Search Results (265)

As a core piece of equipment for marine monitoring, search and rescue missions, and other applications, the motion state prediction accuracy of Unmanned Surface Vehicles (USVs) directly determines mission reliability and safety. However, existing methods fail to fully consider the motion characteristic differences in various vessel sizes and variable-speed navigation under complex sea conditions, and struggle to capture the spatiotemporal dynamic features of state variations. This paper proposes a hybrid prediction algorithm based on Random Forest-Long Short-Term Memory (RF-LSTM), which utilizes Random Forest for key feature selection while employing LSTM to excavate temporal correlations. An intelligent routing mechanism based on the dominant frequency energy ratio (P_d) is introduced to achieve adaptive prediction mode switching, enabling comprehensive characterization of state variations. Under the 20 kn high-speed condition of a 7.5 m USV, the proposed algorithm achieves a Circular RMSE for heading prediction that is 1.9 times lower than the Extended Kalman Filter (EKF) and 1.2 times lower than a standalone LSTM, with pitch and roll prediction RMSE reduced to 0.36° and 0.85°, respectively. On a 14.5 m-long USV at 23 kn, it maintains a heading prediction accuracy of 0.10°, verifying favorable scale generalization capability. Furthermore, the algorithm demonstrates strong robustness against Gaussian white noise and synthetic ocean noise. Experimental results indicate that RF-LSTM significantly outperforms traditional methods, effectively breaking through the application limitations of fixed-architecture models, substantially enhancing USV autonomy and adaptability in complex marine environments, and providing robust guarantees for mission reliability and safety. Full article

Glaciers are critical indicators of global climate change, and their accelerated retreat has profound implications for sea-level rise, water resources, and ecosystem stability. Accurate and timely mapping of glacier extent is essential for monitoring these changes. This review provides a comprehensive overview of the evolution of remote sensing techniques for glacier mapping, charting the progression from traditional spectral indices to the current state-of-the-art machine learning (ML) and deep learning (DL) models. We analyze the strengths and limitations of various methods, including the computational efficiency of indices like the Normalized Difference Snow Index (NDSI), the classificatory power of ML algorithms like Random Forest (RF), and the superior performance of DL architectures, particularly U-Net and its variants, for semantic segmentation of glacier mapping. Our analysis highlights a clear trend towards automated, data-driven approaches that have significantly enhanced the accuracy and scale of glacier delineation. However, progress is slowed by key challenges, most importantly the difficulty in getting accurate ‘ground truth’ data due to a lack of standardized, high-resolution training and validation datasets. Other key limitations include an over-reliance on a few model architectures and the need to bridge the gap between research-level accuracy and operational, real-time monitoring systems. Future progress in the field will depend on community-led efforts to create robust benchmark datasets, explore more diverse and efficient model architecture, develop sophisticated data fusion techniques, and improve model transferability and uncertainty quantification. By integrating cutting-edge AI with improved data practices, the remote sensing community can deliver the crucial data needed to understand and respond to the impacts of a changing climate. Full article

Field measurements of ship motions at berth are often sparse, heterogeneous, and collected across multiple vessels and locations, limiting the applicability of conventional response-modelling approaches. This study presents a statistical framework for analysing sea-state-conditioned motion responses using long-term monitoring data with incomplete overlap between degrees of freedom (DoF). Each DoF is analysed independently and conditioned on significant wave height ($H_s$) and peak wave period ($T_p$), with directional values retained across the full angular range (0–360°) and examined separately. A two-stage quality-control procedure combining plausibility checks and robust regression removes inconsistent response–sea-state pairs while preserving dominant behaviour. Motion response envelopes are derived by binning observations in sea-state space and computing median and upper-percentile statistics. To quantify sampling uncertainty, bootstrap resampling provides 95% confidence intervals for envelopes and derived metrics, ensuring robust comparative conclusions. Results show systematic growth in motion variability with increasing $H_s$, with surge exhibiting the strongest translational sensitivity and roll the largest amplification. Synthetic sea surfaces generated using a spectral random-phase approach reproduce prescribed sea-state characteristics, supporting physical interpretation. The study contributes a data-driven framework for heterogeneous berth datasets, robust quality control, uncertainty-aware response envelopes, and statistically consistent synthetic seas, aligning field-based monitoring with practical port operability assessment. Full article

