Search Results (1,022)

The accessibility of flying drones (unmanned aerial vehicles) presents reproducible and cost-effective methods to monitor submerged aquatic vegetation. In particular, drone-borne topobathymetric LiDAR provides high-resolution (cm-scale), three-dimensional information about the geometry and structure of surveyed areas, allowing for quantification of vegetation volume in addition to bathymetry. For seagrasses, this information can advance research regarding the structure of canopies in relation to blue carbon storage and biodiversity. Here, we demonstrate how drone-borne LiDAR can be used to estimate the habitat volume of eelgrass (Zostera marina) within a sheltered bay in Norway. After classifying LiDAR points using a Random Forest model, we created a Digital Terrain Model of the sea floor and a Digital Surface Model of the eelgrass canopy. From these models, we showed that eelgrass canopy volume can be estimated (between 862 and 1099 m³ across the small study area) and the above-ground carbon stock in living tissue can be quantified (between 96 and 122 kg C). To our knowledge, this is the first study to utilise drone-borne LiDAR to quantify the habitat volume and carbon-storage potential of a marine habitat-forming species like eelgrass, demonstrating a novel methodology for providing reproducible and high-resolution data of submerged aquatic habitats. Full article

A first study analyzed the effect of the presence of a highly frequented tourist trail on the size and reproductive capacity of Globularia alypum, a Mediterranean shrub of conservation interest. In Sardinia, this species is a typical example of a plant with Extremely Small Populations (PSESPs), restricted to a natural area embedded within an urban matrix, which makes it particularly vulnerable to ecological pressures. In this second contribution, the investigation expands to the entire population of the species distributed across different habitats. The possible correlations between vegetative and reproductive traits of the plant are examined, along with the influence exerted by both habitat type and varying levels of human disturbance. To evaluate potential drivers of its persistence, morphological (H, diameter and plant volume) and reproductive traits (number of flowers, number of fruits and number of seed per plant) were recorded at the individual level. Additionally, to assess human disturbance (consisting mainly of trampling), the presence of trails was used as a proxy and, accordingly, each plant was categorized following its relative position to the nearest path according to three categories: Near Trail (NT), Mid-Trail Distance (MTD), or Far from Trail (FT). A total of 114 individuals distributed across four habitat types were measured. Statistical analyses revealed only marginal associations between habitat type and vegetative or reproductive traits. While trail proximity did not influence flower and fruit production, plant volume tended to be greater in individuals located farther from trails, suggesting a potential, albeit limited, effect of reduced human pressure on plant growth. These findings highlight the importance of understanding subtle ecological interactions that shape the persistence of PSESPs in urban close to nature area and provide valuable insights for developing targeted conservation and management strategies. Full article

This paper studies the dynamics of a low-density gas directly injected onto a flat wall, focusing on the influence of different pressure ratios (PRs) and plate position. Due to safety reasons, Helium (He) was employed as substitute to reproduce the mixing characteristics of hydrogen. A Nd:YAG laser has been used to generate the luminous background in the constant volume chamber (CVC) and vegetable oil particles as trackers to identify the induced flow-field. Two configurations were investigated: the first, with a flat wall perpendicularly positioned at an axial distance of 10 mm from the injector tip, and the second with the same plate at 30 mm downstream of the injector, inclined at 30°. The pressure of injection was swept from 20 to 50 bar, while the backpressure inside the CVC ranged from 2 to 6 bar to enable the reproduction of five different values of PRs: 3, 4, 7, 10 and 17. The comparison of the results in the two configurations has highlighted the role of the plate at short distance in decelerating the jet speed (230 m/s to 160 m/s) while improving the vorticity intensity (+10%). In addition, a stagnation region was observed to form on the flat wall, downstream of the injector axis for 10 mm configuration. In this area the velocity ranged from 50% to 60% compared to the average jet speed. This phenomenon was noted to be less pronounced with the 30 mm, 30° configuration that led to a more contained speed reduction to 150–160%. Full article

Understanding how oat (Avena sativa L.) cultivars differ in canopy development and competitive ability is essential for improving yield stability under increasing weed pressure. This study used unmanned aerial vehicle (UAV)-based multispectral imaging to characterize the temporal spectral and structural traits of sixteen oat cultivars grown under weed-free and weedy conditions across two locations for two years. Weedy conditions involved natural weed populations and pseudo-weeds where canola (Brassica napus) seeded as a weed. Weekly drone imaging was carried out using a multispectral sensor, which provided vegetation indices (NDVI, NDRE, ExG) and canopy metrics (ground cover, height, volume). Logistic and Gompertz models were fitted to cultivar traits to describe growth trajectories and obtain dynamic growth parameters. Cultivars showed clear differences in early canopy expansion, maximum NDVI, and canopy volume, with forage types expressing aggressive growth and several grain types combining high early growth rate with high yield potential. Machine-learning models integrating static and dynamic UAV-derived plant traits identified early ground cover and NDRE at three weeks after planting as the strongest predictors of grain yield. Models accurately predicted both weed-free (MAE = 262, R² = 0.90) and weedy yield (MAE = 258, R² = 0.90), demonstrating that early-season UAV traits capture the physiological and structural characteristics associated with competitive ability and grain yield. These findings show that high-throughput UAV phenotyping can reliably identify traits linked to yield formation and weed tolerance, providing a scalable approach for selecting competitive oat cultivars without relying solely on labor-intensive weedy field trials. Full article

