Search Results (47)

Traditionally, sea fog detection technologies have relied primarily on in situ observations. However, point-based observations suffer from limitations in extensive monitoring in marine environments due to the scarcity of observation stations and the limited nature of measurement data. Satellites effectively address these issues by covering vast areas and operating across multiple spectral channels, enabling precise detection and monitoring of sea fog. Despite the increasing adoption of deep learning in this field, achieving further improvements in accuracy and reliability necessitates the simultaneous use of multiple satellite datasets rather than relying on a single source. Therefore, this study aims to achieve higher accuracy and reliability in sea fog detection by employing a deep learning-based advanced co-registration technique for multi-satellite image fusion and autotuning-based optimization of State-of-the-Art (SOTA) semantic segmentation models. We utilized data from the Advanced Meteorological Imager (AMI) sensor on the Geostationary Korea Multi-Purpose Satellite 2A (GK2A) and the GOCI-II sensor on the Geostationary Korea Multi-Purpose Satellite 2B (GK2B). Swin Transformer, Mask2Former, and SegNeXt all demonstrated balanced and excellent performance across overall metrics such as IoU and F1-score. Specifically, Swin Transformer achieved an IoU of 77.24 and an F1-score of 87.16. Notably, multi-satellite fusion significantly improved the Recall score compared to the single AMI product, increasing from 88.78 to 92.01, thereby effectively mitigating the omission of disaster information. Ultimately, comparisons with the officially operational GK2A AMI Fog and GK2B GOCI-II Marine Fog (MF) products revealed that our deep learning approach was superior to both existing operational products. Full article

Storm surges are the leading marine disaster in China’s coastal cities, with their impacts exacerbated by climate change and rapid urbanization. Despite their significance, most existing studies focus on a single scale, neglecting the complex, multi-scale nature of urban resilience and the interrelated governance strategies needed to address storm surge risks. This study introduces a dual-scale resilience indicator system—macro (prefecture-level cities) and micro (coastal buffer grids)—within the “exposure–sensitivity–adaptation” framework, utilizing multi-source data for a comprehensive assessment. This research also explores the impact mechanisms of storm surges on urban areas and proposes zonal governance strategies. Findings indicate that resilience varies spatially in Chinese coastal cities, with a pattern of “high resilience in the north, low resilience in the south, and a mix in the center.” At the macro scale, key limitations include policy implementation, infrastructure capacity, and social vulnerability. At the micro scale, factors such as inadequate green space, increased impervious surfaces, limited shelter access, and low utility network density lead to the emergence of “low-resilience units” in ecologically sensitive and mixed coastal zones. The study further reveals the synergies between resilience drivers across scales, emphasizing the need for integrated cross-scale governance. This research advances resilience theory by expanding spatial scales and refining indicator systems, while proposing a zonal governance framework tailored to resilience gradation. It offers a quantitative basis and practical strategies for fostering “safe cities” and advancing “adaptive spatial planning” in the context of sustainable development. Full article

Ocean surface wind fields are crucial for marine environmental research and applications in weather forecasting, ocean disaster monitoring, and climate change studies. However, traditional wind retrieval methods often struggle with modeling complexity and ambiguity due to the nonlinear nature of geophysical model functions (GMFs), leading to increased computational costs and reduced accuracy. To tackle these challenges, this study establishes a sea surface wind field retrieval model employing a backpropagation (BP) neural network, which integrates multi-angular observations from the Wind Radar (WindRAD) sensor aboard the Fengyun-3E (FY-3E) satellite. Experimental results show that the proposed model achieves high precision in retrieving both wind speed and direction. The wind speed model achieves a root-mean-square error (RMSE) of 1.20 m/s for the training set and 1.00 m/s for the selected test set when using ERA5 data as the reference, outperforming the official WindRAD products. For wind direction, the model attains an RMSE of 23.99° on the training set and 24.58° on the test set. Independent validation using Tropical Atmosphere Ocean (TAO) buoy observations further confirms the model’s effectiveness, yielding an RMSE of 1.29 m/s for wind speed and 24.37° for wind direction, also surpassing official WindRAD products. The BP neural network effectively captures the nonlinear relationship between wind parameters and radar backscatter signals, showing significant advantages over traditional methods and maintaining good performance across different wind speeds, particularly in the moderate range (4–10 m/s). In summary, the method proposed herein significantly enhances wind field retrieval accuracy from space; it has the potential to optimize satellite wind field products and improve global wind monitoring and meteorological forecasting. Full article

