Search Results (175)

Multi-vessel formation shipping demonstrates significant potential for enhancing maritime transportation efficiency and economy. However, existing route planning systems inadequately address the unique challenges of formations, where traditional methods fail to integrate global optimality, local dynamic obstacle avoidance, and formation coordination into a cohesive system. Global planning often neglects multi-ship collaborative constraints, while local methods disregard vessel maneuvering characteristics and formation stability. This paper proposes GLFM, a three-layer hierarchical framework (global optimization–local adjustment-formation collaboration module) for intelligent route planning of transport ship formations. GLFM integrates an improved multi-objective A* algorithm for global path optimization under dynamic meteorological and oceanographic (METOC) conditions and International Maritime Organization (IMO) safety regulations, with an enhanced Artificial Potential Field (APF) method incorporating ship safety domains for dynamic local obstacle avoidance. Formation, structural stability, and coordination are achieved through an improved leader–follower approach. Simulation results demonstrate that GLFM-generated trajectories significantly outperform conventional routes, reducing average risk level by 38.46% and voyage duration by 12.15%, while maintaining zero speed and period violation rates. Effective obstacle avoidance is achieved, with the leader vessel navigating optimized global waypoints and followers maintaining formation structure. The GLFM framework successfully balances global optimality with local responsiveness, enhances formation transportation efficiency and safety, and provides a comprehensive solution for intelligent route optimization in multi-constrained marine convoy operations. Full article

Coastal erosion, exacerbated by climate change, poses a critical global threat to both the environment and human livelihoods. Acquiring accurate, high-resolution topo-bathymetric data is vital for understanding these dynamic environments, without underestimating the hydrodynamic and meteo-oceanographic conditions. However, traditional methods often present significant challenges in achieving comprehensive, high-resolution topo-bathymetric coverage efficiently in shallow coastal zones, leading to a notable ”white ribbon” data gap. This study introduces a novel, integrated methodology combining unmanned aerial vehicles (UAVs) for terrestrial surveys, unmanned surface vehicles (USVs) for bathymetry, and the Global Navigation Satellite System (GNSS) for ground control and intertidal gap-filling. Through this technologically rigorous approach, a seamless Bathymetry-Topography Digital Surface Model for the Guardamar del Segura dune system (Spain) was successfully elaborated using a DJI Mini 2 UAV, Leica Zeno FLX100 GNSS, and Apache 3 USV. The method demonstrated a substantial time reduction of at least 50–75% for comparable high-resolution coverage, efficiently completing the 86.4 ha field campaign in approximately 4 h. This integrated approach offers an accessible and highly efficient solution for generating detailed coastal elevation models crucial for coastal management and research. Full article

Underwater towed systems (UTSs) are widely used in underwater exploration and oceanographic data acquisition. However, the heave motion information of the towing ship is usually affected by the measurement transmitting delay, sensor noise and surface waves, which will result in uncontrolled depth variation of the towed vehicle, so as to adversely affect the monitoring performance and mechanical robustness of the UTS. To resolve this problem, a heave motion prediction approach based on sparse Bayesian learning (SBL) incorporated with empirical mode decomposition (EMD) for the UTS is proposed in this paper. With the proposed approach, a heave motion model of the towing ship with random waves is firstly developed based on strip theory. Meanwhile, the EMD is employed to eliminate the high-frequency noise of the measurement data to restore low-frequency towing ship motion. And then, the SBL is utilized to train the weight parameters in the built model to predict the heave motion, which not only reconstruct the heave motion from non-stationary sensor signals with noise but also prevent overfitting. Furthermore, the depth compensation of the towed vehicle is then performed using the predicted heave motion. Finally, experimental results demonstrate that the proposed EMD-SBL method significantly improves both the prediction accuracy and model adaptability under various sea conditions, and it also guarantees that the maximum prediction depth error of the heave motion does not exceed 1 cm. Full article

This research addresses the increasing vulnerability of coastal urban areas to climate change, with a particular focus on the increased risk of overtopping and coastal flooding events in Praia da Vitória, Azores. This study, conducted within the LIFE-GARACHICO project, aims to develop a holistic and flexible management approach to coastal flood risk. The methodology included a comprehensive risk assessment that combined a vulnerability analysis (considering factors such as population, land use, and infrastructure) with the probability of coastal inundation events (using oceanographic data and models). Public risk perception was assessed through surveys to understand residents’ awareness and preferences. A response protocol for overtopping events was developed with civil protection officials, and the HIDRALERTA early warning system was implemented. The risk assessment showed a mostly acceptable risk with some undesirable areas, resulting from a mostly low probability and medium vulnerability. Public surveys indicated that residents had experienced flooding but felt unprepared and preferred softer management measures such as access restrictions and early warning systems. The response protocol developed and integrated into HIDRALERTA responds to these preferences. This study concludes that this integrated framework improves coastal risk management, increases public awareness and confidence, and provides a flexible and sustainable model for coastal risk management. Full article