The increasing volume, temporal density, and diversity of satellite Earth observation (EO) data have fundamentally transformed quantitative vegetation remote sensing. Dense multi-sensor time series and computationally intensive modelling have rendered traditional download-and-process workflows increasingly impractical. Cloud-native computing—where data access, storage, and computation are co-located and analyses are executed in data-proximate environments—has therefore emerged as a key paradigm for scalable and reproducible vegetation EO analysis. This review provides a science-oriented synthesis of cloud-native EO for quantitative vegetation research. We examine architectural principles, data models, and compute patterns that shape how vegetation analyses are implemented, scaled, and scientifically interpreted. Particular attention is given to machine learning as a system component, including model lifecycle management, domain shift, and evaluation integrity in distributed environments. We analyse how cloud-native data abstractions influence algorithmic assumptions, validation design, and long-term product consistency, highlighting trade-offs between analytical complexity, computational cost, latency, and scientific robustness. We provide a forward-looking perspective on emerging imaging spectroscopy missions and the growing system-level requirements for reproducible, scalable, and uncertainty-aware vegetation analytics at continental-to-global scales. We also outline how cloud-native EO infrastructures are driving new scientific paradigms based on continuous monitoring, systematic reprocessing, and AI-driven modelling. Full article

Soil salinization in arid oases constrains soil functioning and crop production, making spatially explicit monitoring important for land management. Multispectral optical remote sensing enables large-area salinity assessment, but in oasis environments such as the Keriya Oasis, its performance can be limited by spectral confusion between salt crusts and bright bare soils, sparse vegetation cover, and strong surface heterogeneity. Synthetic aperture radar (SAR), by contrast, provides all-weather imaging capability and sensitivity to surface scattering and dielectric-related conditions, but its salinity interpretation is often affected by surface complexity and environmental coupling. To address these, a spectral index–polarimetric scattering integration framework that combines RADARSAT-2 and Landsat-8 OLI features within a simple two-dimensional (2D) feature space was developed. Two groups of models were constructed from variables selected through a data-driven screening process: (1) polarimetric feature space models based on combinations such as VanZyl volume scattering with Pauli odd-bounce or Touzi alpha scattering; and (2) multi-source feature space models that integrate the optimal polarimetric component with key spectral indicators such as SI4 and MSAVI. Among all tested models, VanZyl_vol-SI4 achieved the best performance (fitting: R² = 0.749, RMSE = 5.798 dS m⁻¹, MAE = 4.086 dS m⁻¹; validation: R² = 0.716, RMSE = 5.566 dS m⁻¹, MAE = 4.528 dS m⁻¹). The results indicate that integrating PolSAR scattering information with optical indices can improve salinity mapping relative to single-source feature spaces in the Keriya Oasis. The proposed 2D framework provides a concise way to compare different feature combinations and supports regional identification of salt-affected soils. Full article

The expansion of the global agri-food industry has led to the generation of large volumes of processing by-products that, although traditionally treated as waste, represent valuable sources of bioactive phytochemicals with potential for sustainable valorisation. This review critically examines the integration of fruit, vegetable, cereal, and dairy processing side streams into functional dairy products. Particular attention is given to recent advances in green and emerging extraction technologies, including ultrasound-assisted extraction, microwave-assisted extraction, and supercritical fluid extraction, with emphasis on their efficiency, environmental performance, and effects on the stability and recovery of phytochemicals. The review also discusses the health-related properties of these bioactive compounds, including antioxidant, anti-inflammatory, and metabolic regulatory effects, in relation to their incorporation into milk, yogurt, cheese, and ice cream matrices. In addition, key barriers to industrial implementation are assessed, including compound stability, sensory constraints, bioavailability, and current regulatory limitations. Beyond direct fortification, the review also considers broader valorisation pathways, such as the biotechnological production of microbial enzymes from agro-industrial biomass, as relevant strategies for supporting circularity. Overall, this review highlights how sustainable extraction approaches and functional dairy innovation can contribute to improving the nutritional value, resource efficiency, and circularity of the dairy sector. Full article

