Search Results (6,415)

Seagrass meadows support critical coastal ecosystems, but corresponding species-level remote sensing data remain limited, particularly in the Arabian Gulf, where field spectral data for dominant taxa are extremely limited. We present the first multi-species spectral characterisation of three dominant seagrass species in the Kingdom of Bahrain—Halophila stipulacea (n = 46 spectra, 25 stations), Halodule uninervis (n = 34, 19 stations), and Halophila ovalis (n = 17, 8 stations)—measured with an ASD FieldSpec^® 4 Hi-Res spectroradiometer (Malvern Panalytical, Malvern, UK; 350–2500 nm) from samples collected across 29 geographic stations (52 species–station sampling units). All sample counts reported here underwent quality control. Kruskal–Wallis tests with Benjamini–Hochberg (BH) correction, Jeffries–Matusita (JM) distance, Hedges’ g, and linear discriminant analysis (LDA) were used to characterise inter-species differences. H. ovalis was clearly distinguished from both co-occurring species: the Hd. uninervis–H. ovalis pair showed a discriminating window of 692–1394 nm (mean |g| = 1.31, BH q = 0.000046), and that for the H. stipulacea–H. ovalis pair was 700–1376 nm (mean |g| = 1.21, BH q = 0.000285); the JM distances were 1.60–1.67. A secondary shortwave-infrared discriminating window (1607–1755 nm; mean |g| = 0.90, BH q = 0.006) was also identified for the Hd. uninervis–H. ovalis pair. The H. stipulacea–Hd. uninervis pair showed meaningful geometric separation (JM = 0.994) but no individually significant wavelengths at the available sample size. ASentinel-2-proxy LDA achieved 85.6% overall accuracy (balanced accuracy = 87.3%; macro area under the curve = 0.917), outperforming a Landsat-proxy model by 20 percentage points. For each species, both a best-overall index and a visible-range alternative optimised for submerged satellite remote sensing are reported. The primary indices achieved balanced accuracies of 0.877–0.924; the visible-range alternatives achieved 0.818–0.907. Performance degraded substantially under noise (σ ≥ 0.002: −7.5 percentage points [pp]) and wavelength misregistration (±2–3 nm shifts caused losses of 5.5–15.7 pp), calling for stringent calibration requirements. These results constitute the first multi-species spectral library for Kingdom of Bahrain seagrasses, supporting Sentinel-2-based species mapping in the Arabian Gulf. Full article

Accurate estimation of winter wheat aboveground biomass (AGB) is essential for crop growth monitoring and precision agricultural management. To reduce the effects of canopy structural complexity and spectral saturation on AGB estimation, this study evaluated winter wheat grown under different compost substitution ratios and planting densities. Based on unmanned aerial vehicle (UAV) multispectral and RGB imagery acquired over two growing seasons at four key growth stages, spectral vegetation indices, colour vegetation indices, and canopy structural features were extracted and integrated. Recursive feature elimination, Elastic Net, and support vector regression were used to construct stage-specific AGB estimation models. The optimal feature strategy varied among growth stages, indicating that AGB estimation requires stage-specific feature selection rather than a single fixed feature combination. The proposed framework achieved validation R² values of 0.872, 0.898, 0.867, and 0.895 at the jointing, booting, flowering, and grain-filling stages, respectively, and the corresponding RRMSE values were 12.5%, 12.1%, 14.3%, and 12.0%, respectively. Additional comparisons with PLSR, RF, and XGBoost based on the stage-specific optimal feature sets further confirmed the competitive performance of SVR under the present small-sample and multi-source feature conditions. Model improvement was more evident at the flowering and grain-filling stages. At these stages, the integration of selected spectral, colour, and structural features better represented canopy closure, spike-layer formation, and late-season biomass variation. Under the treatment combining 20% compost substitution with a planting density of 4.5 million plants ha⁻¹, winter wheat maintained relatively high AGB levels across growth stages. The novelty of this study lies in demonstrating that the effectiveness of multi-source UAV feature fusion for winter wheat AGB estimation is growth-stage dependent and is enhanced when coupled with feature selection. These findings provide a methodological reference for multi-temporal AGB monitoring and precision cultivation management under similar field conditions. Full article