Riverine systems in tropical deltaic environments are increasingly exposed to hydrological variability driven by climate change, sea level rise, and extreme precipitation. In Nigeria’s Niger Delta, recurrent flooding and environmental degradation are intensifying pressures on freshwater ecosystems and dependent communities. This study examines hydrological stressors in riverine settlements of Bayelsa State and explores associated socio-ecological responses. Using an exploratory qualitative design, data were collected from 51 women residing in highly vulnerable riverine communities through 24 in-depth interviews and three focus group discussions. Thematic analysis identified prolonged flooding, riverbank erosion, salinity intrusion, water quality deterioration, and oil pollution, as key drivers of declining fisheries, reduced agricultural productivity, and household water insecurity. These stressors have prompted relocation, livelihood diversification, and reliance on indigenous adaptation practices. The study recommends: (1) installation of community-based flood early warning systems; (2) routine monitoring of surface water quality and salinity; (3) enforcement of oil spill remediation and pollution control measures; (4) rehabilitation of wetlands and natural drainage channels; and (5) targeted support for climate-resilient livelihoods such as aquaculture and elevated farming systems. These measures are critical for sustaining freshwater ecosystems and strengthening resilience in vulnerable deltaic communities. Full article

Effective operation and maintenance (O&M) of deep-sea observation networks are challenged by complex environments and energy limitations. While digital twin (DT) technology offers promising solutions, existing frameworks struggle with high-fidelity, multi-platform orchestration and predictions of electrical energy state. This study proposes a DT framework for a deep-sea observation network (DSON-DT), encompassing telemetry acquisition, predictive analytics, and feedback control to realize a closed-loop workflow for monitoring and managing platform states within virtual scenes. Powered by real-time Internet of underwater things (IoUT) data, a high-fidelity virtual environment is constructed in the Unreal Engine 5 game engine, accurately mapping ambient marine environments and reconstructing platform dynamic behaviors via data-driven approaches and geometric constraints. An improved auto-regressive long short-term memory (AR-LSTM) network is proposed to forecast the battery state of charge (SoC). Experimental results show that this algorithm effectively mitigates the impacts of severe deep-sea noise and the flat open-circuit voltage plateau, suppressing state oscillations to provide reliable references for proactive endurance management. The Vue.js-based web prototype, deployed via pixel streaming, offers seamless interfaces for interactive visualization, analysis, and remote operation. This research achieves comprehensive situational awareness for deep-sea platforms, providing validated technical support for the holistic evaluation and intelligent O&M of heterogeneous marine infrastructures. Full article

Climate change is modifying the physical structure and biogeochemical functioning of stratified marine systems, with important consequences for trace element (TE) transport, speciation, and exposure. The Black Sea provides a structurally amplified case because restricted exchange, persistent stratification, a basin-scale redoxcline, and extensive shelf-sediment reservoirs intensify climate–contaminant interactions. This review synthesizes mechanistic evidence to develop a climate-informed interpretive framework for TE redistribution under non-stationary environmental forcing. We examine how warming, deoxygenation, hydrological variability, sediment resuspension, acidification, and episodic events alter TE partitioning across dissolved, particulate, sedimentary, and biotic compartments. The synthesis identifies six major redistribution pathways involving surface-layer retention, river plume and suspended particulate transport, shelf-sediment remobilization, redoxcline dynamics, acidification–ligand effects, and event-driven exposure pulses. Together, these processes show that TE patterns increasingly reflect state-dependent internal redistribution rather than external loading alone. To address this shift, we propose a monitoring and risk-interpretation framework that links climate-sensitive state variables to redistribution pathways, integrates multiple matrices, and supports adaptive assessment through trigger-based monitoring escalation. The Black Sea is treated as a structurally amplified reference system for examining climate-sensitive redistribution pathways in stratified basins, although their expression and relative importance remain dependent on basin-specific structural controls. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

Offshore wind energy has developed rapidly in recent years as a crucial component of renewable energy. However, offshore wind turbines (OWTs) face significant challenges in operations under complex marine environmental conditions, such as multimodal nonlinear vibrations, reliable structural monitoring, efficient maintenance, and sustainable long-term operations. The model-updating-based parameter identification takes advantages of structural vibration measurements, assisting in structural health monitoring. However, the traditional methods have not fully accounted for the parameter uncertainties and the need for real-time state updating, making them insufficient to meet the long-term online monitoring requirements for OWTs. This study introduces an innovative structural parameter identification framework that integrates modal parameter identification with Bayesian recursive updating. The proposed framework enables more efficient updates and uncertainty quantification of critical physical parameters for OWTs. It combines the covariance-driven stochastic subspace identification (COV-SSI) method for automatic modal parameter identification with the unscented Kalman filter (UKF) for parameter estimation. A 10 MW jacket-type offshore wind turbine was used as a case study. First, the numerical simulations were conducted to generate synthetic measurements for method validation and demonstration, enabling stepwise updating of the tower material’s elastic modulus across different sea conditions. A comparison of update speed and the convergence rate with the traditional time-step-based UKF method demonstrated the superiority of the proposed sea-condition-based approach in terms of computational efficiency and stability. Finally, the proposed framework was systematically validated using scaled model experimental data of a jacket-type OWT with a 4.2% identification error, confirming its engineering applicability. This research provides reliable technical support for the safety assessment of offshore wind turbine structures. Full article