Seed priming is a simple, economical, and sustainable technique capable of enhancing crop resilience to abiotic stresses. A plastic greenhouse experiment was conducted on the durum wheat cultivar, Karim, sown in a 375 L volume container under semi-controlled conditions. Plots were arranged in a completely randomized design regarding treatments (control, salinity) and priming agents (indole-3-acetic acid, IAA; gibberellic acid, GA₃; and salicylic acid, SA). Some physiological, biochemical, and morphometric traits were analyzed at vegetative and reproductive stages. The obtained results demonstrated that salinity stress reduced plant growth and the SPAD index, hampered photosynthetic efficiency through disrupted PSII integrity and energy management in the electron transfer chain, and significantly affected ear filling (EF) and grain caliber (marked by mean weight of 100 grains, MW100G). However, seed hormonal priming allowed the alleviation of salinity stress effects on durum wheat growth and yield. Although IAA and GA₃ have shown significant potential in improving durum wheat tolerance to salinity, SA was found to be the most effective priming agent. It promotes the biosynthesis of chlorophyll pigments, restores the functional integrity of PSII, enhances photosynthetic efficiency, increases plant growth, and stimulates ear filling and wheat grain development. The principal component analysis demonstrated the interdependence of the vegetative and reproductive traits and presents SA as the most effective treatment that brings plants close to control conditions, despite the salinity. Full article

The object of the study is a single heated carbonaceous particle of relatively small size, 0.003 to 0.01 m. Main hypothesis: The formation of soot particles and black carbon particles is caused by the thermochemical destruction of dry organic matter of forest fuel and the mechanical fragmentation of coke residue. The aim of the study is to conduct numerical simulations of heat and mass transfer in a single heated carbonaceous particle, taking into account the soot formation process and assessing its fragmentation with regard to heat exchange with the external environment in a 2D setting. As part of this study, a new model of heat and mass transfer in a pyrolyzed carbonaceous particle was developed, taking into account its step-by-step fragmentation (fragmentation tree model with four secondary particle formations from the initial particle). The calculations resulted in the distributions of temperature and volume fractions of phases in the carbonaceous particle across various scenarios. Scenarios of surface fires (initial temperatures of 900 K and 1000 K), crown fires (1100 K), and a firestorm (1200 K) for typical vegetation (pine, spruce, birch) are considered. Cubic carbonaceous particles are considered in the approximation of a 2D mathematical model. To describe heat and mass transfer in the structure of the carbonaceous particle, a differential equation of thermal conductivity with corresponding initial and boundary conditions of the third type is used, taking into account the gross reaction in the kinetic scheme of pyrolysis and soot formation. Differential analogues of partial differential equations are solved using the finite difference method of second-order approximation. Options for using the developed mathematical model and probabilistic fragmentation criterion for assessing aerosol emissions are proposed. Recommendations: The suggested mathematical model must be incorporated with mathematical models of forest fire plume and aerosol transport in the upper layers of the atmosphere. Moreover, probabilistic criteria for health assessment must be developed for the practical use of the suggested mathematical model. Full article

This study analyses two wetlands within the Medina del Campo groundwater body (Duero River Basin, Spain) to reconstruct flood patterns and quantify the hydrological volumes involved in episodic inundation. We integrate Sentinel satellite imagery (2015–2024), targeted field campaigns (2024–2025), and preliminary water-balance assessments (2015–2022). Calculations were constrained to the inundated cells of each wetland bed to reduce spatial heterogeneity issues. For Laguna de los Lavajares, an initial standing water depth was assumed to estimate infiltration losses more accurately. We discuss the primary sources of uncertainty—particularly the representation of atmospheric losses as evaporation versus evapotranspiration—and recommend computing water balances for wet, average, and dry years to capture interannual variability. Key findings include the identification of distinct hydroperiods for each wetland, the dominant role of infiltration in the water balance of Laguna de los Lavajares, and the critical influence of vegetation-driven evapotranspiration in Laguna Redonda. Full article

Biostimulant applications may alleviate various stresses and improve the yield of crops, thus contributing to the promotion of crop growth and development in agricultural systems. Despite these potential benefits, the effects of biostimulants on root morphological traits remain poorly understood. In the present study, a global meta-analysis of 111 peer-reviewed publications was conducted to quantify the effects of biostimulant applications on various root morphological traits and identify the determining factors. Compared to untreated controls, biostimulant applications significantly increased the primary root length by 14.7%, total root length by 17.7%, root biomass by 24.5%, root activity by 21.7%, root diameter by 4.0%, root-to-shoot ratio by 2.4%, root volume by 25.7%, root surface area by 15.6%, root tips by 15.4%, and root forks by 15.6%. The biostimulant type and crop species were identified as the main moderators of root morphological responses. Among various biostimulants, humic acid showed the most consistent and pronounced positive effects. Additionally, orchard and vegetable crops exhibited greater responsiveness than grain crops. These findings provide quantitative evidence that biostimulants promote root system development across diverse crop species. They also underscore the potential of biostimulants to enhance nutrient acquisition and support more sustainable agricultural production. Full article

