Marine Monitoring Research Articles

Marine oil spill detection using improved polarimetric feature based on polarization SAR image

ABSTRACT Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications.

CenterNet-Saccade: Enhancing Sonar Object Detection with Lightweight Global Feature Extraction.

Sonar imaging technology is widely used in the field of marine and underwater monitoring because sound waves can be transmitted in elastic media, such as the atmosphere and seawater, without much interference. In underwater object detection, due to the unique characteristics of the monitored sonar image, and since the target in an image is often accompanied by its own shadow, we can use the relative relationship between the shadow and the target for detection. To make use of shadow-information-aided detection and realize accurate real-time detection in sonar images, we put forward a network based on a lightweight module. By using the attention mechanism with a global receptive field, the network can make the target pay attention to the shadow information in the global environment, and because of its exquisite design, the computational time of the network is greatly reduced. Specifically, we design a ShuffleBlock model adapted to Hourglass to make the backbone network lighter. The concept of CNN dimension reduction is applied to MHSA to make it more efficient while paying attention to global features. Finally, CenterNet's unreasonable distribution method of positive and negative samples is improved. Simulation experiments were carried out using the proposed sonar object detection dataset. The experimental results further verify that our improved model has obvious advantages over many existing conventional deep learning models. Moreover, the real-time monitoring performance of our proposed model is more conducive to the implementation in the field of ocean monitoring.

Lessons from Lyme Bay (UK) to inform policy, management, and monitoring of Marine Protected Areas

Abstract This decade represents a critical period to profoundly rethink human–nature interactions in order to address the interwoven climate and biodiversity crises. Marine Protected Areas (MPAs) demonstrate promise for increasing ecosystem resilience and reversing habitat and population declines, but outcomes vary considerably from context to context. Partially protected areas offer a compromise between ecological recovery and the social needs of local communities, but their success is contingent on an array of factors. This in-depth review summarizes 15 years of marine conservation research and impact in Lyme Bay (southwest UK), to serve as a model for the future adoption of partially protected MPAs. The findings from the UK’s longest integrated socioecological monitoring MPA study are presented and supplemented by an evaluation of the whole-site management approach as a core element of Lyme Bay’s achievements. The journey from research to improved monitoring and ambitious policy is illustrated within and interspersed with stories of novel discoveries, ongoing challenges, and method developments. What started as a dedicated group of community members has grown into an immense collaboration between fishers, scientists, NGOs, and regulators, and their combined efforts have sent ripple effects of positive change across the globe.

Development of a Mobile Buoy with Controllable Wings: Design, Dynamics Analysis and Experiments

Marine monitoring equipment such as Argo profiling buoys and underwater gliders are important devices for oceanographic research and marine resource exploration. In this study, a novel mobile buoy capable of vertical profiling motion like Argo profiling buoys and sawtooth gliding motion like underwater gliders is proposed. The proposed mobile buoy can switch between the two motion modes with controllable wings. To verify the feasibility of the proposed mobile buoy, a fluid–multibody coupling model considering multibody dynamics and hydrodynamics was developed to investigate the dynamic response. A scaled-down buoy prototype was fabricated and the feasibility of the two motion modes was experimentally investigated in a laboratory tank. The experimental results agree well with the results of numerical simulation. This work can be helpful for the design and analysis of this kind of mobile buoy.

Enhancing Georeferencing and Mosaicking Techniques over Water Surfaces with High-Resolution Unmanned Aerial Vehicle (UAV) Imagery

Aquatic ecosystems are crucial in preserving biodiversity, regulating biogeochemical cycles, and sustaining human life; however, their resilience against climate change and anthropogenic stressors remains poorly understood. Recently, unmanned aerial vehicles (UAVs) have become a vital monitoring tool, bridging the gap between satellite imagery and ground-based observations in coastal and marine environments with high spatial resolution. The dynamic nature of water surfaces poses a challenge for photogrammetric techniques due to the absence of fixed reference points. Addressing these issues, this study introduces an innovative, efficient, and accurate workflow for georeferencing and mosaicking that overcomes previous limitations. Using open-source Python libraries, this workflow employs direct georeferencing to produce a georeferenced orthomosaic that integrates multiple UAV captures, and this has been tested in multiple locations worldwide with optical RGB, thermal, and multispectral imagery. The best case achieved a Root Mean Square Error of 4.52 m and a standard deviation of 2.51 m for georeferencing accuracy, thus preserving the UAV’s centimeter-scale spatial resolution. This open-source workflow represents a significant advancement in the monitoring of marine and coastal processes, resolving a major limitation facing UAV technology in the remote observation of local-scale phenomena over water surfaces.

