Search Results (456)

Seagrasses are vital ecosystem engineers and habitat architects in coastal environments, with Posidonia oceanica in the Mediterranean playing a crucial role as an indicator of ecological health. As an endemic and vulnerable species, P. oceanica meadows are highly susceptible to environmental degradation, underscoring the importance of non-destructive monitoring techniques. Traditional SCUBA-based surveys are accurate but resource-intensive and difficult to scale, especially for estimating shoot density and leaf length. This study applies a conservative acoustic-based approach to assess Posidonia oceanica biometrics, habitat characteristics, and ecological status along the Turkish Levant coast. The method offers a non-destructive alternative to SCUBA surveys and addresses a regional knowledge gap in Mediterranean seagrass monitoring. Acoustic data collected during winter and summer 2019 along the Turkish Levant coast were analyzed to estimate seagrass biometrics and derive ecological indicators, with validation via SCUBA observations. Results show that acoustic methods can reliably estimate shoot density, leaf area index, and canopy height. They provide broad-scale coverage and efficiency, though further refinement is required to improve calibration across depths and substrates. While acoustic methods provide broad, non-invasive coverage, they are affected by spatial and temporal variability that SCUBA surveys capture more reliably. Calibration of the POSIBIOM (vers 1.1) algorithm was based on specimens collected at 15 m depth on rocky substrates. While this provided consistent regression relationships, it may limit accuracy when extrapolated to habitats such as sand, mud, or matte. This study represents the first high-resolution, spatiotemporal mapping of P. oceanica meadows and benthic habitats along a significant portion of the Turkish Levant coast using acoustics alone. Overall, the study highlights the potential of acoustics as a scalable, non-invasive tool for seagrass monitoring. This approach contributes to ecosystem-based management and conservation strategies in the Mediterranean. Future work will focus on refining models to address bottom type- and depth-dependent acoustic responses and improve biometric accuracy. Full article

The diagnosis of missing seedlings in high-density drill-seeded crops is often hindered by the strong coupling between visual perception and diagnostic rules, which leads to an irreversible cascade amplification of underlying missed detection errors. To address this dilemma, this paper proposes a “Perception–Topology” collaborative decoupling framework oriented toward row structure perception. In the perception phase, a row-structure-enhanced detection model (RS-YOLO) is constructed. It integrates Space-to-Depth (SPD) conversion, a Selective Frequency-domain Aggregation Module (SFAM), and a Row-Structure Attention Mechanism (RSM) to effectively suppress tire rut interference and explicitly reinforce the spatial topological priors of crops. In the diagnostic phase, an Adaptive Intra-row Gap Analysis (AIGA) algorithm is proposed. By utilizing a dynamic median intra-plant spacing scale and core canopy geometric pruning, this algorithm fundamentally reformulates missing seedling diagnosis into a physical interruption metric of one-dimensional graph connectivity. Evaluated on a finely reconstructed UAV-based sorghum imagery dataset, RS-YOLO achieved a significant improvement of 2.7% in precision and 3.2% in recall over the baseline model, providing a structure-aligned, high-confidence input for the diagnostic process. Based on this perceptual foundation, the AIGA algorithm ultimately achieved a diagnostic precision of 96.11% and a recall of 91.48% without the need for negative sample annotations. This framework effectively severs the propagation chain of perceptual errors, providing a noise-robust and highly physically interpretable new paradigm for the automated inspection of field population structures. Full article