This article describes the development of a compact and affordable variable-temperature NMR instrument designed primarily to measure dynamic molecular motion in solids and liquids. The instrument consists of Lab-Tools’ Mk4 palm-top time-domain NMR spectrometer fitted with a Peltier-cooled variable-temperature probe inside a shimmed Halbach magnet. Measurement of NMR relaxation times T₁, T₂, and ${\mathrm{T}}_1\rho$ is possible over the temperature range −20 °C to 70 °C with cooling and heating rates, and data acquisition is controlled from an integrated mini-PC. The overall footprint of the instrument is roughly that of a shoe box, making both in-the-field and bench-top measurements possible. Applications of this instrument include measuring pore-size distribution in porous rocks, the viscosity of oils and tars trapped in porous rock, the properties of polymers, and the viscosity of the liquid components of foods (e.g. fruits, vegetables and seeds). Results of test measurements for calibrated oils and olive oil are presented together with measurements of molecular mobility in a solid polymer. Full article

Intercropping medicinal and aromatic plants (MAPs) with olive trees provides significant economic and ecological benefits in Mediterranean agroecosystems; however, belowground competition for water and nutrients often limits understory plant development. Therefore, the main aim of this two-year field study, conducted in Kilis, Türkiye, was to evaluate the effects of black polyethylene mulch on the vegetative growth and essential oil characteristics of lavender (Lavandula x intermedia) and rosemary (Rosmarinus officinalis L.) cultivated as intercrops between olive rows. The results demonstrated that black polyethylene mulch application significantly enhanced lavender’s vegetative growth, substantially increasing average fresh (1665.5 g for mulched vs. 785.0 g for non-mulched control) and dry biomass (783.5 g for mulched vs. 403.0 g for non-mulched control), plant height (83.37 cm vs. 63.42 cm), and canopy diameter (89.03 cm vs. 71.85 cm) compared to the non-mulched control. Furthermore, lavender essential oil yield improved significantly (5.40% vs. 4.09%), with linalool (30.22%) and camphor (12.69%) identified as the major volatile compounds. For rosemary, mulching positively impacted plant height (42.50 cm vs. 35.00 cm) and shoot length (28.65 cm vs. 22.62 cm), while maintaining a stable essential oil yield (0.40% on average) composed primarily of camphor, eucalyptol, and α-pinene. In conclusion, mulching emerges as a highly effective agronomic practice for mitigating resource competition, promoting vegetative growth, and optimizing essential oil production—particularly for lavender—thereby increasing the sustainability and overall productivity of olive and MAP intercropping systems in semi-arid environments. Full article

The world has already been facing food, nutrition, and security challenges for the last few decades. The coronavirus 2019, COVID-19, has a significant impact on food security and agriculture, such as affecting food demand and the food supply chain, with the greatest consequences on the most vulnerable population. This review provides a comprehensive overview of the effects of COVID-19 on global agriculture and food security, drawing on recent scientific publications, institutional reports, and policy documents from 2020 to 2026. The review examines the impact of the pandemic on cropping patterns, fruit and vegetable harvests, availability of farm inputs, connectivity of the agricultural system, food supply chains, food demand, and labor availability. Vegetable and fruit markets were most affected due to the spread of COVID-19. Due to the closing of markets and restaurants, produce distributors and farmers were required to transfer supplies entirely from the food production to the marketplace. These effects are additionally being felt in agriculture and food security. Almost 55% of researchers indicated that COVID-19 has the most impact on agriculture and its complete harvest during the season, and an additional 45% stated that COVID-19 has adversely affected food security. However, food has slowed down well to date in numerous nations. The spread of COVID-19 is beginning to disrupt the supply of agricultural products and food to consumers and the marketplace across and within borders. The different spring crops, such as sunflower, canola, maize, barley, spring wheat, and various field vegetables, cannot be grown during COVID-19. Consequently, COVID-19 has had a binding effect on the food supply chain and agriculture due to the disruption, which the government should have addressed promptly. Full article

Most studies in the field of application technology have focused on the interaction between adjuvants and agrochemicals, highlighting the need for further research to evaluate the behavior of adjuvants in association with other classes of crop protection products. In this context, the objective of this study was to evaluate the influence of adjuvants and air velocity on spray drift deposition in simulated applications conducted in a wind tunnel using a bioinsecticide based on Bacillus thuringiensis. The experiment was carried out in an open-circuit, blower-type wind tunnel installed at the Agricultural Machinery Laboratory of the State University of Goiás—Central Campus. The study was conducted in a completely randomized design arranged in a 5 × 4 × 4 factorial scheme, with three replications. Treatments consisted of five horizontal distances from the spraying point (0.45, 0.75, 1.05, 1.35, and 1.65 m), four wind speeds inside the tunnel (1 m s⁻¹, 2 m s⁻¹, 3 m s⁻¹, and 4 m s⁻¹), and four spray solution formulations (water; Dipel^®, Dipel^® + Veget’Oil^®, and Dipel^® + Break Thru^®). Artificial targets positioned transversely to the airflow were used to collect spray deposition and, after spraying, were divided into lower, middle, and upper thirds according to the height of the test section. Data were obtained by spectrophotometry and, after verification of the ANOVA assumptions, were subjected to analysis of variance (p < 0.05). When significant effects were observed, regression analyses were applied. Statistical analyses were conducted using the R and Sisvar software packages. Mean deposition values were converted into deposition percentage as a function of the total sprayed volume. The experimental data were also subjected to geostatistical analysis using GS+ software (Version 7^®). After confirming spatial dependence, contour maps were generated using kriging. Higher wind speeds led to higher deposition percentages. The use of adjuvants affected spray deposition in the upper and middle thirds, with responses depending on the spray solution composition. Spray deposition in the wind tunnel can be analyzed using geostatistics, as this variable showed a high degree of spatial variability across all treatments evaluated. Full article