The accurate prediction of significant wave height presents a complex yet vital challenge in the fields of ocean engineering. This capability is essential for disaster prevention, fostering sustainable development and deepening our understanding of various scientific phenomena. We explore the development of a comprehensive predictive methodology for wave height prediction by integrating novel Takagi–Sugeno–Kang fuzzy models within a multiresolution analysis framework. The multiresolution analysis emerges via wavelets, since they are prominent models characterised by their inherent multiresolution nature. The maximal overlap discrete wavelet transform is utilised to generate the detail and resolution components of the time series, resulting from this multiresolution analysis. The novelty of the proposed model lies on its hybrid training approach, which combines least squares with AdaBound, a gradient-based algorithm derived from the deep learning literature. Significant wave height prediction is studied as a time series problem, hence, the appropriate inputs to the model are selected by developing a surrogate-based wrapped algorithm. The developed wrapper-based algorithm, employs Bayesian optimisation to deliver a fast and accurate method for feature selection. In addition, we introduce a projection step, to further refine the approximation capabilities of the resulting predictive system. The proposed methodology is applied to a real-world time series pertaining to spectral wave height and obtained from the Poseidon operational oceanography system at the Institute of Oceanography, part of the Hellenic Center for Marine Research. Numerical studies showcase a high degree of approximation performance. The predictive scheme with the projection step yields a coefficient of determination of $0.9991$, indicating a high level of accuracy. Furthermore, it outperforms the second-best comparative model by approximately $\mathrm{49\%}$ in terms of root mean squared error. Comparative evaluations against powerful artificial intelligence models, using regression metrics and hypothesis test, underscore the effectiveness of the proposed methodology. Full article

As humans continue to develop the undersea engineering ecosystem of systems, the consequences of catastrophic events must continue to be investigated and understood. Almost every undersea pressure vessel, from pipelines to sensors to unmanned vehicles, has the potential to experience a catastrophic collapse, known as an implosion. This collapse can be caused by hydrostatic pressure or any combination of external loadings from natural disasters to pressure waves imparted by other implosion or explosion events. During an implosion, high-magnitude pressure waves can be emitted, which can cause adverse effects on surrounding structures, marine life, or even people. The imploding structure, known as an implodable volume, can be in a free-field or confined environment. Confined implosion is characterized by a surrounding structure that significantly affects the flow of fluid around the implodable volume. Often, the confining structure is cylindrical, with one closed end and one open end. This work seeks to understand the effect of fluid flow restriction on the physics of implosion inside a confining tube. To do so, a comprehensive experimental study is conducted using a unique experimental facility. Thin-walled aluminum cylinders are collapsed inside a confining tube within a large pressure vessel. High-speed photography and 3D Digital Image Correlation are used to gather structural displacement and velocities during the event while an array of dynamic pressure sensors capture the pressure data inside the confining tube. The results of this work show that by changing the size of the open end, referred to as the flow area ratio, there can be a significant effect on the structural deformations and implosion severity. It also reveals that only certain configurations of holes at the open end of the tube play a role in the dynamic pressure pulse measured at the closed end of the tube. By understanding the consequences of an implosion, designers can make decisions about where these pressure vessels should be in relation to other pressure vessels, critical infrastructure, marine life, or people. In the same way that engineers design for earthquakes and analyze the impact their structures have on the environment around them, contributors to the undersea engineering ecosystem should design with implosion in mind. Full article