Recent advances in PEDOT/PProDOT-derived nano biosensors: engineering nano assemblies for fostering advanced detection platforms for biomolecule detection.

With the recent unprecedented emergence of a global pandemic, unknown diseases and new metabolic patterns expressing serious health issues, the requirement to develop new diagnostic tools, therapeutic solutions, and healthcare and environmental monitoring systems are significantly higher in the present situation. Considering that high sensitivity, selectivity, stability and a low limit of detection (LOD) are inevitable requirements for an ideal biosensor, the class of conducting polymers of poly(3,4-ethylenedioxythiophene) (PEDOT) and recently poly(3,4-propylenedioxythiophene) (PProDOT) materials have been demonstrated to be promising candidates for designing sensor devices. Nanostructure engineering of these polymeric materials with tunable surface properties and side chain functionalization to enable sensor probe conjugation combined with signal amplification devices such as OECTs and OFETs can fulfil the requirements of next-generation smart nano-biosensors. In this review, we analyze recent reports on PEDOT/PProDOT nanostructures and nanocomposites for developing nano-biosensors and their application in the detection of different biomarkers, environmental, toxicology, marine and aquatic monitoring, forensic and illicit drug detection, etc. In addition, we discuss the challenges associated with the design of PEDOT/PProDOT nano-biosensors and future perspectives on the exploration of novel sensor platforms, particularly PProDOT derivatives for bioelectronics and novel design strategies for next-generation smart nano-biosensors.

Satellite-Derived Bathymetry Using a Fast Feature Cascade Learning Model in Turbid Coastal Waters

Obtaining accurate bathymetric maps is very valuable for marine environment monitoring, port planning, and so on. Accurately estimating water depth in turbid coastal waters using satellite remote sensing encounters challenges originating from low water transparency, but it is limited by the quantity, quality, and water quality of samples. This study introduces a fast feature cascade learning model (FFCLM) to enhance the accuracy of bathymetric inversion from multispectral satellite images, particularly when limited field samples are available. FFCLM leverages spectral bands and in situ data to derive effective inversion weights through feature concatenation and cascade fitting. Field experiments conducted at Nanshan Port and Rushikonda Beach gathered water depth, satellite, and in situ data. Comparative analysis with conventional machine learning algorithms, including support vector machine, random forest, and gradient boosting trees, indicates that FFCLM achieves lower errors and demonstrates more robust performance across study areas. This is especially more pronounced when using small training samples ( n < 100). Examination of key parameters and water depth profiles highlights FFCLM’s advantages in generalization and deep-water inversion. This study presents an efficient solution for small-sample bathymetric mapping in turbid coastal waters, utilizing spectral and physical information to overcome sample size limitations and enhancing satellite remote sensing capabilities for shallow water monitoring.

Marine Monitoring System Powered by Flexible Solar Panel

Marine Monitoring System Powered by Flexible Solar Panel

An integrated coastal ecosystem monitoring strategy: Pilot case in Naf-Saint Martin Peninsula, Bangladesh

Rapid population growth creating an excessive pressure on the marine environment and thus monitoring of marine ecosystem is essential. However, due to high technical and financial involvement, monitoring of coastal ecosystem is always challenging in developing countries. This study aims to develop an integrated coastal ecosystem monitoring system that combines scientific sampling, numerical model simulation and citizen science observations to monitor the coastal ecosystem of Bangladesh. This concept of integrated monitoring approach was piloted from January 2022 to April 2023 at the South East coastal zone of Bangladesh. Scientific sampling and numerical model simulations were performed for temperature and salinity data collection. Citizen science approach was employed to collect data on environmental conditions, fisheries, plankton, other marine resources, and plastic pollution. Numerical model simulations and citizen scientists observations of temperature and salinity showed good agreement with the scientifically collected data. In addition, citizen scientists observations on fisheries, plankton, other marine resources and plastic pollution were also in line with the existing database and previous studies. The proposed integrated monitoring approach presents a viable technique, creating a new avenue for coastal and marine ecosystem monitoring where infrastructural facilities are limited.

