaf photosynthetic parameters, canopy structure indicators, and carbon–water–nitrogen metabolism indicators were systematically measured. Correlation analysis was employed to identify key influencing factors, BP neural network models with different hidden layer structures were constructed, and the optimal feature subset was screened through feature importance analysis, single-factor sensitivity analysis, and ablation experiments, ultimately establishing a simplified and efficient prediction model. Pn, Gs, SPCI, and DUE showed significant positive correlations with VC, SS, and SS/TA, whereas N and NLT were significantly positively correlated with TA content. SUE was identified as a common core driving factor for VC, SS, and SS/TA. The BP neural network demonstrated strong predictive performance for the four quality indicators, with the optimal model achieving validation set R² values of 0.87, 0.86, 0.86, and 0.89, respectively. The simplified model developed through feature screening exhibited further improved performance: the validation set R² for the VC prediction model increased to 0.93, while MAE and MAPE decreased by 32% and 35%, respectively. Photosynthetic characteristics and nitrogen metabolism status of the fruit trees serve as key physiological foundations determining apple quality. The quality prediction model based on the BP neural network achieved high accuracy, and its predictive performance was significantly enhanced after feature refinement, providing an effective tool for precise apple quality prediction and smart orchard management. Full article

This study analyzed the spatial patterns of species composition and biodiversity according to elevation on Mt. Gyebangsan, a representative protected ecosystem and the national park in Korea. Based on existing vegetation survey data, differences in species composition heterogeneity according to elevation were verified using non-metric multidimensional scaling and multi-response permutation procedure analyses. Significant differences were identified using the Sørensen distance measure. Zeta (ζ)-diversity was analyzed based on the number of shared species among habitats to quantitatively interpret the structural characteristics of biodiversity along the altitudinal gradient. The analysis revealed that the understory species composition became increasingly distinct and alpha-diversity increased with elevation. High-elevation areas (A3, A4) experienced frequent physical disturbances, including wind damage and limited moisture, resulting in active canopy openings. Consequently, rhizomatous species, including Sasa borealis rapidly covered the ground, influencing the understory vegetation structure. ζ-Diversity analysis showed that the ζ-ratio in high-elevation regions sharply declined with increasing ζ-order, indicating limited species overlap among habitats and the dominance of deterministic processes. Thus, altitudinal gradients represent a key factor in shaping biodiversity, indicating that climatic variables directly affect understory distribution and species turnover. This study quantitatively assessed biodiversity and ecological heterogeneity within the national park, providing a scientific foundation for biodiversity conservation and management. Full article

Desertification erodes arable land and human habitats. Vegetation restoration represents a critical process for improving the quality of sandy land by enhancing soil structure and nutrient cycling. This study aims to investigation how vegetation restoration affects soil physicochemical properties and soil quality. Five vegetated land types were selected (Pinus sylvestris var. mongholica Litv., PS; Amygdalus pedunculata Pall., AP; Salix psammophila, SP; Amorpha fruticosa L., AF; Artemisia desertorum Spreng., AD). Bare sandy land (BS) served as the control. The physicochemical properties of 270 soil samples from three vertical depth intervals (0–10, 10–20, and 20–30 cm) were analyzed. The findings demonstrated that vegetation restoration markedly improved the proportion of finer soil particles (clay and silt) and organic carbon, while the variations in total phosphorus, ammonia nitrogen, and nitrate nitrogen were not significant. To better understand the variations in soil quality in different vegetated lands, a soil quality index (SQI) was developed that considers multiple soil physical and chemical indicator selections and scoring methods. The SQI based on the minimum dataset and linear scoring method better differentiated the soil quality for sandy lands and showed higher values for SP among all five vegetated lands and BS. Improvements in soil quality were closely related to vegetation properties (density and coverage) and litter characteristics (thickness, water content, and total phosphorus content). Restoration strategies for sandy lands should focus more strongly on species selection, taking into account interspecific variations in litter production, physicochemical properties, canopy architecture, and planting density to more effectively improve soil quality. Full article