As the first marine economic zone, the coastal zone is a complex and active ecosystem, serving as an important resource breeding area. However, during the process of economic development, coastal zone resources have been severely exploited, leading to fragile ecology and frequent natural disasters. Therefore, it is imperative to analyze coastline changes and their correlation with coastal ecological space. Utilizing long-time series high-resolution remote sensing images, Google Earth images, and key sea area unmanned aerial vehicle (UAV) remote sensing monitoring data, this study selected the coastal zone of Ningbo City as the research area. Remote sensing interpretation mark databases for coastline and typical coastal ecological space were established. Coastline extraction was completed based on the visual discrimination method. With the help of the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI) and maximum likelihood classification, a hierarchical classification discrimination process combined with a visual discrimination method was constructed to extract long-time series coastal ecological space information. The changes and the linkage relationship between the coastlines and coastal ecological spaces were analyzed. The results show that the extraction accuracy of ground objects based on the hierarchical classification process is high, and the verification effect is improved with the help of UAV remote sensing monitoring. Through long-time sequence change monitoring, it was found that the change in coastline traffic and transportation is significant. Changes in ecological spaces, such as industrial zones, urban construction, agricultural flood wetlands and irrigation land, dominated the change in artificial shorelines, while the change in Spartina alterniflora dominated the change in biological coastlines. The change in ecological space far away from the coastline on both the land and sea sides has little influence on the coastline. The research shows that the correlation analysis between coastline and coastal ecological space provides a new perspective for coastal zone research. In the future, it can provide technical support for coastal zone protection, dynamic supervision, administration, and scientific research. Full article

The 21st‑century Maritime Silk Road initiative highlights the importance of oceans as hubs for resources, ecology, and trade, yet a comprehensive understanding of marine natural disaster risks within this region remains limited. This study focused on 30 countries along the Maritime Silk Road and developed a multi-hazard natural disaster risk assessment framework tailored for large-scale regional evaluation. It goes beyond single-factor or single-disaster assessments to enhance disaster resilience and support effective disaster response strategies. The framework integrates 65 indicators across four dimensions: disaster-causing factors, disaster-conceiving environments, disaster-bearing bodies, and disaster reduction capacities. It employs five single-indicator evaluation models alongside a combination assessment method based on maximum deviations to evaluate national-scale natural disaster risks. Results reveal spatial consistency in risk evaluations and capture the exposure and sensitivity of 30 countries to different hazards. South Asia exhibits higher seismic risks, while Saudi Arabia consistently receives the lowest risk. Tropical countries like Vietnam and the Philippines face significant storm risks. Drought hazard risk is higher in the Middle East and East Africa, while it is lower in Brunei, Indonesia, and Malaysia. Flood risks are notably higher in Bangladesh, while Iran and Tanzania consistently receive lower risk ratings. Overall, South Asia exhibits higher multi-hazard risks, with medium-to-low risks along the Mediterranean and Southeast Asia. These findings provide technical support for disaster risk reduction by identifying high-risk areas, prioritising resource allocation, and strengthening disaster reduction strategies. Full article

The drastic changes in the marine environment can induce the instability of seabed sediments, threatening the safety of marine engineering facilities such as offshore oil platforms, oil pipelines, and submarine optical cables. Due to the lack of long-term in situ observation equipment for the engineering properties of seabed sediments, most existing studies have focused on phenomena such as the erosion suspension of the seabed boundary layer and wave-induced liquefaction, leading to insufficient understanding of the dynamic processes affecting the seabed environment. In this study, a long-term in situ observation system for subsea engineering geological environments was developed and deployed for 36 days of continuous monitoring in the offshore area of Qingdao. It was found that wave action significantly altered sediment mechanical properties, with a 5% sound velocity increase correlating to 39% lower compression, 7% higher cohesion, 11% greater internal friction angle, and 50% reduced excess pore water pressure at 1.0–1.8 m depth. suggesting sustained 2.2 m wave loads of expelled pore water, driving dynamic mechanical property variations in seabed sediments. This long-term in situ observation lays the foundation for the monitoring and early warning of marine engineering geological disasters. Full article

The length of the mainland coastline in Guangdong Province ranks first in the country, and the rapid development of the marine economy have also supported Guangdong Province’s GDP to remain at the top of the country for 35 consecutive years. The coastline has extremely important ecological functions and resource values. Guangdong Province has always attached great importance to the renovation and restoration of its coastline, continuously strengthening the ecological, disaster reduction, and tourism functions of the coastal areas. This article analyzes the main measures, achievements, and main problems of coastal protection in Guangdong Province and selects typical areas for driving force analysis. Finally, some thoughts and targeted countermeasures on the protection of Guangdong Province’s coastline are proposed, which provide useful references for comprehensively strengthening coastline protection, scientifically carrying out coastline renovation and restoration, and improving the natural coastline retention rate in the future. This can also output wisdom and experience for the construction of a maritime power under the background of land–sea coordination. Full article