Rising temperatures, driven by global climate change, are profoundly altering high-latitude ecosystems, influencing vegetation phenology and productivity. However, understanding the long-term, nuanced responses of these ecosystems remains a critical challenge. Soil warming experiments have served as useful tools for understanding these shifts. However, many of these studies have relied on a single measure, predominantly the Normalized Difference Vegetation (NDVI), measured at a single level of warming. This approach often fails to separate structural greening from underlying physiological responses. To address these gaps, this study provided a comprehensive snapshot assessment of growing season vegetation dynamics in a subarctic grassland ecosystem in Iceland that had been exposed to continuous geothermal soil warming for over 60 years. Using high-resolution multispectral drone imagery, twelve different vegetation indices (VIs) were derived to assess not only greenness but also physiological stress and photosynthetic efficiency across a range of mean annual soil temperatures (MATs). Using linear regression and redundancy analysis (RDA), the responses of these indices to warming and their relationships with other environmental drivers, such as standing biomass and plant nutrient concentrations (nitrogen and phosphorus), were analyzed. The results revealed significant positive linear relationships between most of the indices and MATs across the 5 to 11 °C range. This indicated that higher MATs led to increased biomass and structural growth, without revealing any significant thresholds or tipping points in vegetation response within the observed warming range. However, the Photochemical Reflectance (PRI) showed a significant negative relationship with warming, suggesting a decoupling between structural greening and photosynthetic light-use efficiency. Furthermore, RDA results indicated that, while most of the VIs were primarily driven by biomass, the decline in PRI was likely a compounding effect of physical canopy self-shading and plant phosphorus constraints. Ultimately, this study demonstrated that, while these subarctic grasslands exhibited local evidence of “Arctic greening” under further warming, multispectral drone remote sensing could detect underlying physiological adjustments and nutrient constraints that traditional greenness indices might overlook, providing a more nuanced understanding of ecosystem response. Full article

Roadways classified as state routes in both rural and urban settings are tree-lined, creating a canopy over the pavement surface. Although trees are beneficial to the environment, they pose a hazard to road safety in a variety of ways. For example, they offer reduced skid resistance due to fallen leaves; restrict direct sunlight on pavement surface, causing the formation of black ice and fog; and increase the risk of entire trees/branches/fruits falling on passing vehicles or blocking traffic lanes. State agencies have undertaken the task of eliminating tree canopies, inviting widespread criticism. Currently, there is a gap in the literature about the impact of tree canopy on traffic safety in the form of scientific research. This paper addresses this gap by presenting insight into the benefits of tree-trimming and/or removal operations along state routes in Ohio. Adopting the Empirical Bayesian approach, this study evaluates crashes to quantify the safety benefits. Additionally, a surrogate safety assessment was conducted to evaluate driver behaviors in the presence/absence of tree canopy. The results indicated that roadside tree trimming and pruning showed site-specific safety benefits at most locations, though no consistent project-level crash reduction or conclusive effects on surrogate safety measures, such as reduced speeds and hard braking, were observed. Full article

Net primary productivity (NPP) is an important indicator of ecosystem carbon accumulation capacity and vegetation productivity potential, and its accurate estimation is of great significance for agricultural management and regional carbon cycle research. To address the problem that the temporal continuity of single-source optical remote sensing data is easily affected by cloud cover, this study used Sentinel-2 imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as data sources and constructed an NDVI time series with high spatial and temporal resolution for the study area based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) method. On this basis, the Simple Ratio (SR) index was incorporated to supplement canopy information, and the key parameters of the Carnegie–Ames–Stanford Approach (CASA) model were differentially optimized for different crop types, thereby enabling remote sensing-based estimation of crop NPP. The results showed that the fused NDVI effectively compensated for observation gaps caused by cloud interference, and its temporal variation was generally consistent with the crop growth process. In addition, the Fraction of Photosynthetically Active Radiation (FPAR) improved with the fused NDVI, which effectively characterized phenological differences among crops. Compared with the unoptimized model, the improved model significantly improved NPP estimation accuracy for both maize and rice. Specifically, for maize, the coefficient of determination ($R^2$) increased from 0.75 to 0.88, and the mean absolute percentage error (MAPE) decreased from 67.00% to 34.68%. For rice, the MAPE decreased from 78.51% to 23.43%, while the mean absolute error (MAE) decreased from 345.1 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$ to 95.6 $\mathrm{gC}·{\mathrm{m}}^{-2}·{\mathrm{a}}^{-1}$. These results indicate that constructing a highly continuous vegetation index time series through spatiotemporal fusion, together with optimizing the CASA model by incorporating the SR index and crop-specific parameterization, can effectively improve the stability and accuracy of NPP estimation for agricultural crops. Full article