Sea surface height (SSH) serves as a fundamental geophysical parameter in oceanographic research. In 2023, China successfully launched the world’s first spaceborne interferometric GNSS-R (iGNSS-R) altimeter, which features dual-frequency multi-beam scanning, interferometric processing, and compatibility with three major satellite navigation systems: the BeiDou Navigation Satellite System (BDS), the Global Positioning System (GPS), and the Galileo Satellite Navigation System (GAL). This launch marked the first in-orbit validation of the iGNSS-R altimetry technology. This study provides a detailed overview of the iGNSS-R payload design and analyzes its dual-frequency delay mapping (DM) measurements. We developed a refined DM waveform-matching algorithm that precisely extracts the propagation delays between reflected and direct GNSS signals, enabling the retrieval of global sea surface height (SSH) through the interferometric altimetry model. For validation, we employed an inter-satellite crossover approach using Jason-3 and Sentinel-6 radar altimetry as references, achieving an unprecedented SSH accuracy of 17.2 cm at a 40 km resolution. This represents a breakthrough improvement over previous GNSS-R altimetry efforts. The successful demonstration of iGNSS-R technology opens up new possibilities for cost-effective, wide-swath sea level monitoring. It showcases the potential of GNSS-R technology to complement existing ocean observation systems and enhance our understanding of global sea surface dynamics. Full article

This work presents a high-speed hybrid communication system integrating Underwater Optical Wireless Communication (UOWC), Multimode Fiber (MMF), and Free-Space Optics (FSO) channels, leveraging Orbital Angular Momentum (OAM) beams for enhanced data transmission. A Photodetector, Remodulate, and Forward Relay (PRFR) is employed to enable wavelength conversion from 532 nm for UOWC to 1550 nm for MMF and FSO links. Four distinct OAM beams, each supporting a 5 Gbps data rate, are utilized to evaluate the system’s performance under two scenarios. The first scenario investigates the effects of absorption and scattering in five water types on underwater transmission range, while maintaining fixed MMF length and FSO link. The second scenario examines varying FSO propagation distances under different fog conditions, with a consistent underwater link length. Results demonstrate that water and atmospheric attenuation significantly impact transmission range and received optical power. The proposed hybrid system ensures reliable data transmission with a maximum overall transmission distance of 1125 m (comprising a 25 m UOWC link in Pure Sea (PS) water, a 100 m MMF span, and a 1000 m FSO range in clear weather) in the first scenario. In the second scenario, under Light Fog (LF) conditions, the system achieves a longer reach of up to 2020 m (20 m UOWC link + 100 m MMF span + 1900 m FSO range), maintaining a BER ≤ 10⁻⁴ and a Q-factor around 4. This hybrid design is well suited for applications such as oceanographic research, offshore monitoring, and the Internet of Underwater Things (IoUT), enabling efficient data transfer between underwater nodes and surface stations. Full article

The Gulf of Mexico (GoM) is a complex oceanic basin with a maximum depth of 4000 m. It is a complex hydrodynamic system formed by different water masses with distinctive physical and biological characteristics that shape its rich biodiversity. In this study, as a contribution to better understanding the microbial communities inhabiting the meso- and bathypelagic zones of the Mexican Exclusive Economic Zone (EEZ) of the GoM, an extensive set of seawater samples was collected at three depths (350–3700 m) during three oceanographic cruises. The V4-16S rRNA gene analysis identified Pseudomonadota (27.1 ± 9.8%) and Nitrosopumilales (26.4 ± 2.3%) as the dominant bacterial and archaeal members, respectively. The depth, salinity, and apparent oxygen utilization were key environmental drivers, which explained 35% of the community variability. The mesopelagic zone presented a more homogeneous structure characterized by a nitrifier community, while the bathypelagic was more heterogeneous, with hydrocarbon-degrading bacteria and methanogens serving as the key players. This study is the first to report the archaeal community in the deeper waters of the Mexican EEZ of the GoM, playing crucial roles in the nitrogen and carbon cycles, highlighting the region’s ecological complexity and the need for further research to understand the broader biogeochemical implications of these processes. Full article

Ports function as logistical hubs through which approximately 80% of the world’s goods are transported annually. Recent regulatory frameworks from the International Maritime Organization (IMO) and the European Union require ships and ports to adopt measures aimed at minimizing the environmental impact of port activities and mitigate climate change. These measures include investing in renewable energy generation systems to transition from fossil fuel-based energy to renewable electricity. Consequently, to meet increasing energy demands, new energy infrastructure must be developed. However, due to spatial constraints in port environments, there is a growing interest in utilizing port service areas, inner docks, and exterior/adjacent water zones for the deployment of marine renewable energy generation systems. This study applies high-resolution meteorological and oceanographic modelling—incorporating validated wave agitation models—to assess the feasibility of integrating marine renewable energy generation within port service areas. Full article