Impact assessments are necessary for supporting fisheries’ disaster applications and management options for states affected by disasters. This paper measures the joint and individual impacts of man-made and natural disasters, global pandemics and recessions, the U.S.-China trade war, and recent increases in fuel prices on commercial dockside values of the Mississippi blue crab fishery. The mean-difference model estimates the direct impacts when the current dockside values fall below the benchmark values. The marine economic recovery model identifies the significant determinants of the variations in the dockside values. Mean-difference model results indicate that the Mississippi blue crab fishery sustained direct losses due to Hurricane Katrina in 2005, the Deepwater Horizon oil spill in 2010, and the opening of the Bonnet Carre Spillway in 2011. The estimated marine economic recovery model explained 93 percent of the variations in real dockside values. Two independent variables are statistically significant, including blue crab landings and time. The disaster variables have the expected signs but are not statistically significant. These methodologies’ usefulness is applicable in assessing the direct impacts on fisheries and other economic sectors affected by disasters such as major hurricanes, oil spills, massive freshwater intrusion, and harmful algal blooms. Full article

The coast is the most dynamic part of the Earth’s surface due to its strategic position at the interface of the land and the sea. It is, therefore, exposed to hazards and specific risks because of the geography as well as the geological and environmental characteristics of different countries. The coastal environment is essentially dynamic and evolving in time and space, marked by waves, tides, and seasons; moreover, it is subjected to many marine and continental processes (forcing). This succession of events significantly influences the frequency and severity of coastal hazards. The present paper aims at describing and characterizing the hazards and vulnerabilities on the Cameroonian coast. Cameroon possesses 400 km of coastline, which is exposed to various hazards. It is important to determine the probabilities of these hazards, the associated effects, and the related vulnerabilities. In this study, in this stable intraplate setting, the methodology used was diverse and combined techniques for the study of the shore and methods for the treatment of climatic data. Also, historical data were collected during field observations and from the CRED website for all the natural hazards recorded in Cameroon. In addition, documents on climate change were consulted. Remotely sensed data, combined with GIS tools, helped to determine and assess the associated risks. A critical grid combining a severity and frequency analysis was used to better understand these hazards and the coastal vulnerabilities of Cameroon. The results show that Cameroon’s coastal margins are subject to natural processes that cause shoreline changes, including inundation, erosion, and accretion. This study identified seven primary hazard types (earthquakes, volcanism, landslides, floods, erosion, sea level rise, and black tides) affecting the Cameroonian coastline, with the erosion rate exceeding 1.15 m/year at Cape Cameroon. Coastal populations are continuously threatened by these natural or man-induced hazards, and they are periodically subjected to catastrophic disasters such as floods and landslides, as experienced in Cameroon. In addition, despite the existence of the National Contingency Plan devised by the Directorate of Civil Protection, National Risk, and Climate Change Observatories, the implementation of disaster risk reduction and mitigation strategies is suboptimal. Full article

Approximately 1 million tons of green tides decompose naturally in the Yellow Sea of China every year, releasing large quantities of nutrients that disrupt the marine ecological balance and cause significant environmental consequences. Currently, the identification of areas affected by green tides primarily relies on certain methods, such as ground sampling and biochemical analysis, which limit the ability to quickly and dynamically identify decomposition regions at large spatial and temporal scales. While multi-source remote sensing data can monitor the extent of green tides, accurately identifying areas of algal decomposition remains a challenge. Therefore, satellite data were integrated with key biochemical parameters, such as the carbon-to-nitrogen ratio (C/N), to develop a method for identifying green tide decomposition regions (DRIM). The DRIM shows a high accuracy in identifying green tide decomposition areas, validated through regional repetition rates and UAV measurements. Results indicate that the annual C/N threshold for green tide decomposition regions is 1.2. The method identified the primary decomposition areas in the Yellow Sea from 2015 to 2020, concentrated mainly in the southeastern region of the Shandong Peninsula, covering an area of approximately 1909.4 km². In 2015, 2016, and 2017, the decomposition areas were the largest, with an average annual duration of approximately 35 days. Our method provides a more detailed classification of the dissipation phase, offering reliable scientific support for accurate and detailed monitoring and management of green tide disasters. Full article