In recent decades, the issue of marine litter has emerged as a major environmental concern, particularly with regard to plastic litter. The European Marine Strategy Framework Directive (MSFD, 2008/56/EC) requires member states to monitor marine litter along the coast, in the water, and on the seabed. Since 2014, beach litter monitoring has been carried out in Italy’s coastal regions, an activity entrusted to the Regional Environmental Agencies System (ARPA). ARPA Puglia is responsible for monitoring the Apulian coastline, and this paper summarizes the main results obtained from 2014 to 2023. The monitoring, which was repeated twice a year, consists of a visual census of litter items along a 100-meter stretch of beach in six different locations across the Puglia region. During this period, an average of 506 litter items per 100 m were observed on the six target beaches in Puglia, 90% of which were plastic ones. Among these, single-use plastic items (SUPs) accounted for 37%. A trend analysis reveals a decline in the aggregate quantity of marine litter on Apulian beaches over the past decade, a phenomenon that is particularly evident when considering the SUP subcategory in isolation. This decreasing trend is consistent with the overall pattern observed along the Italian coastline and the coastlines of European seas. Consequently, it can be hypothesized that an increase in awareness of the issue, in conjunction with the implementation of European Directive 2019/904 for the reduction in single-use plastics, has resulted in more responsible practices. However, further efforts are needed to achieve the goal of 20 litter items per 100 m of beach to attain the Good Environmental Status under the Marine Strategy Framework Directive. The findings emphasize the importance of constant monitoring of litter items along the shoreline, as well as the integration of new and alternative methodologies (e.g., drone surveys) to evaluate the efficacy of European regulatory implementation. Full article

A comparative analysis of the long-term dynamics of phytobenthos on the Black Sea coast from 1964 to 2020 has been conducted. The aim of the work was to assess changes in species composition, quantittive characteristics, and distribution of bottom vegetation under the influence of natural and anthropogenic factors. The research was carried out at three transects using standard hydrobotanical methods and analysis of climatic data. The results revealed significant structural reorganization of the communities: a decrease in the proportion of key brown algae (Ericaria crinita and Gongolaria barbata) by the middle of the observation period with partial recovery by 2020, an overall increase in biomass and species diversity, and increased role of epiphytes and green algae. An expansion of the depth range of the phytal zone and an increase in the presence of the deep-water species Phyllophora crispa were established. The main drivers of the transformation are increased anthropogenic pressure and climate change, which aligns with global trends. The obtained data are important for developing measures to preserve coastal ecosystems and can be used in monitoring the ecological state of the aquatic area. A promising direction for further research is the quantitative assessment of the role of the macrophytobenthos in this area in carbon sequestration. Full article

Wind-driven surface processes are a major source of underwater ambient sound and are therefore an important component of coastal soundscapes. Yet their frequency-dependent expression in shallow nearshore reef environments remains insufficiently characterized from field observations. This study investigates low-to-mid-frequency (20–1000 Hz) ambient acoustic variability at Faro’s natural reef (southern Portugal) using short-term passive acoustic monitoring combined with concurrent sea state measurements. The results show evidence of a relationship between frequency-dependent acoustic response and wind-driven surface processes. At frequencies of 20–100 Hz, ambient sound levels exhibit a weak relationship with wind-driven surface conditions, with elevated variability under low agitation. This is attributed to persistent background anthropogenic noise, particularly vessel traffic. In contrast, above 100 Hz, the ambient sound level increases consistently with wind-driven agitation, indicating that wind-driven surface processes dominate ambient sound in the 100–1000 Hz frequency range. Transient high-energy peaks increase in frequency and intensity with surface agitation, consistent with breaking-wave events, even though elevated background sound levels persist after peak removal. These findings demonstrate that wind-related ambient sound variability at Faro’s natural reef is robustly expressed above approximately 100 Hz. This highlights the importance of frequency-dependent interpretation in passive acoustic monitoring as a necessary baseline for assessing the nearshore reef environment’s influence on ambient sound levels and acoustic propagation under variable sea state conditions. Full article