Rapid urbanization in tropical Asia has led to a critical loss of green cover, exacerbating urban environmental challenges. While green roofs offer a promising Nature-based solution, their implementation in Asian countries is hindered by the prevalence of sloped roofs and high structural conversion costs. This research addresses this gap by developing a novel, lightweight vegetative roof tile designed as a direct structural replacement for conventional roofing materials in Sri Lanka. Existing roofing systems were studied, followed by a laboriousness study to determine the optimum tile dimensions. To meet these requirements, a modular tile measuring 900 mm × 1200 mm with a wave-shaped corrugated profile (a 10 mm rise and a 200 mm pitch) was engineered using SolidWorks 2024 and ABAQUS 2024 to meet Eurocode standards. Field investigations into plant health helped to finalize the depth of the roof tile as 2.5 cm. Following root penetration testing, fiber-reinforced plastic was selected for the tile structure to ensure durability while maintaining a total saturated weight of 52.5 kg/m². Biological testing demonstrated robust greening performance, with Axonopus compressus and Zoysia matrella achieving 100% survival rates and over 80% canopy coverage. This design methodology can be adapted across tropical Asia, contributing significantly to regional green infrastructure development and sustainable building practices. Full article

The climate crisis, housing precarity, and the loss of everyday architectural heritage are converging challenges in Mediterranean cities. This article investigates the adaptive reuse of early twentieth-century adobe refugee dwellings in Nea Ionia and Kaisariani, neighborhoods of Attica, Greece, as an integrated social, environmental, and cultural strategy. Historical documentation, urban-morphological analysis, field observations, building survey data, material assessment, and design-based microclimatic analysis were combined to evaluate compatible restoration and bioclimatic upgrades as alternatives to demolition and conventional energy retrofit practices, with the main aim of preserving an important part of Greek history and architecture. The study develops a replicable qualitative assessment framework that identifies how existing adobe envelopes, compact layouts, courtyards, thresholds, vegetated pergolas, and low-water evaporative cooling may support low-carbon housing reuse. The results clarify the current preservation conditions and reuse potential of the selected case-study fragments, showing that adobe dwellings can preserve embodied material value, retain thermal mass and hygroscopic regulation, and support social housing when repaired with compatible, low-impact techniques. The article argues that the reuse of adobe refugee dwellings can function as a distributed urban strategy for housing provision, heritage continuity, and microclimatic adaptation. Its main contribution is a transferable analytical framework for assessing overlooked earthen housing stocks in dense Mediterranean contexts. The study argues that adaptive reuse can serve simultaneously as a means of social housing, a mechanism for optimizing the microclimate, and a means of preserving the tangible and intangible heritage of Greek adobe buildings that have been standing for over 100 years. This position extends circular construction debates by prioritizing non-demolition and direct reuse while preserving an important period of history. Full article

Sugarcane is a high-value crop in Egypt, yet weed communities in the understudied Upper Egypt region have not been systematically characterized. This study provides a comprehensive analysis of weed floristic composition, phytogeographical affinities, and the edaphic and canopy light factors governing vegetation structure across contrasting Nile Valley clay and reclaimed desert lands in Qena Governorate. Fourteen stands were surveyed during the 2024/2025 sugarcane growing season, recording 110 species from 33 families (68 annuals and 42 perennials), which were dominated by Poaceae, Asteraceae, Fabaceae, Euphorbiaceae, and Amaranthaceae (54.6% of the flora recorded). Therophytes were the most abundant life form (60.9%), and 51.8% of species belonged to Neotropical, Palaeotropical, Cosmopolitan, and Pantropical chorotypes. Diversity indices showed high and balanced species diversity, with no dominance by any single species. Seasonal variation showed that species richness peaked in spring, decreased through summer and autumn, and correlated with light intensity under the canopy. TWINSPAN identified four vegetation groups, which were merged into three primary vegetation groups (A, B, and C) via DCA and CCA ordinations and linked to microhabitats shaped by elevation and soil physicochemical properties. CCA revealed that Group C (stands in the Nile Riverbank lands) had the highest diversity, which was associated with organic matter, clay, and field capacity. In contrast, Group A (stands of reclaimed desert land) had low richness linked to high levels of Total Dissolved Solids (TDS), Electrical Conductivity (EC), Na, K, Mg, CaCO₃, and sandy soils. Group B (stands of Nile clay lands) was an intermediate transitional community between groups A and C. These findings establish edaphic factors as the primary determinant of weed community structure, with salinity as the critical constraint in reclaimed lands and seasonal light variation as a secondary diversity filter. Full article