Airblast sprayers remain the dominant pesticide delivery system in California citrus; however, mechanistic characterization of spray transport and off-target fate under realistic field-scale atmospheric variability remains limited. Regulatory airblast drift assessments in the United States (U.S.) currently rely on a sparse, dormant-apple canopy representation, despite substantial structural differences from foliated citrus canopies that may influence drift behavior. To address this gap, this study quantified airblast spray drift in a commercial citrus orchard across multiple downwind distances under varied daytime meteorological conditions and evaluated the influence of distance and weather variables on measured drift. Airborne and sedimentation drift were measured from a conventional axial-fan airblast sprayer operating at 10.3 bar, 5.1 km·h⁻¹, and 935 L·ha⁻¹ in a 4.0 m tall mandarin (Citrus reticulata) orchard using a U.S. Environmental Protection Agency (EPA)-approved, International Organization for Standardization (ISO) standard 22866-aligned protocol. Drift collectors (n = 2688), including flat cards, artificial foliage, and horizontal and vertical string samplers, were deployed from 33 m upwind to 183 m downwind of the orchard edge. Airborne drift measurements showed no significant vertical stratification or near-field decay between 8 m and 23 m downwind (p > 0.05), indicating rapid plume homogenization following canopy exit. In contrast, sedimentation drift declined sharply within 30 m and attenuated logarithmically with distance, governed by progressive droplet depletion and plume dilution. Estimated drift cessation distances were 127.5 m for artificial foliage and 182.1 m for horizontal string samplers. Drift magnitude varied significantly among trials (p < 0.05), reflecting sensitivity to meteorological variability. Multiple linear regression identified wind direction, wind speed, and atmospheric pressure as significant predictors of downwind deposition (p < 0.05), whereas air temperature and relative humidity primarily influenced drift through evaporative control of droplet lifetime. Collectively, these results demonstrate that spray drift from foliated citrus canopies is substantially attenuated relative to dormant-canopy scenarios. Although not intended to define regulatory buffer distances, the high-resolution dataset generated provides mechanistically interpretable parameterization inputs for next-generation airblast drift models, supporting improved representation of canopy interactions, plume evolution, and meteorological modulation in regulatory exposure assessments. Full article

Accurate and reproducible delineation of stream bankfull geometry remains a persistent challenge in environmental planning. To address this gap, we developed the Cross-Sections and Dimensions Tool, a semi-automated, slope-based method for extracting stream cross-sections and estimating bankfull width, elevation and depth using high-resolution elevation data. The tool applies a configurable slope threshold to identify bank edges, generates perpendicular cross-sections from a stream centreline, and stores all outputs in a structured geodatabase to ensure transparency and reproducibility. Validation against manually delineated bankfull polygons across 191 km of stream length in Greater Melbourne, Australia, demonstrated strong spatial agreement, with an average F1 score (a measure of prediction-observation overlap) of 74% and a mean absolute error of 0.64 m in bankfull elevation. The tool was most reliable in larger streams (Strahler order 5 and above) with low to moderate vegetation canopy cover (<80%). We also investigated the practical visibility limits of small or indistinct channels typically encountered by human mappers and verified that the tool did not produce unrealistic channel delineations. This approach advances geomorphic feature extraction by grounding bankfull delineation in deterministic geometry rather than hydrological recurrence or data-driven modelling. In practice, it enables scalable, transparent, and repeatable analysis of stream morphology for ecological assessment, infrastructure planning, and waterway management. Full article