Aiming at diverse urban tree structures and difficulties in vegetation point cloud extraction and utilization, this study proposed single-tree-scale biological parameter estimation methods for urban scenarios to enhance point cloud’s application value in urban greening management. For single-tree segmentation, it constructed a method based on the constraints of the trees’ geometric features and combined the gravitational modeling characteristics, called the CGF-CG single-tree segmentation method. This method (i) combines clustering and principal direction analysis to extract trunk points, (ii) introduces canopy segmentation based on trunk positions, (iii) optimizes edge point attributes via a gravitational model. Based on CGF-CG’s accurate results, an improved random forest method for single-tree biological parameter (IRF-BP) estimation (aboveground biomass, carbon storage, leaf area index, living vegetation volume) was proposed: (i) correlation analysis with variable screening, (ii) adaptive feature selection and pigeon-inspired optimization to enhance model generalization, (iii) adopting Shapley Additive Explanations (SHAP) to improve interpretability. Based on these, a complete model for different tree species was constructed. Validation showed that CGF-CG exhibited negligible over-segmentation and under-segmentation in the selected study areas, with overall average precision, recall, and F1-score over 98.5%. Additionally, on the selected overall region, the overall mF1 score, mPTP, and mPTR of our method are 99.13%, 99.15%, and 99.12%, respectively, which are superior to Forestmetrics, lidR, PyCrown, and DBSCAN methods. IRF-BP performed well, with a highest R² of 0.81 and a lowest mean absolute percentage error of 7.5%, effectively surpassing the performance of traditional models such as RFR, GBR, KNN, and XGB. In summary, results provided theoretical and technical support for urban green resource management and evaluation. Full article

Alluvial plains in the marginal zone of the monsoon system are sensitive to the climate–hydrology interaction. However, long term, high-resolution sedimentary records remain scarce in the Songnen Plain of Northeast China. This limited our understanding of the paleoclimate–paleohydrology coupling evolution over glacial–interglacial cycles. A 50.6 m continuous core was retrieved from the western Songnen Plain. The age–depth model and wavelet transform spectrum showed sedimentary continuity from ~885 ka B.P. (the late Early Pleistocene) to ~6 ka B.P. (the early Holocene), with no major hiatuses exceeding orbital resolution. Grain size analyses revealed 18 microfacies, which were synthesized into two major evolutionary cycles: a fan-delta front cycle (dominated by subaqueous mouth bars and distributary channels) and a fan-delta plain cycle (characterized by intertributary bays, floodplain lakes/swamps, and crevasse splays). The absence of pro-delta facies and the sediment succession record the oscillatory shrinkage of the Songnen paleolake. The pulsed enhancements of hydrodynamic energy, marked by grain size coarsening, coincide with major glacial–interglacial transitions (MIS 20/19, 18/17, 16/15, 14/13, 8/7, 6/5, 4/3, and 2/1), whereas fining grain sizes dominate warm interglacial periods (MIS 11, 9, 7, 5, 3, 1). These patterns are sensitive response of the alluvial plain to orbital-scale climate change. Cold–arid glacial background promoted vegetation loss and hydrological instability, and warm–humid interglacial background favored low-energy hydrological condition. This study demonstrates that the regional alluvial evolution was primarily controlled by global ice-volume fluctuations through variability of the East Asian summer monsoon. This study provides a reference for the muti-scale climate–hydrology coupling mechanism study in the northern marginal zone of EASM and highlights the importance of alluvial sediment succession in paleo-research. Full article

A detailed characterization of root system architecture (RSA) and growth dynamics is key to develop stress-resilient maize varieties. We evaluated sixty-five Mediterranean maize inbred lines using automated high-throughput phenotyping under controlled conditions. Shoot and root traits were extracted from imaging data during early vegetative development, revealing significant genotype-specific variation in root biomass-related traits (total root length, total root volume), root architecture (root angle, root system depth, root system width), and relative growth rates. Notably, lines previously classified as heat and drought stress-resilient or stress-sensitive based on above-ground development did not group according to particular root traits, indicating that multiple strategies may underlie tolerance to combined stress. We identified lines with contrasting RSA, including deeper roots, shallower roots, or overall larger root systems, that offer new opportunities for resilience breeding. Our results underscore root traits as critical yet underexploited targets for improving stress resilience and resource efficiency. Full article

Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana^®) with two planting densities (625 and 1250 trees ha⁻¹) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling. Full article