Research

25 pages, 6494 KiB

Open AccessArticle

Application of Satellite-Derived Summer Bloom Indicators for Estonian Coastal Waters of the Baltic Sea

by Ian-Andreas Rahn, Kersti Kangro, Andres Jaanus and Krista Alikas

Appl. Sci. 2023, 13(18), 10211; https://doi.org/10.3390/app131810211 - 11 Sep 2023

Cited by 1 | Viewed by 625

The aim of this study was to test and develop the indicators for the remote sensing assessment of cyanobacterial blooms as an input to the estimation of eutrophication and the environmental status (ES) under the Marine Strategy Framework Directive (MSFD) in the optically [...] Read more.

The aim of this study was to test and develop the indicators for the remote sensing assessment of cyanobacterial blooms as an input to the estimation of eutrophication and the environmental status (ES) under the Marine Strategy Framework Directive (MSFD) in the optically varying Estonian coastal regions (the Baltic Sea). Here, the assessment of cyanobacteria blooms considered the chlorophyll-a (chl-a), turbidity, and biomass of N₂-fixing cyanobacteria. The Sentinel-3 A/B Ocean and Land Colour Instrument (OLCI) data and Case-2 Regional CoastColour (C2RCC) processor were used for chl-a and turbidity detection. The ES was assessed using four methods: the Phytoplankton Intensity Index (PII), the Cyanobacterial Surface Accumulations Index (CSA), and two variants of the Cyanobacterial Bloom Indicator (CyaBI) either with in situ-measured cyanobacterial biomass or with satellite-estimated cyanobacterial biomass. The threshold values for each coastal area ES assessment are presented. During 2022, the NW Gulf of Riga reached good ES, but most of the 16 coastal areas failed to achieve good ES according to one or multiple indices. Overall, the CyaBI gives the most comprehensive assessment of cyanobacteria blooms, with the CyaBI (in situ) being the best suited for naturally turbid areas. The CyaBI (satellite) could be more useful than in situ in large open areas, where the coverage of in situ sampling is insufficient. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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20 pages, 11053 KiB

Open AccessArticle

Towards Improved Quality Control of In Situ Sea Surface Temperatures from Drifting and Moored Buoys in the NOAA iQuam System

by Boris Petrenko, Alexander Ignatov, Victor Pryamitsyn and Olafur Jonasson

Appl. Sci. 2023, 13(18), 10205; https://doi.org/10.3390/app131810205 - 11 Sep 2023

Viewed by 570

Abstract

The NOAA in situ Sea Surface Temperature (SST) Quality Monitor (iQuam) online system collects in situ SSTs from various sources, performs quality control (QC), and provides QC’ed data to users. Like many other in situ QCs, the iQuam QC employs [...] Read more.

The NOAA in situ Sea Surface Temperature (SST) Quality Monitor (iQuam) online system collects in situ SSTs from various sources, performs quality control (QC), and provides QC’ed data to users. Like many other in situ QCs, the iQuam QC employs comparisons with Level 4 SST analysis. However, the current daily L4 analyses do not capture the diurnal cycle, nor do they resolve the fine structure of SST in dynamic areas. As a result, high-quality in situ SSTs significantly deviating from the L4 SST may be rejected. This paper discusses the new Diurnal Reference Check (DRC), which addresses overscreening for buoys whose sampling frequency is sufficient for resolving the diurnal cycle. The DRC separates records from individual buoys into 24-h segments and characterizes each segment with the median nighttime (MNT) SST and the amplitude of the diurnal signal (ADS). The segment is rejected if the ADS is unrealistically large or if the difference between the MNT and L4 SST exceeds a geographically dependent threshold. The outliers are further screened out by comparison of individual in situ SSTs with the MNT. All thresholds are determined from the analysis of matchups with reprocessed NOAA SSTs from multiple low-orbiting satellites. The satellite matchups are also used to validate the QC results. The DRC minimizes the overscreening, increases the number of high-quality in situ data by ~5%, and reduces the QC reliance on the L4 analysis. In addition, a new retrospective satellite-based quality check is introduced to identify matchups, which are most useful for training SST algorithms and validation of reprocessed satellite data. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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28 pages, 4991 KiB

Open AccessArticle

Marine Vessel Classification and Multivariate Trajectories Forecasting Using Metaheuristics-Optimized eXtreme Gradient Boosting and Recurrent Neural Networks

by Aleksandar Petrovic, Robertas Damaševičius, Luka Jovanovic, Ana Toskovic, Vladimir Simic, Nebojsa Bacanin, Miodrag Zivkovic and Petar Spalević

Appl. Sci. 2023, 13(16), 9181; https://doi.org/10.3390/app13169181 - 11 Aug 2023

Cited by 9 | Viewed by 1149

Abstract

Maritime vessels provide a wealth of data concerning location, trajectories, and speed. However, while these data are meticulously monitored and logged to maintain course, they can also provide a wealth of meta information. This work explored the potential of data-driven techniques and applied [...] Read more.