Application of Biological Methods and Remote Sensing in Marine Monitoring

Due to the serious side effects caused by poured nuclear sewage, marine monitoring ought to be paid much more attention than before. This article concludes two major general approaches, respectively biological methods and remote sensing. Biological methods contain specifically 4 different methods: ecological monitoring, biological monitoring, measurement of biological physiological and biochemical indicators, and determination of residual amounts of pollutants in the organism. Sensor monitoring technology based on marine organisms is a common technology, which desires marine life suitable for being sensor-equipped and well-developed tags to undergo successive monitoring. Biological detection methods are established by scientists in relevant fields to make up for the shortcomings of physical and chemical pollution detection. The remote sensing method, which includes a range of techniques such as satellite imagery, aerial photography, and sonar systems, offers a unique vantage point that allows us to observe and analyze large expanses of oceanic territory with unprecedented precision and efficiency. It can also be used to identify the sources of marine pollution, assess the impacts of marine pollution on marine ecosystems, monitor the effectiveness of pollution mitigation measures, and develop and implement policies to prevent marine pollution.

Maximizing the energy conversion of triboelectric nanogenerator through the synergistic effect of high coupling and dual-track circuit for marine monitoring

The wave energy harvesting efficiency (EHE) and electrical energy storage efficiency (ESE) significantly impact ocean power at the front-end and back-end stages, respectively. In this work, to maximize the overall energy conversion affected by these two factors, a clip-like structured pendulum coupled nanogenerator (CP-CNG) integrated with a dual-track circuit (DTC) is elaborately designed to address poor energy conversion of the TENGs for marine monitoring. The optimized EHE was realized through coupling one freestanding triboelectric nanogenerator (FR-TENG) unit and two electromagnetic generator (EMG) units into two contact-separation triboelectric nanogenerator (CS-TENG) units. The space utilization rate of CP-CNG can be as high as 91.9%. Meanwhile, increased ESE is achieved by integrating DTC as voltage regulators, which perform backend energy management for CS-TENG and EMG, respectively. It is worth noting that for a 1 mF capacitor, the stored energy via the cooperation of CP-CNG and DTC can reach 60.7 mJ in 110 s, which is about 164 times that of CS-TENG (0.37 mJ) coupling with FR-TENG and EMG and 1.6 times that of CS-TENG (36.8 mJ) with buck circuit, demonstrating excellent synergistic effects. Owing to the synergistic effect, one CP-CNG can continuously drive four thermohygrometers under simulated water wave conditions. In additional, a hazard alarm system, including an immersion sensor, Bluetooth and gateway support, can also be powered by one CP-CNG successfully. This work develops a state-of-the-art concept for maximum energy conversion of TENGs, which should have a profound impact on the design of high-performance TENGs in the future.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Performance Analysis of a WPCN-Based Underwater Acoustic Communication System

Underwater acoustic communication (UWAC) has a wide range of applications, including marine environment monitoring, disaster warning, seabed terrain exploration, and oil extraction. It plays an indispensable and increasingly important role in marine resource exploration and marine economic development. In current UWAC systems, the terminal nodes are usually powered by energy-limited batteries. Due to the harshness of the underwater environment, especially in the ocean environment, it is very costly and difficult, even impossible, to replace the batteries for the terminal nodes in UWACs, which results in the short lifetime and unreliability of the terminal nodes and the systems. In this paper, we present the application of a wireless powered communication network (WPCN) to the UWAC systems to provide an auxiliary and convenient energy supplement for solving the energy-limited problem of the terminal nodes, where the hybrid access point (H-AP) transfers energy to the terminal nodes in the downlink. In contrast, the terminal nodes use the harvested energy to transmit the information to the H-AP in the uplink. To evaluate the proposed WPCN-based UWAC systems, we investigate the performance of the average bit error rate (BER), outage probability, and achievable information rate for the systems in a frequency-selective sparse channel and non-white noise environment. We derive the closed-form expression for the probability density function (PDF) of the received signal-to-noise ratio (SNR). Based on this, we further derive novel closed-form expressions for the average BER and the outage probability of the systems. Numerical results confirm the validity of the proposed analytical results. It is shown that there exists an optimal signal frequency and time allocation factor for the systems to achieve optimal performance, and a larger optimal time allocation factor is preferred for a smaller hybrid access point (H-AP) transmit power or a larger transmission distance, while a smaller optimal signal frequency is required for a larger transmission distance.