Global Navigation Satellite Systems (GNSSs) are the primary source of information for Positioning, Navigation, and Timing (PNT) in the maritime sector; however, they are vulnerable to unintentional or deliberate interference, such as jamming, spoofing, or meaconing. The continuous monitoring of GNSS signals is crucial for vessels and mobile maritime platforms to ensure the integrity, availability, and accuracy of positioning and navigation services. This monitoring is essential for guaranteeing the safety and security of navigation and contributes to the accurate positioning of vessels and platforms involved in hydrographic and oceanographic research. This paper presents the implementation of a complex system for monitoring the quality of signals within the GNSS spectrum at the Maritime Hydrographic Directorate (MHD). The system provides real-time analysis of signal parameters from various GNSSs, enabling alerts in critical situations and generating statistics and reports. It comprises four permanent stations equipped with state-of-the-art GNSS receivers, which integrate a spectrum analyzer and store raw data for post-processing. The system also includes software for monitoring the GNSS spectrum, detecting interference events, and visualizing signal quality data. Implemented using a Docker-based platform to enable efficient management and distribution, the software architecture consists of a reverse proxy, message broker, front-end, authorization service, GNSS orchestrator, and GNSS monitoring module. This system enhances the quality of command, control, communications, and intelligence decisions for planning and execution. It has demonstrated a high success rate in detecting and localizing jamming and spoofing events, thereby improving maritime situational awareness and navigational safety. Future development could involve installing dedicated stations to locate interference sources. Full article

The Pliensbachian–Toarcian boundary and early Toarcian events indicate significant environmental and oceanographic instabilities attributed to the emplacement of the Karoo–Ferrar large igneous province and subsequent greenhouse gas emissions. These geologic processes influenced carbon cycle perturbations and global warming, consistent with phases of a sea level rise. This study presents a high-resolution dataset of total organic carbon (TOC) and bulk rock geochemistry and mineralogy from a complete upper Pliensbachian–Toarcian interval of the Quse Formation at the Qixiangcuo section in the Southern Qiangtang Basin. The Qixiangcuo section consists of carbonate and siliciclastic organic carbon-poor sediments deposited in a shallow-shelf setting in the eastern Tethys Ocean. Chemostratigraphic data, including Ti, Zr, U, Ca, Mn, and Sr and their ratios normalized to Al, record characteristic changes linked to sea level evolution and resulting depositional sequences. Trends in these geochemical data allow for the subdivision of the Quse Formation into nine complete third-order transgressive–regressive sequences, referred to as Pliensbachian sequences PSQ1 and PSQ2, Toarcian sequences TSQ1 to TSQ7, and one incomplete sequence. Elemental proxies indicative of terrigenous detrital input and sediment grain size along with a mineralogical composition of quartz, plagioclase, and clay minerals exhibit similar trends. Increased values of these proxies suggest a sea level fall and the deposition of regressive systems tract (RST) sediments, with peak values indicating a maximum regressive surface (MRS), and vice versa for transgressive systems tract (TST) sediments and the maximum flooding surface (MFS). On the contrary, rising trends in calcite content and carbonate-bound elements indicate phases of a relative sea level transgression, reaching maximum values at the MFS, while declining trends mark a sea level regression. The Sr/Ca ratio exhibited inverse patterns to the carbonate proxies, in part, with rising values indicating a sea level fall and vice versa. Full article

Oil spills represent a critical environmental hazard with far-reaching ecological and economic consequences, necessitating the development of sophisticated modelling approaches to predict, monitor, and mitigate their impacts. This study presents a computationally efficient and physically grounded modelling framework for simulating oil spill drift in marine environments, developed using Python coding. The proposed model integrates core physical processes—advection, diffusion, and degradation—within a simplified partial differential equation system, employing an integrator for numerical simulation. Building on recent advances in marine pollution modelling, the study incorporates real-time oceanographic data, satellite-based remote sensing, and subsurface dispersion dynamics into an enriched version of the simulation. The research is structured in two phases: (1) the development of a minimalist Python model to validate fundamental oil transport behaviours, and (2) the implementation of a comprehensive, multi-layered simulation that includes NOAA ocean currents, 3D vertical mixing, and support for inland and chemical spill modelling. The results confirm the model’s ability to reproduce realistic oil spill trajectories, diffusion patterns, and biodegradation effects under variable environmental conditions. The proposed framework demonstrates strong potential for real-time decision support in oil spill response, coastal protection, and environmental policy-making. This paperwork contributes to the field by bridging theoretical modelling with practical response needs, offering a scalable and adaptable tool for marine pollution forecasting. Future extensions may incorporate deep learning algorithms and high-resolution sensor data to further enhance predictive accuracy and operational readiness. Full article