This study quantifies the sustainable development thresholds of marine ecosystems under high-intensity human development by establishing a composite evaluation framework based on the Pressure–State–Response (PSR) model. Taking the Nantong sea area as a typical study region, this research indicates that prior to large-scale development (2006–2010), the comprehensive carrying capacity was higher in the northern region than in the south. The lowest capacity was observed near the Yangtze River Estuary, while the Subei Radial Sand Ridges in the north exhibited the highest capacity. Following the period of intensive coastal development (2016–2020), a significant decline in composite marine carrying capacity occurred in the northern radial sand ridge area, whereas the central waters remained stable. The nearshore areas in the south exhibited the poorest capacity. Despite a substantial increase in anthropogenic pressure, the overall decline of the sea area’s composite marine carrying capacity remains within an acceptable range, with all levels categorized as “Near Carrying Capacity” or above. Quantitative assessment of marine environmental carrying capacity and marine ecological carrying capacity provides an effective pathway for monitoring the specific status of the marine environment and determining whether critical thresholds have been reached under high-intensity human development scenarios. Full article

Sea-surface chlorophyll-a concentration (Chl-a) is a core indicator reflecting phytoplankton biomass and marine ecological conditions. Its spatiotemporal variation patterns are closely related to environmental changes and human activities, especially in coastal waters around heavily populated areas, e.g., the Bohai Sea in China. Benefiting from long time-series ocean-color (i.e., Chl-a provided by Aqua-MODIS) multi-source merged sea surface temperature (SST) and wind speed (i.e., ERA5) and dissolved inorganic nitrogen concentration (DIN) data, this study investigated the long-term variation characteristics of Chl-a in the Bohai Sea and its influencing factors during the period of 2003 to 2022. After rigorous quality control and data reconstruction, this study analyzed the interannual, seasonal, and spatial variation patterns of Chl-a in the Bohai Sea across five ecological functional subregions (Bohai Bay, the Qinhuangdao coast, Liaodong Bay, Laizhou Bay, and the central Bohai Sea), and explored the influence of SST, wind speed, and DIN on variations in Chl-a. The results showed that the spatial distribution of Chl-a in the Bohai Sea exhibited a significant coastal–offshore gradient, with higher concentrations in coastal bays and the Qinhuangdao coast and lower concentrations in the central Bohai Sea. Temporally, despite a long-term trend of first increasing and then decreasing with a peak around 2011, Chl-a underwent a significant regime shift around 2015. After the shift, the average concentration decreased by 0.36 mg/m³ compared with that before the shift. On a seasonal scale, the average Chl-a concentration over the whole Bohai showed the largest decrease in summer (−0.65 mg/m³) and the smallest decrease in winter (−0.21 mg/m³), with contrasting changes among subregions: the Qinhuangdao coast had the most significant decrease (−1.54 mg/m³), while Laizhou Bay remained basically stable. Driver mechanism analysis indicated that Chl-a in the Bohai Sea was significantly negatively correlated with SST (r = −0.51, p = 0.022) and significantly negatively correlated with wind speed (r = −0.77, p < 0.01). Furthermore, both SST and wind speed have undergone significant regime shifts toward a warmer and a windier state, respectively. The timing of these climatic shifts coincided with or preceded the Chl-a regime shift, which may help suppress phytoplankton blooms and maintain lower Chl-a levels. In addition, the surface DIN concentration in Bohai Bay decreased by 23.6% after the Chl-a regime shift, indicating a reduction in nutrient input may be responsible for the decrease in Chl-a in this region. The research results reveal the long-term variation patterns and multi-factor synergistic regulatory mechanism of Chl-a in the Bohai Sea, providing a scientific reference for red-tide monitoring and early warning as well as regional ecological environment management in the Bohai Sea. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

Drones are increasingly used in marine science for detecting and monitoring large megafauna in nearshore areas. Remotely operated, autonomous drone missions have the potential to improve the overall efficiency of drone-based research. We assessed the utility of autonomous drone operations by comparing real-time detection rates of marine megafauna (i.e., dolphins, rays, sharks, turtles) between a remotely operated Drone-In-A-Box (DIAB) system using pre-programmed missions and standard site-operated manual flight procedures. Megafauna were identified in real time during each drone mission, and missed detections were quantified through post-analysis of drone footage. A total of 71 missions were completed, with autonomous and manual flights operating concurrently at either 60 m or 80 m altitude, and a flight speed of 8 m/s. There were 107 and 117 real-time megafauna observations recorded for autonomous and manual operations, respectively. Post-flight analysis determined an overall missed detection of 52.4% for autonomous and 30.4% for manual operations, with undercounting higher for autonomous operations across all faunal groups. Dolphin detection in real time had the highest agreement with post-flight analysis, while real-time turtle detection proved the most difficult. Cloud cover, sea state, time of day, and water clarity significantly affected real-time false negative detection rates, though their relative importance varied across faunal groups and between flight procedures. Overall, remotely operated, autonomous drones have the potential to enhance long-term marine megafauna research, particularly when combined with post-flight analysis. Integrating artificial intelligence into autonomous drone operations will also be beneficial, especially for shark surveillance programs where real-time detection is essential for beach-user safety. Full article