Maritime vessels provide a wealth of data concerning location, trajectories, and speed. However, while these data are meticulously monitored and logged to maintain course, they can also provide a wealth of meta information. This work explored the potential of data-driven techniques and applied artificial intelligence (AI) to tackle two challenges. First, vessel classification was explored through the use of extreme gradient boosting (XGboost). Second, vessel trajectory time series forecasting was tackled through the use of long-short-term memory (LSTM) networks. Finally, due to the strong dependence of AI model performance on proper hyperparameter selection, a boosted version of the well-known particle swarm optimization (PSO) algorithm was introduced specifically for tuning the hyperparameters of the models used in this study. The introduced methodology was applied to real-world automatic identification system (AIS) data for both marine vessel classification and trajectory forecasting. The performance of the introduced Boosted PSO (BPSO) was compared to contemporary optimizers and showed promising outcomes. The XGBoost model tuned using boosted PSO attained an overall accuracy of 99.72% for the vessel classification problem, while the LSTM model attained a mean square error (MSE) of 0.000098 for the marine trajectory prediction challenge. A rigid statistical analysis of the classification model was performed to validate outcomes, and explainable AI principles were applied to the determined best-performing models, to gain a better understanding of the feature impacts on model decisions. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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25 pages, 5230 KiB

Open AccessArticle

Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming

by Andromachi Chatziantoniou, Nikos Papandroulakis, Orestis Stavrakidis-Zachou, Spyros Spondylidis, Simeon Taskaris and Konstantinos Topouzelis

Appl. Sci. 2023, 13(10), 6122; https://doi.org/10.3390/app13106122 - 17 May 2023

Cited by 3 | Viewed by 2467

Abstract

Marine aquaculture has been expanding rapidly in recent years, driven by the growing demand for marine products. However, this expansion has led to increased competition for space and resources with other coastal zone activities, which has resulted in the need for larger facilities [...] Read more.

Marine aquaculture has been expanding rapidly in recent years, driven by the growing demand for marine products. However, this expansion has led to increased competition for space and resources with other coastal zone activities, which has resulted in the need for larger facilities and the relocation of operations to offshore areas. Moreover, the complex environment and exposure to environmental conditions and external threats further complicate the sustainable development of the sector. To address these challenges, new and innovative technologies are needed, such as the incorporation of remote sensing and in-situ data for comprehensive and continuous monitoring of aquaculture facilities. This study aims to create an integrated monitoring and decision support system utilizing both satellite and in-situ data to monitor aquaculture facilities on various scales, providing information on water quality, fish growth, and warning signs to alert managers and producers of potential hazards. This study focuses on identifying and estimating parameters that affect aquaculture processes, establishing indicators that can act as warning signs, and evaluating the system’s performance in real-life scenarios. The resulting monitoring tool, called “Aquasafe”, was evaluated for its effectiveness and performance by test users through real-life scenarios. The results of the implemented models showed high accuracy, with an R² value of 0.67. Additionally, users were generally satisfied with the usefulness of the tool, suggesting that it holds promise for efficient management and decision making in marine aquaculture. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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17 pages, 2699 KiB

Open AccessArticle

MEC: A Mesoscale Events Classifier for Oceanographic Imagery

by Gabriele Pieri, João Janeiro, Flávio Martins, Oscar Papini and Marco Reggiannini

Appl. Sci. 2023, 13(3), 1565; https://doi.org/10.3390/app13031565 - 25 Jan 2023

Cited by 2 | Viewed by 1377

Abstract

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence [...] Read more.

The observation of the sea through remote sensing technologies plays a fundamentalan role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT’s Metop and NASA’s Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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15 pages, 2291 KiB

Open AccessArticle

Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning

by Nuno Antunes, João C. Ferreira, José Pereira and Joana Rosa

Appl. Sci. 2022, 12(21), 11112; https://doi.org/10.3390/app122111112 - 02 Nov 2022

Viewed by 1334

Abstract

The application of anomaly-monitoring and surveillance systems is crucial for improving maritime situational awareness. These systems must work on the fly in order to provide the operator with information on potentially dangerous or illegal situations as they are occurring. We present a system [...] Read more.