The ecological strategies of understory shrubs are critical for maintaining the structure and function of forest understory vegetation. Understanding the assembly mechanisms of these shrub communities is a central issue in modern ecology. To address this, our study was conducted in the typical red soil regions of Jiangxi, China, focusing on secondary forests (including both broadleaved and coniferous types) of similar stand age. We aimed to assess the effects of various environmental factors—such as soil pH, total nitrogen content, bulk density, and understory temperature—along with tree-layer characteristics—including canopy closure, tree species richness, and diameter at breast height (DBH)—on the species composition, functional traits, and phylogenetic structure of the shrub layer. Results showed: One-way ANOVA revealed significant differences in functional traits between the two forest types. Specifically, leaf thickness, specific leaf area, and chlorophyll content were significantly higher in the coniferous forest, whereas leaf dry matter content was significantly lower compared to the broadleaved forest (p < 0.05). These results suggest that understory shrubs in the coniferous forest primarily adopt a resource-conservative strategy, while those in the broadleaved forest exhibit a resource-acquisitive strategy. Phylogenetic analysis further revealed that the phylogenetic diversity (PD) of coniferous forests was significantly lower than that of broadleaved forests (p < 0.05). The phylogenetic structure in coniferous forests showed a more clustered pattern (NTI > 0, NRI > 0), suggesting stronger environmental filtering. Diversity index analysis showed that the Chao1 index indicated a richer potential species pool in broadleaved forests (p < 0.05), while species distribution was more even in coniferous forests (p < 0.05). Random Forest model analysis identified the diameter at breast height (DBH) of trees as the most critical negative driver, while soil pH was the primary positive driver. Redundancy Analysis (RDA) confirmed that the community structure in coniferous forests was mainly driven by biotic competition pressure represented by DBH, whereas the structure in broadleaved forests was more closely associated with abiotic factors like soil total nitrogen and pH (R² = 0.29, p < 0.05). These environmental drivers, through strong environmental filtering, collectively resulted in a phylogenetically clustered pattern of shrub communities in both forest types. This study demonstrates that the assembly of understory shrub communities is a complex, multi-level process co-regulated by multiple factors, shaped by both the biotic pressure from the overstory structure and abiotic filtering from the soil environment. This finding deepens our understanding of the rules governing community assembly in forest ecosystems. Full article

Agrivoltaics, an emerging approach that integrates solar energy generation with agricultural production, offers an effective solution to land-use conflicts by enabling the simultaneous production of clean energy and crops. However, the shading effect of photovoltaic (PV) modules significantly alters both the quantity and distribution of light within crop canopies, creating challenges in balancing power output with crop light requirements. This study employs the Rhino–Grasshopper parametric modeling platform, combined with Ladybug and PVsyst, to conduct batch simulations of 44 configuration schemes for an agrivoltaic system in Lianyungang, Jiangsu Province. Annual simulations of the light environment and energy generation were performed, and model accuracy was validated through field measurements using Daily Light Integral (DLI), light uniformity (coefficient of variation, CV), and annual energy yield as key indicators to assess the effects of different module layouts and tilt angles. The results reveal pronounced seasonal variations in the system’s light environment. The tilt angle exhibits a seasonal reversal pattern: higher tilt angles in winter and spring substantially reduce DLI (up to a 44% decrease under high ground coverage ratio, GCR, conditions), whereas moderate tilt angles in summer and autumn enhance light transmission, with low-GCR layouts enabling DLI values exceeding 30.6 mol·m⁻²·d⁻¹. Light uniformity was highest in the dual-row layout with 0.2 m spacing, maintaining a CV between 0.16 and 0.18. Energy yield peaked at a 30 tilt angle, showing a parabolic response pattern. Overall, this study proposes a photovoltaic module layout design method based on seasonal light distribution characteristics and the balance between agricultural and energy production goals. This method provides a scientific basis for structural layout planning and planting-model design in agrivoltaic systems and contributes to improving light-energy utilization efficiency and agricultural output, thereby achieving synergistic benefits between photovoltaic power generation and crop production. Full article