Freezing rain is a high-impact winter weather phenomenon that acts as a major disturbance agent in forest ecosystems, causing canopy damage, stem breakage, tree mortality, and long-term changes in forest structure and functioning. Although ice storms have been studied for decades, research on freezing rain impacts on forests remains fragmented across multiple disciplines, and few studies have attempted an integrated synthesis that simultaneously combines climatological, ecological, and methodological perspectives. In this study, we present a systematic and integrative review of the scientific literature on freezing rain and forests, combining a large-scale bibliometric analysis with an in-depth qualitative synthesis. A total of 241 publications retrieved from the Scopus and Web of Science databases were analyzed following PRISMA guidelines. The bibliometric assessment examined publication trends, geographic distribution, institutional contributions, research domains, and keyword networks. The qualitative review synthesized current knowledge on freezing rain climatology, forest damage mechanisms, species-specific vulnerability, major ice storm events, detection and modeling approaches, and ecological consequences. Results reveal a strong increase in scientific output over the last two decades, dominated by research from North America and northern Europe. Ice accretion intensity emerges as the primary driver of forest damage, while species traits, crown architecture, tree size, stand structure, topography, and exposure strongly modulate damage severity. Freezing rain affects a wide range of forest types worldwide and triggers both immediate structural damage and long-term ecological effects, including altered successional dynamics and reduced forest productivity. Recent methodological advances—including passive remote sensing (e.g., optical satellite data), active remote sensing (e.g., LiDAR), experimental ice storm simulations, reanalysis datasets, and machine learning approaches—have significantly improved detection, monitoring, and forecasting capabilities. Despite these advances, major research gaps remain, particularly regarding long-term ecosystem recovery, trait-based vulnerability, socio-economic impacts, and future freezing rain regimes under climate change. This review highlights freezing rain as an increasingly important but underappreciated forest disturbance and underscores the need for interdisciplinary research and adaptive management strategies in ice-prone regions. Full article

Soil moisture critically governs terrestrial energy and water cycles. Precise monitoring of soil water content is essential for precision agriculture, drought early warning, and water resource management. Ground-based observations offer limited spatial coverage, and satellite remote sensing generally lacks high spatial resolution. Unmanned aerial vehicle (UAV) remote sensing, which provides centimeter-level spatial detail, can effectively address this gap and has therefore attracted considerable attention in soil moisture inversion research. Using CiteSpace, we performed a bibliometric analysis of 97 Chinese papers from the China National Knowledge Infrastructure (CNKI) and 321 English papers from the Web of Science Core Collection (2014–2025). The field has expanded rapidly since 2018, with China occupying a leading role. Domestically, Northwest A&F University represents a major research cluster, while the Chinese Academy of Sciences leads internationally. Key research topics include UAVs, soil moisture, machine learning, hyperspectral sensing, canopy temperature, and precision agriculture. Research themes have progressed from reliance on vegetation indices and temperature data toward the integration of hyperspectral and thermal infrared measurements, and toward the use of machine learning approaches to improve inversion models and achieve more accurate estimations. This study delineates the classification and developmental context of a knowledge system for soil moisture inversion using UAV remote sensing. Current work concentrates on integrating multi-sensor data with machine learning, while future efforts will emphasize coupling physical mechanisms with deep learning. These findings offer researchers a clear view of the field’s frontiers and a basis for planning future studies. Full article

As fundamental units of forest ecosystems, individual trees provide essential structural characteristics for forest resource assessment. However, existing LiDAR-based individual tree segmentation methods are often limited by a trade-off between information preservation and computational efficiency. This study proposes a novel framework for individual tree segmentation from LiDAR data based on canopy surface points (CSP), aiming to balance this trade-off. The framework introduces a Snow-Covered Filter (SCF) that simulates snow deposition to extract surface points from the point cloud. After removing ground points from these surface points, the resulting CSP retains the core 3D structure of the canopy while significantly reducing data volume. We validate the proposed framework on four multi-platform datasets using four algorithms that represent the evolution of individual tree segmentation methods: Dalponte2016, K-means, Li2012, and SegmentAnyTree. The results demonstrate that: (a) the SCF effectively extracts surface points, with an average F1-score of 0.703; (b) segmentation using CSP achieves accuracy comparable to that obtained using all points or raster data (mean $\Delta F = 0.027$), with the primary gap observed for SegmentAnyTree (maximum F-score reduction of 0.259); (c) the framework offers substantial efficiency gains: >40% point reduction, ~38.4% average runtime reduction (maximum saving ~4660 s), and lower memory consumption. By providing a lightweight yet structurally rich data representation, this work presents an innovative and efficient approach to individual tree segmentation, with promising potential for large-scale forest resource management. Full article