The application of anomaly-monitoring and surveillance systems is crucial for improving maritime situational awareness. These systems must work on the fly in order to provide the operator with information on potentially dangerous or illegal situations as they are occurring. We present a system for identifying vessels deviating from their normal course of travel, from unlabelled AIS data. Our approach attempts to solve problems with scalability and on-line learning of other grid-based systems available in the literature, by applying a dynamic grid size, adjustable per vessel characteristics, combined with a binary-search tree method for data discretization and vessel grid search. The results of this study have been validated during the Portuguese Maritime Trial in April 2022, conducted by the Portuguese navy along the southern coast of Portugal. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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19 pages, 1837 KiB

Open AccessArticle

Simulation Tool for Winter Navigation Decision Support in the Baltic Sea

by Ketki Kulkarni, Pentti Kujala, Mashrura Musharraf and Ilari Rainio

Appl. Sci. 2022, 12(15), 7568; https://doi.org/10.3390/app12157568 - 27 Jul 2022

Cited by 6 | Viewed by 1177

Abstract

This article presents a novel simulation tool for the analysis of winter navigation operations in the Baltic Sea in the context of the Finnish–Swedish Winter Navigation System (FSWNS). The aim of the tool is to simulate the performance of the FSWNS under various [...] Read more.

This article presents a novel simulation tool for the analysis of winter navigation operations in the Baltic Sea in the context of the Finnish–Swedish Winter Navigation System (FSWNS). The aim of the tool is to simulate the performance of the FSWNS under various potential future operating scenarios and thereby support decision making in matters affecting the operation and development of the FSWNS, for instance, in terms of icebreaking resources and ice class regulations. To this end, the tool considers key performance factors and characteristics of the FSWNS, such as the prevailing ice conditions, the ice-going capability and other technical characteristics of the relevant merchant vessels, the availability of icebreaking resources, and the features of specific icebreaking operations (e.g., convoys). The tool would allow testing of several “what-if” scenarios, answering questions related to optimal engine power for safe, efficient, and environmentally friendly navigation and the optimal scheduling of icebreakers for effective and cost-efficient assistance missions. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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18 pages, 6622 KiB

Open AccessArticle

Web Based Spatio-Temporal Data Bidirectional Relationship Visualization—A Case Study of Oceanographic Data

by Damir Ivanković, Vlado Dadić, Ljiljana Šerić and Antonia Ivanda

Appl. Sci. 2022, 12(13), 6307; https://doi.org/10.3390/app12136307 - 21 Jun 2022

Viewed by 1328

Abstract

Environmental data visualization tools are used for data validation, maintenance, and assessment of status and trends of environmental variables. However, currently available visualization tools do not meet all the requirements needed for efficient data management. In this paper, we propose a new approach [...] Read more.

Environmental data visualization tools are used for data validation, maintenance, and assessment of status and trends of environmental variables. However, currently available visualization tools do not meet all the requirements needed for efficient data management. In this paper, we propose a new approach and present a web-based implementation of a visualization tool that focuses on efficient visualization and seamless integration of maps and graphs. Our approach emphasizes the spatio-temporal relationship of environmental data stored in a relational database. Several new advantages emerge with this approach. Through a case study of two oceanographic datasets—the HarmoNIA project database and the Croatian national monitoring database—we show how this approach enables intuitive status and trend assessments, simplifies data validation, enables the updating of corrections, and is suitable for a web-based implementation that works efficiently even with large datasets. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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13 pages, 10477 KiB

Open AccessArticle

A Study on the Characteristics of Fishery Resources Distribution in Coastal Waters of Yeongil Bay Using Acoustic Survey

by Yoo-Won Lee, Woo-Seok Oh, Dong-Soo Kim, Doo-Jin Hwang and Kyoung-Hoon Lee

Appl. Sci. 2021, 11(14), 6627; https://doi.org/10.3390/app11146627 - 19 Jul 2021

Viewed by 1719

Abstract

In this study, the identification of dominant fish species in the East Sea was conducted using the dB-difference method. The survey was conducted using the two frequencies of 38 and 120 kHz in transect 6 of the southern part of the East Sea. [...] Read more.

In this study, the identification of dominant fish species in the East Sea was conducted using the dB-difference method. The survey was conducted using the two frequencies of 38 and 120 kHz in transect 6 of the southern part of the East Sea. Information on fish species was identified using fishing gear and e-DNA, and the dominant target fish species were selected and analyzed as cod, anchovy, common squid, and herring. The dB-difference range for each fish species was set to −0.86 dB < ∆MVBS _{38–120 kHz} < 0.82 dB for cod and to the range of 2.66 dB < ∆MVBS _{38–120 kHz} < 2.84 dB for anchovy. The dB-difference of the common squid was set to −0.36 dB < ∆MVBS _{38–120 kHz} < 1.25 dB and to the range of 0.88 dB < ∆MVBS _{38–120 kHz} < 2.28 dB for herring; the fish species were then identified in the echograms. When comparing the results of swimming depths by fish species and previous studies, cod was detected mainly at the bottom of the sea, and anchovy and common squid were detected mainly at a depth of 50 m. Herring was detected to be mainly distributed in water depths from 50 to 150 m. Full article

(This article belongs to the Special Issue Intelligent Systems Applied to Maritime Environment Monitoring)

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