As global demand for renewable energy continues to grow, high-altitude wind energy, characterized by high speed, wide distribution, and strong stability, has emerged as a promising alternative to low-altitude wind energy. Airborne Wind Energy systems (AWEs) are key to harnessing high-altitude wind, and Ground-Generator (Ground-Gen) AWEs are favored for their lower costs and simpler deployment. This study focuses on the umbrella–ladder-type Ground-Gen AWEs, aiming to address the research gap by exploring the influence of canopy permeability on the aerodynamic and structural response characteristics of flexible wind-capturing umbrellas. A single-umbrella model of the high-altitude wind-capturing umbrella was established, and bidirectional fluid–structure interaction (FSI) numerical simulations were conducted using the Arbitrary Lagrangian–Eulerian (ALE) method. Simulations were performed under a 30° angle of attack with two canopy thicknesses (5 × 10⁻⁵ m and 1 × 10⁻⁴ m) and varying permeability (adjusted via viscosity coefficient a and inertial coefficient b). Results showed that higher permeability (smaller a and b) hindered upper canopy inflation, while lower permeability promoted full inflation and more uniform stress distribution. The max/min in-plane shear stress for the model with the lowest permeability (Model F) was approximately 85% lower than that of the model with the highest permeability (Model A). The tension coefficient increased with decreasing permeability. Full inflation resulted in a slightly higher axial load in the upper suspension lines due to the lift force, with a difference of up to 92.3% during slight collapse. This difference becomes significantly more pronounced during severe collapse. Asymmetric flow fields at a 30° attack angle generated a lift force, resulting in higher tension coefficients than those at a 0° attack angle. These findings provide valuable references for the design and optimization of high-altitude wind-capturing umbrellas. Full article

This study investigates the influence of urban greenery on microclimate conditions in Novi Sad, a city characterized by a temperate oceanic climate, by integrating high-resolution remote sensing data with in situ measurements from 12 urban climate stations. Sentinel-2 imagery was used to capture vegetation patterns, including tree lines and small green patches, while air temperature data were collected across two climatically contrasting years. Vegetation extent and structural characteristics were quantified using NDVI thresholds (0.6–0.8), capturing variability in vegetation activity and canopy density. Results indicate that high-activity vegetation, particularly dense tree canopies, exerts the strongest cooling effects, significantly influencing air temperatures up to 750 m from measurement sites, whereas total green area alone showed no significant effect. Cooling effects were most pronounced during summer and autumn, with temperature reductions of up to 2 °C in areas dominated by mature trees. Diurnal–nocturnal analyses revealed consistent spatial cooling patterns, while seasonal variability highlighted the role of evergreen and deciduous composition. Findings underscore that urban heat mitigation is driven more by vegetation structure and composition than by green area size, emphasizing the importance of preserving high-canopy trees in urban planning. This multidimensional approach provides actionable insights for optimizing urban greenery to enhance microclimate resilience. Full article

Understanding the structural dynamics of temperate forests is essential for their sustainable management. This study assessed the vertical structure of a mixed temperate forest in the Sierra Madre Occidental, Mexico, under an individual selection cutting regime implemented in 2012 and 2022. Nine Permanent Silvicultural Research Sites were established, and measurements were carried out in 2012, 2022, and 2023 to record tree species, height, and crown cover. The analyses describe dendrometric variables, structural verticality indices and the Pretzsch index; regression models were fitted and Kruskal–Wallis tests performed. The results revealed a multistratified forest: Pinus durangensis dominates the upper canopy, while broadleaved species concentrate in the lower layers, enriching the understorey. Following silvicultural interventions, structural reorganisation was evident, with an increase in emergent individuals in the canopy and stability in crown-cover frequencies. A slight increase in pine and oak cover was detected, together with the presence of new tree species characteristic of the region. Taken together, the findings indicate that planned individual-selection cutting can maintain the stand’s original vertical structure and the functionality of the mixed temperate forest in northern Mexico, providing an analytical approach applicable to other comparable forest regions. Full article