Single-index greening trends can misrepresent post-mining recovery because they do not show whether disturbed surfaces are converging toward the spectral conditions of nearby stable vegetation. Here, we present a 22-year (2003–2024) Landsat-based assessment of the Nannihu molybdenum mine (Henan, China) by combining LandTrendr-based disturbance and recovery timing from annual NBR series with a benchmark-relative spectral recovery index (RSRI) and five-epoch random forest land-surface classification used as contextual support. The classifier was trained on 2024 samples and transferred to earlier epochs without independent validation at each epoch. Historical class labels should therefore be treated as approximate contextual support. A five-type recovery pathway typology showed that only 41.8% of mine-affected pixels followed vegetated recovery pathways, while 28.2% stabilized as non-vegetated surfaces and 25.0% remained under persistent disturbance. Even the combined vegetation recovery type had a mean RSRI of only 0.309 (SD = 0.143), suggesting that greening alone does not imply close benchmark-relative spectral proximity to the local stable-vegetation reference. Disturbance magnitude was the feature most strongly associated with RSRI variation (XGBoost SHAP mean, |SHAP| = 0.075). The RSRI quantifies benchmark-relative spectral proximity using local stable-vegetation benchmarks, and it does not measure species composition, biomass, or ecosystem function. This site-specific case study indicates that benchmark-relative spectral assessment can complement conventional greening metrics in retrospective mine monitoring using open-access Landsat archives, with field validation the natural next step toward linking these spectral findings to ecological or functional recovery. Full article

Although the green belt around Greater Lomé (GBGL) is a vital ecological buffer, it is currently facing significant degradation. This decline appears to be associated with a combination of various socioeconomic uses by the local community and formal operations of established businesses. Grounded in the cultural materialism framework, this study aims to contribute to a better understanding of the dynamics of the socioeconomic uses of the green belt around Greater Lomé in a context of degradation and investigates the dynamics of these socioeconomic uses and their environmental impacts through a multidisciplinary methodology. This approach combines anthropological analysis based on field observation, 53 semi-structured interviews and 5 focus groups, a quantitative questionnaire survey (n = 384) and an analysis of land use and land cover (LULC) dynamics derived from Landsat imagery (2003–2023). The results reveal six main types of socioeconomic uses of the GBGL (notably land transactions, agriculture, breeding and grazing, exploitation of wood energy, timber and utility wood, sand mining, and waste disposal), which lead to complex social dynamics ranging from conflicts to alliances among stakeholders. The LULC dynamics analysis indicates a staggering 468.26% expansion in built-up areas over the last 20 years, at the expense of swamp vegetation/gallery forest (−76.79%), tree-and-shrub savanna (−53.47%) and plantations (−49.43). This study provides a scientific basis supporting the urgent necessity to establish the GBGL as a legally protected entity and argues in favour of an inclusive management model that is designed to reconcile the socioeconomic survival needs of local populations with sustainable preservation of essential ecosystem services. Full article

Radar is critical for urban security against Unmanned Aerial Vehicles (UAVs), yet signal occlusion caused by dense buildings and complex urban structures remains a major challenge for coverage assessment. Existing approaches commonly rely on 2D maps or 2.5D Digital Surface Models (DSMs), which have difficulty representing vertical facades, vegetation, bridges, overhanging structures, and void spaces. These geometric limitations can introduce errors in radar occlusion determination and direct-path power-density estimation. Full 3D ray-tracing methods offer high fidelity, but their multi-path modeling and material-parameter requirements can be costly for large oblique photogrammetric city meshes. To address this problem, this paper proposes the Visible Radar Power-Density Field (VRPF), a 3D radar power-density field computation framework based on high-resolution oblique photogrammetric models. The method constructs a reusable spatial index for large numbers of triangular facets and performs two-stage occlusion queries: rapid Axis-Aligned Bounding Box (AABB) pruning followed by ray-triangle intersection tests. Together, these components enable efficient direct-path power-density calculation while accounting for line-of-sight occlusion in complex urban scenes. Qualitative and quantitative experiments show that VRPF better preserves occlusion boundaries around building edges, vegetation, and elevated structures than DSM-based baselines. VRPF also requires less time for repeated occlusion queries than a conventional 3D BVH ray-casting baseline while maintaining highly consistent radar-signal occlusion determinations. With 32 threads, VRPF computes power density for ${10}^8$ target points in 5.92 s, about $2.66\times$ faster than the 1 m DSM method. These results indicate that VRPF provides a practical balance between geometric fidelity and computational efficiency for direct-path radar power-density assessment with urban geometric occlusion. Full article