The need for real-time and scalable oceanographic monitoring has become crucial for coastal management, marine traffic control and environmental sustainability. This study investigates the integration of sensor technology into marine cables to enable real-time monitoring, focusing on tidal cycles and wave characteristics. A 2000 m cable demonstrator was deployed off the coast of Portugal, featuring three active repeater nodes equipped with pressure sensors at varying depths. The goal was to estimate hourly wave periods using fast Fourier transform and calculate significant wave height via a custom peak detection algorithm. The results showed strong coherence with tidal depth variations, with wave period estimates closely aligning with forecasts. The wave height estimations exhibited a clear relationship with tidal cycles, which demonstrates the system’s sensitivity to coastal hydrodynamics, a factor that numerical models designed for open waters often fail to capture. The study also highlights challenges in deep-water monitoring, such as signal attenuation and the need for high sampling rates. Overall, this research emphasises the scalability of sensor-integrated smart marine cables, offering a transformative opportunity to expand oceanographic monitoring capabilities. The findings open the door for future real-time ocean monitoring systems that can deliver valuable insights for coastal management, environmental monitoring and scientific research. Full article

The validation of HF radar systems remains an area with significant scope for advancement, particularly in terms of linking data quality with system operational parameters, fully utilizing the potential of redundant data (e.g., overlapping radial measurements), and accurately capturing the spatiotemporal variability observed by independent devices, such as drifters. In this study, we conducted a large-scale Lagrangian measurement campaign in the Tuscan Archipelago, aimed at validating surface current data from the HF radar network. This radar network, a recent addition to the area, monitors an oceanographic region critical to Mediterranean dynamics. The validation was executed using different approaches: a Eulerian method, comparing the radial velocities measured by radar with drifter-derived velocities along radial directions; a Lagrangian method, contrasting the observed drifter trajectories with the synthetic virtual trajectories generated from radar-based flow fields; and radar-to-radar comparisons with the concurrent utilization of two radars in same point. Through fine-tuning of the quality control parameters and an analysis of the impact of different thresholds of such parameters, we assessed the radar’s ability to capture dynamic processes, identifying both strengths and limitations. Our results not only confirm the utility of HF radar in coastal monitoring but also provide a basis for improving calibration strategies, ultimately supporting more accurate, high-resolution radar observations in complex marine environments. Full article

This study evaluates the measurement uncertainty of the wave buoy calibration device using a vertical lifting method to ensure the accuracy and reliability of wave buoy measurements for marine research. The calibration device employs a linear motor-driven vertical displacement system, integrating a standard steel tape for wave height measurement and a photoelectric switch-based time calibration module for wave period verification. To address the limitations of traditional instruments, the device utilizes a 0.1 mm laser beam and image processing software to enhance the resolution of the standard steel tape, reducing the smallest division measurement from 1 mm to 0.1 mm. Additionally, a high-precision time calibration method synchronizes the time of the motor’s upper computer software and a frequency meter, minimizing indication error. Key uncertainty sources, including repeatability, environmental temperature effects, and the smallest division measure of instrument, were systematically analyzed. Results demonstrate that the extended measurement uncertainty (k = 2) for wave heights of 0.03 m and 40 m are 0.058 mm and 1.088 mm, respectively, while the uncertainty for a 30 s wave period is 3 ms. These values meet the stringent accuracy requirements (0.5% of measured values) for calibrating advanced wave buoys like the Directional Waverider 4. The proposed device provides a robust solution for validating wave buoy performance, offering significant practical value for oceanographic studies and coastal engineering applications. Full article

This study develops an autonomous ocean observation system designed for continuous, multidimensional marine parameter monitoring. The system integrates sensor-based monitoring and sample collection capabilities, utilizing tidal energy to facilitate vertical movement within the water column (0–50 m). The system combines tidal energy utilization with a buoyancy regulation unit, significantly reducing reliance on conventional battery power while maintaining the system’s flexibility in deep control, demonstrating superior energy efficiency compared to traditional platforms. The combination of sensor monitoring and sample acquisition enables real-time acquisition of oceanographic parameters (e.g., temperature, salinity, chlorophyll) and on-demand water sample collection for high-precision laboratory analysis. Laboratory and sea trials validated its ability to perform reciprocating vertical motion, autonomous buoyancy regulation, and leak-free sample collection, confirming feasibility for long-term coastal ecosystem monitoring. This study highlights the potential of autonomous systems for sustainable ocean observation and environmental monitoring. Full article