The Muški bunar old-growth forest on Mount Psunj represents one of the rare preserved mixed ecosystems of sessile oak (Quercus petraea (Matt.) Liebl.) and European beech (Fagus sylvatica L.) in Southeastern Europe, providing an important reference for understanding natural forest dynamics. This study aimed to analyse stand structure, age distribution, growth dynamics, and disturbance regime based on repeated field surveys conducted in 1979 and 2021. The results revealed pronounced structural heterogeneity and clear interspecific differences. European beech dominates smaller- and medium-diameter classes, as well as a wider range of age classes, whereas sessile oak is primarily present in older and larger diameter classes. A very high growing stock (1155.81 m³ ha⁻¹) indicates exceptional stand productivity, with maximum cambial ages of 295 years for oak and 253 years for beech. Basal area increment analysis showed that both species maintain substantial growth at advanced ages. However, recent decades show divergence, with increasing growth in beech and stagnation or decline in oak. Importantly, growth releases in sessile oak were not accompanied by successful regeneration, indicating a decoupling between growth response and recruitment. Stand dynamics are mainly driven by low-intensity disturbances. These findings highlight the importance of old-growth forests as reference systems and improve understanding of species-specific responses to disturbance. Full article

Unmanned aerial vehicles (UAVs) are becoming an important platform for precision agriculture, supporting both high-throughput sensing and active field operations such as spraying, monitoring, and phenotyping. However, unlike general UAV applications, agricultural environments impose distinctive challenges due to heterogeneous field structures, canopy occlusion, terrain variation, dynamic disturbances, and strong coupling between navigation performance and task quality. To address this gap, this review presents a systematic analysis of UAV navigation in agricultural environments from a system-level perspective. The review first summarizes the core technical components of agricultural UAV navigation, including sensing, localization, mapping, planning, and control. It then discusses how navigation requirements vary across representative scenarios such as open fields, orchards, and terraced farmland, and examines their roles in key applications including aerial mapping, field monitoring, precision spraying, and close-range orchard operations. In addition, datasets, simulation platforms, and evaluation protocols relevant to agricultural UAV navigation are reviewed. Finally, major challenges are identified, including scene heterogeneity, perception degradation, insufficient task-semantic integration, limited control robustness, and the lack of standardized benchmarks. Future research should move toward robust, task-aware, and modular navigation architectures that support reliable and scalable agricultural UAV deployment. Full article

Urban trees are increasingly deployed as nature-based infrastructure to mitigate heat and manage stormwater, yet quantitative guidance on how species traits and site context shape transpiration remains fragmented. We conducted a systematic metadata analysis of seven field studies that measured daily transpiration rate in urban settings using heat-pulse methods. The units and spatial scales reported were harmonized with the sap flow density across active sapwood (J_s, g H₂O/${\mathrm{cm}}^2$/day) by converting reported stand transpiration and the outer 2 cm of sapwood sap flux using established Gaussian radial distribution functions for angiosperms and gymnosperms, which account for the non-linear decline in sap flux from the vascular cambium to the heartwood boundary. We then summarized distributions and tested group differences with Kruskal–Wallis and Dunn post hoc comparisons across wood anatomy, climate, soil texture, and planting configuration. Conifers exhibited significantly lower median $J_s$ (39.76 g/${\mathrm{cm}}^2$/day) than angiosperms, while the ring-porous group (median $J_s$ = 92.25 g/${\mathrm{cm}}^2$/day) and diffuse-porous groups (median $J_s$ = 96.70 g/${\mathrm{cm}}^2$/day) had similar distributions overall. Climate-modulated responses within wood anatomy groups differed, with diffuse-porous species exhibiting the highest median $J_s$ (152.59 g/${\mathrm{cm}}^2$/day) in semi-arid regions, ring-porous species maintaining comparatively stable median $J_s$ across climates (varying slightly between 80.72 and 99.32 g/${\mathrm{cm}}^2$/day), and conifers reaching their highest median $J_s$ (69.90 g/${\mathrm{cm}}^2$/day) in humid continental sites. Soil texture effects were consistent with moisture availability: sandy loam generally reduced $J_s$ relative to loam or silt loam for conifers and diffuse-porous species. Across anatomies, single trees transpired more than clustered trees or closed canopies. For example, planting as single trees increased median $J_s$ by 86% in conifers (from 33.01 to 61.37 g/${\mathrm{cm}}^2$/day) and by 45% in diffuse-porous species (from 81.31 to 118.25 g/${\mathrm{cm}}^2$/day). These results provide actionable ranges and contrasts to inform species selection and planting design for urban greening and runoff reduction, while highlighting data gaps for future research. Ultimately, by matching specific wood anatomies and planting configurations to local soil and climatic conditions, urban planners and ecohydrologists can strategically optimize urban forests to maximize targeted ecosystem services. Full article