Tunnel seepage in heterogeneous ground can trigger hydrogeological hazards such as concentrated water inflow, groundwater depletion, deformation of surrounding structures, and subsequent eco-environmental degradation. However, these processes are still commonly evaluated using deterministic models that neglect the spatial variability of hydrogeological parameters. To address this limitation, this study develops a stochastic hydro–geo–mechanical–ecological framework that integrates random field theory with physics-informed neural networks (PINNs) for hazard evaluation and rapid prediction of tunnel seepage responses. The spatial variability of key parameters, including permeability and porosity, is characterized using the Karhunen–Loeve expansion and embedded into coupled governing equations for unsaturated–saturated seepage, seepage–stress interaction, and groundwater–soil–vegetation responses. A PINN surrogate model with random-field inputs is then constructed to predict hydraulic head, tunnel inflow, displacement, groundwater depth, vegetation coverage, and soil physicochemical indices, while simultaneously quantifying uncertainty. A karst tunnel case in Chongqing, China, is used to demonstrate the proposed framework. The results show that spatial heterogeneity promotes preferential flow paths and intensifies seepage-induced hazards compared with deterministic mean simulations, leading to larger groundwater drawdown, stronger ecological degradation, and greater overall response variability. The proposed PINN achieves high predictive accuracy (R² > 0.97) and reduces single-case computational time from hours to seconds, enabling efficient multi-scenario evaluation and uncertainty-aware risk assessment. This framework provides a physically consistent and computationally efficient tool for evaluating water-related hazards and long-term environmental impacts in underground engineering. Full article

Background/Objectives: Soil salinity is a major constraint on legume productivity worldwide, threatening forage pea (Pisum sativum L.) cultivation in semiarid regions. This study evaluated the effect of exogenous melatonin in attenuating NaCl-induced salinity stress across diverse forage pea genotypes. Methods: A three-factor factorial experiment was conducted under greenhouse conditions, testing three NaCl levels (0, 100 and 200 mM) and four melatonin concentrations (0, 100, 150 and 200 µM) across 13 genotypes with three replications (468 pots). Nine vegetative traits were measured and analyzed by factorial ANOVA and Tukey’s HSD test. Results: Increasing NaCl from 0 to 200 mM reduced plant height by ~28% and node number by ~32%. Application of 100 µM melatonin under 100 mM NaCl reduced canopy temperature from 28.1 °C to 23.7 °C and restored SPAD values from 21.7 to 26.5 under 200 mM NaCl. By contrast, 200 µM melatonin under severe salinity paradoxically suppressed SPAD to 8.9 and reduced root length. Emirbey and Kirazlí showed the greatest vegetative growth, while Özkaynak exhibited the highest chlorophyll content. Conclusions: 100 µM melatonin emerged as the optimal concentration for alleviating moderate salt stress in forage pea, and genotype selection is critical when deploying melatonin as a biostimulant under saline conditions. Direct measurement of biomass, yield, and forage quality under field conditions remains an essential next step before agronomic deployment. Full article

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have become valuable tools in precision agriculture, enabling the monitoring of crop health, biomass estimation, and stress detection. However, the effectiveness of these measurements depends on several factors, including repeatability, sensitivity, and accuracy. Understanding these factors is crucial to ensure reliable data collection, particularly in regions with fluctuating weather patterns. This study evaluated the sensitivity of multispectral data collected within a short time frame and its impact on vegetation indices in normal field conditions. Measurements were taken over three days, with three UAV flights performed each day. Multispectral data were analyzed to identify statistically significant differences in vegetation indices, with calculations performed independently for each measurement day. The repeatability of vegetation indices varied between measurement days. When all measurement days were analyzed together, GARI, GNDVI, NDRE, and NDVI were the only indices that did not show statistically significant differences between flights. However, the magnitude of differences varied depending on the index, with some indices showing only minor variations between flights. Full article