Corrosion Properties of a Novel FeCrMoNbB Thin Film Metallic Glass Deposited by DC Magnetron Sputtering

The application of amorphous alloys as thin-film coatings with good corrosion properties has drawn attention in the last years since overcoming the typical limitations of these materials, such as the critical size and brittleness. Techniques based on Physical Vapor Deposition, like DC magnetron sputtering, are interesting once it allows parameter optimization to achieve the desired microstructure [1,2,3]. This study aims to characterize the electrochemical properties of a novel FeCrMoNbB (low Cr content – 11at%) alloy as a coating.Thin-film coatings were produced in a DC magnetron sputtering PVD system with 0.5 Pa Ar working pressure, 92 W of sputtering power, and 200°C deposition temperature. The substrate was 316L stainless steel. Corrosion tests were performed in a three-electrode cell in a Gamry 600+ potentiostat using the following sequence: two hours of open circuit potential (OCP), electrochemical impedance spectroscopy (EIS) from 100kHz to 0,02 Hz, 20 minutes of OCP, and potentiodynamic polarization test from -0,03 to 1,2V (vs. OCP). The solution was a 0,6M NaCl (Cl- content equivalent to seawater), natural pH of 5.5. Measurement Model software, developed by Orazem et al. [4], was used for data regression. X-ray photoelectron microscopy (XPS) was performed to obtain complementary information about the passive film. An XR3E2 apparatus from Vacuum Generator with an Mg-Kα source was used. The analysis was performed after stabilization in an open circuit for two hours.Fully amorphous thin films of about 400 nm were obtained. Outstanding corrosion behavior was observed for the coating, which presented a corrosion current (icorr) around 5 x10-9 Acm-2 and a corrosion potential of 127 mV. The icorr value is more than two decades lower when compared to the 316L of the substrate, indicating a higher corrosion resistance after applying the coating. EIS results showed a high impedance modulus at low frequency was |Z|LF = 3.7 MΩ.cm2. XPS analysis showed that all the alloying elements were in the oxidized form on the surface. Metallic species from the underlying surface could also be detected, meaning the oxide layer has less than 10 nm. Regression with Measurement Model software found a global capacitance C = 5.7 μF.cm2, a typical value for a compact passive layer. From the value of C, the film thickness was estimated to be around 2 nm, corroborating with the XPS analysis. A compact, thin layer of oxide composed of Cr, Nb, and Mo can be associated with high protectiveness of the passive film, hence, good corrosion resistance. Furthermore, the pitting phenomenon typical of 316L near 500mV was not observed in the coating. In summary, the results are promising for using these alloys as coatings for marine applications, potentially replacing conventional stainless steels.[1] M. Panayotova, Y. Garbatov, C. Guedes Soares, Corrosion of Steels in Marine Environment, Monitoring and Standards, Safety and Reliability of Industrial Products, Systems and Structures, Taylor & Francis Group, 2008.p.370.[2] WOOD Maureen; VETERE ARELLANO Ana Lisa; VAN WIJK Lorenzo. Publications Office of the European Union; 2013. JRC84661[3] Chu, J. P. et al. Thin film metallic glasses: Unique properties and potential applications. Thin Solid Films 520, 5097–5122 (2012).[4] M.E.O. W. Watson, EIS: Measurement Model Program., ECSarXiv. (2020). https://doi.org/10.1149/osf.io/kze9x.

AI for Marine, Ocean and Climate Change Monitoring

In the ever-evolving landscape of marine, oceanic, and climate change monitoring, the intersection of cutting-edge artificial intelligence (AI), machine learning (ML), and data analytics has emerged as a pivotal catalyst for transformative advancements [...]

A Survey on Air-to-Sea Integrated Maritime Internet of Things: Enabling Technologies, Applications, and Future Challenges

Future generation communication systems are exemplified by 5G and 6G wireless technologies, and the utilization of integrated air-to-sea (A2S) communication infrastructure is employed to extend network coverage and enhance data throughput to support data-driven maritime applications. These ground-breaking techniques have promoted the rapid development of the maritime internet of things (MIoT). In particular, the integration of air base stations (ABSs) in the MIoT can achieve broadband, low-delay, and reliable wireless transmissions. Considering the potential of ABS-enabled communications, this survey presents the state of the art in the A2S integrated MIoT. More specifically, relevant A2S integrated MIoT architectures are discussed together with the role of their building blocks. Next, we introduce the enabling technologies, including the sensor, communication, data processing and storage, and security and privacy protection techniques. Then, resource allocation, cloud/edge computing and caching, routing protocols, and spatial location optimization in the maritime environment are discussed and grouped based on their performance targets. Additionally, we also show the potential applications of the A2S integrated MIoT in marine environment monitoring, traffic, navigation safety, and resources management. Finally, several future challenges in the area of the A2S integrated MIoT are given, related to the technical security, reliability, and energy efficiency, etc.

Exposure of Mytilus galloprovincialis to Microplastics: Accumulation, Depuration and Evaluation of the Expression Levels of a Selection of Molecular Biomarkers.