Soybean is a vital crop supporting global food, feed, and biofuel production. Soybean yields have surged, with record yields reaching 14,678 kg/ha⁻¹, though average farm yields remain stagnant at 2770–2790 kg ha⁻¹. The persistent yield gaps leave 44% of potential production unrealized due to climate change, threatening food security. To meet future caloric demands, which are projected to rise by 46.8% by 2050, soybean breeding must prioritize climate-resilient, high-yielding varieties with minimal ecological footprints. In this comprehensive and in-depth review, we synthesized existing literature and Google Patents and reviewed the multifaceted impacts of climate-change driven eCO₂ and stresses (heat, drought, flooding, salinity, and pathogens), revealing non-linear interactions where eCO₂ may not compensate yield losses under combined stresses. We then highlight key strategies for soybean breeding under climate-change scenario. To this regard, we provide a detailed trait-by-trait breeding roadmap covering seed number, seed size, seed weight, protein-oil balance and their metabolic trade-offs, above and below ground plant architecture, nitrogen fixation and nodulation dynamics, root system architecture, water use efficiency, canopy architecture, flowering time regulation, early maturity etc., in light of specific genes and validated strategies. We explicitly discuss the novel strategies including deeper understanding of traits, abiotic stress physiology, changing pathogen dynamics, phenomics, (multi-)omics, machine learning, and modern biotechnological techniques for developing future soybean varieties. We provide a future roadmap prioritizing specific actions, including engineering climate-resilient ideotypes through gene stacking, optimizing nitrogen fixation and nutrition under stresses leveraging omics data, pan-genome, wild soybean, speeding breeding hubs, and participatory farmer-network validation, while redefining the future soybean breeder would be a hybrid orchestrator of data and dirt. This review establishes a foundational framework for translating climate-adaptive morphological, biochemical, physiological, omics, agronomic, phenomics, and biotechnological insights into actionable breeding strategies, thereby guiding policy-driven investment in soybean improvement programs targeting 2050 and beyond. Full article

As global urbanization accelerates, biophilic urbanism has emerged as a key nature-based strategy for enhancing public health. While plants are critical active agents for psychological restoration, the specific pathways through which vegetation characteristics influence human–environment interactions remain fragmented. This knowledge gap hinders the evidence-based translation of biophilic principles into actionable urban design and governance. This study conducts a systematic bibliometric analysis of 443 peer-reviewed articles (2000–2025) at the intersection of restorative landscapes, urban settings, and plant-based interventions retrieved from the Web of Science Core Collection. Employing multiple visualization tools (VOSviewer, bibliometrix, and CiteSpace), we map publication trends, international collaborations, and thematic evolution. The results demonstrate a significant shift in the field, moving beyond the validation of foundational restorative theories (e.g., ART and SRT) to a more precise, implementation-oriented framework. This shift is characterized by the operationalization of vegetation attributes as controllable design variables, increasingly relating biophilic principles to broader nature-based solutions (NbS) agendas and evidence-informed urban governance. Thematic clustering analysis identified three core knowledge domains: (1) the role of plants as active exposure agents and behavioral mediators in psychological restoration; (2) the impact of specific plant characteristics—such as canopy structure, species diversity, and seasonal variation—on therapeutic outcomes; and (3) the integration of urban green spaces into broader governance frameworks to promote health equity and inclusive well-being. Our analysis highlights that plant-based interventions are evolving from aesthetic ornaments into precision design levers for fostering human–nature interactions. This study provides a science-based foundation for developing practical design guidelines and policy frameworks, shifting biophilic urbanism toward a robust governance strategy for creating equitable, restorative, and resilient cities. Full article