In response to the intensification of global warming, extreme weather events, such as tropical cyclones (TCs) and cold waves (CWs) have become increasingly frequent near the eastern Guangdong coast, significantly affecting the structure and material transport of coastal waters. Based on nearshore-measured and remote sensing reanalysis data in the winter of 2011 and summer of 2012 on the eastern Guangdong coast, this study analyzed the nearshore hydrodynamic evolution process, influencing mechanism, and marine environmental effects under the influence of TCs and CWs, and further compared the similarities and differences between the two events. The results revealed significant seasonal variations in the hydrological and meteorological elements of the coastal waters, which were disrupted by the passage of TCs and CWs. The primary influencing factors were TC track and CW intensity. The current structure changed significantly during the TCs and CWs, with the TC destroying the original upwelling current and the CW affecting the prevailing northeastward current. Wind is one of the major forces driving nearshore hydrodynamic processes. According to the synchronous analysis of research data, the TC-induced water level rise is primarily attributed to the combined effects of wind stress curl and the Ekman effect, whereas the water level rise associated with CW is primarily linked to the Ekman effect. The water transport patterns during the TC and CW differed, with transport concentrated on the right side of the TC track and within the coastal strong-wind zones, respectively. Additionally, the temporal frequency domain of wavelet analysis highlighted the distinct nature of TC and CW signals, with 1–3 d and 4–8 d, respectively, and with TC signals being short-lived and rapid compared to the more sustained CW signals. This study enhances our understanding of the response of coastal hydrodynamics to extreme weather events on the eastern Guangdong coast, and the results can provide references for disaster management and protection of nearshore ocean engineering under extreme events. Full article

A storm surge, classified as an extreme natural disaster, refers to unusual sea level fluctuations induced by severe atmospheric disturbances such as typhoons. Existing reanalysis data, such as ERA5, significantly underestimates the location and maximum wind speed of typhoons. Therefore, this study initially assesses the accuracy of tropical cyclone positions and peak wind speeds in the ERA5 reanalysis dataset. These results are compared against tropical cyclone parameters from the IBTrACS (International Best Track Archive for Climate Stewardship). The position deviation of tropical cyclones in ERA5 is mainly within the range of 10 to 60 km. While the correlation of maximum wind speed is significant, there is still considerable underestimation. A wind field reconstruction model, incorporating tropical cyclone characteristics and a distance correction factor, was employed. This model considers the effects of the surrounding environment during the movement of the tropical cyclone by introducing a decay coefficient. The reconstructed wind field significantly improved the representation of the typhoon eyewall and high-wind-speed regions, showing a closer match with wind speeds observed by the HY-2B scatterometer. Through simulations using the FVCOM (Finite Volume Community Ocean Model) storm surge model, the reconstructed wind field demonstrated higher accuracy in reproducing water level changes at Tanxu, Gaoqiao, and Zhangjiabang stations. During the typhoon’s landfall in Shanghai, the area with the greatest water level increase was primarily located in the coastal waters of Pudong New Area, Shanghai, where the highest total water level reached 5.2 m and the storm surge reached 4 m. The methods and results of this study provide robust technical support and a valuable reference for further storm surge forecasting, marine disaster risk assessment, and coastal disaster prevention and mitigation efforts. Full article

Following the X-Press Pearl maritime disaster off the coast of Sri Lanka, a stranded spinner dolphin (Stenella longirostris) was recovered, and the cause of death was investigated. Post-mortem examinations revealed evidence of by-catch, but a natural coinfection with dolphin morbillivirus (DMV) and gammaherpesvirus was detected by further analyses, marking the first documented case of a dual viral infection in this species within the region. Molecular diagnostics, including PCR and sequencing, were performed on tissue imprints collected on FTA^® cards, confirming the presence of DMV in the prescapular lymph node and gammaherpesvirus in the lesions in the oral cavity. The concurrent detection of DMV and gammaherpesvirus raises significant concerns regarding the potential impacts of environmental stressors, such as chemical pollutants from the X-Press Pearl maritime disaster, on exacerbating susceptibility to viral infections in marine mammals. These findings highlight the need for ongoing surveillance of cetacean populations in the Indian Ocean to better understand pathogen circulation and health and conservation implications of anthropogenic activities on the marine ecosystem. Full article