Microplastic contamination is a growing marine environmental issue with possible consequences for seafood safety. Filter feeders are the target species for microplastic (MPs) pollution because they filter large quantities of seawater to feed. In the present study, an experimental contamination of Mytilus galloprovincialis was conducted using a mixture of the main types of MPs usually present in the seawater column (53% filaments, 30% fragments, 3% granules) in order to test the purification process as a potential method for removing these contaminants from bivalves intended for human consumption. A set of molecular biomarkers was also evaluated in order to detect any variations in the expression levels of some genes associated with biotransformation and detoxification, DNA repair, cellular response, and the immune system. Our results demonstrate that: (a) the purification process can significantly reduce MP contamination in M. galloprovincialis; (b) a differential expression level has been observed between mussels tested and in particular most of the differences were found in the gills, thus defining it as the target organ for the use of these biomarkers. Therefore, this study further suggests the potential use of molecular biomarkers as an innovative method, encouraging their use in next-generation marine monitoring programs.

Stakeholder Analysis as a strategic tool in framing collaborative governance arenas for marine litter monitoring

Monitoring and assessment of marine litter requires multi-stakeholder involvement at national and subnational levels. Collaborative governance approaches are important, but often fail without adequate effort towards identifying and engaging stakeholders with appropriate profiles for the issue at stake. Stakeholder Analysis (SA) is increasingly used to ensure efficient governance arrangements. Our hypothesis is that SA contributes to collaborative governance processes for marine litter policies. We explored a pioneer participatory process in Brazil, where SA was applied to identify, categorize, and prioritize stakeholders, and analyze their power and interest, for the Strategic Plan for Monitoring and Assessing Marine Litter in the state of São Paulo. A top-down/bottom-up approach revealed that snowball sampling complemented the stakeholder assemblage identified by the consultation of experts. Prioritization of data-related stakeholders streamlined the participatory process. The interest-power matrix evaluated stakeholders' influences, guiding specific engagement strategies. We highlight the significance of SA in collaborative governance and mobilizing key stakeholders for effective marine litter monitoring initiatives, contributing to the global agenda to combat marine pollution.

How we count counts: Examining influences on detection during shoreline surveys of marine debris

Shoreline surveys are a common approach for documenting loads of marine macrodebris (≥ 2.5 cm). When surveys are conducted repeatedly over time and space, patterns in source, abundance, geographic distribution, and composition can be detected. Yet to realize their full potential, monitoring programs that rely on surveys must grapple with high variability in debris abundance, and appropriately manage uncertainty when reporting estimates of debris quantity. A potentially important source of bias in estimating debris loads from shoreline monitoring datasets is variability in debris detection rates. With this in mind, we conducted field experiments using common strip-transect marine debris survey protocols, designed to test detection of macrodebris. We quantified how protocol, shoreline, and debris characteristics influence the detectability of marine macrodebris. Detection rates varied according to debris distance from observer (0–5 m), number of observers, debris characteristics (size, color), and shoreline substrate. Our results highlight considerations for monitoring program design. Comparisons across datasets should be approached cautiously given differences in survey protocols and sources of bias that may affect debris density estimates should be quantified and addressed. We hope these results will inform marine debris monitoring efforts that are optimized for intended data use and impact.

First observations of coral spawning at the Banda Islands, Maluku, Indonesia

Abstract. Novriansyah A, Huhn M, Wicaksono H, Senen B, Subhan B, Fenner D, Madduooa H, Dias PJ. 2023. First observations of coral spawning at the Banda Islands, Maluku, Indonesia. Biodiversitas 24: 6082-6091. Due to their high diversity and productivity, their socio-economic value to coastal communities and their crucial role in coastal protection, coral reefs belong to the most important marine ecosystems in the world. Understanding and protecting coral reefs has never been so important, given the unprecedented anthropogenic pressures they face. Indonesia holds 18% of the world’s coral reefs and is home to some of the richest marine biodiversity on the planet. However, with over 17,000 islands scattered across 86,700 km2, marine monitoring and conservation remains challenging. The Banda Sea (Maluku, Indonesia) ecoregion ranks as a high priority for marine conservation. The present study reports on the first observations of coral spawning at the Banda Islands from 2016 to 2019. Ad-hoc opportunistic monitoring was carried out to identify patterns of coral spawning. Our results suggest the sharpest temperature increases and full moon timing have the biggest influence on the month and day of coral spawning, whereas the tidal cycle influences the hour when gametes are released. Coral spawning peaks were observed to occur in two seasons, April and October-November, 5-7 days after full moon and 2-3 hours after sunset during falling tides. The presence of two spawning seasons may provide coral reefs at the Banda Islands with an advantage to recover from potential impacts, such as storms, bleaching or destructive fishing.