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Advances in Ocean Monitoring and Modeling for Marine Biology
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Advances in Ocean Monitoring and Modeling for Marine Biology

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Marine Biology".

Deadline for manuscript submissions: closed (15 February 2023) | Viewed by 9393

Special Issue Editor

Dr. Hiroshi Kuroda

E-Mail Website
Guest Editor
Japan Fisheries Research and Education Agency, Hokkaido, Japan
Interests: physical oceanography; fisheries oceanography; ocean modeling; ocean forecast system; ocean monitoring; coupled physical–biochemical modeling; remote sensing; climate change

Special Issue Information

Dear Colleagues,

We invite you to submit your work to the Special Issue “Advances in Ocean Monitoring and Modeling for Marine Biology”. Conventional in situ ocean monitoring for understanding marine biology and ecosystems has been conducted by research vessels with CTD sensors, water sampling bottles, and sampling nets. Advanced technology has also provided new platforms (e.g., satellite, UAV, AUV, glider, drifting float, and free-living animal) and new devices (e.g., digital sensor, underwater camera, acoustic technique, and biologging) for ocean monitoring. Moreover, the recent rapid progress of computational techniques and resources has allowed us to easily conduct numerical modeling for marine biology, by coupling a realistic ocean circulation model with biological models (e.g., a simplified particle-tracking or grid-based tracer model, marine ecosystem model, statistical model such as habitat model, and individual-based model). Furthermore, results from coupled models have been validated using chemical and biological parameters measured with a new method, which includes trace elements, isotope, radioisotope, eDNA, and genomic analysis. These kinds of marine biological works to understand marine ecosystems have become increasingly important in recent decades in terms of sustainable usage, conservation, and management of marine ecosystems in the world.

The aim of this Special Issue is to promote and disseminate the latest works with a special focus on ocean monitoring and modeling for marine biology and ecosystem, using individual or interdisciplinary approach. Ocean monitoring and modeling with innovative methods for conserving and managing marine ecosystems are also welcome in this Special Issue.

Dr. Hiroshi Kuroda
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Ocean monitoring
  • Ocean modeling
  • Marine biology
  • Marine ecosystem
  • Lower trophic level
  • Higher trophic level
  • Conventional method
  • Advanced method
  • Interdisciplinary work

Published Papers (4 papers)

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Research

26 pages, 11952 KiB  
Article
A Marine Organism Detection Framework Based on Dataset Augmentation and CNN-ViT Fusion
by Xiao Jiang, Yaxin Zhang, Mian Pan, Shuaishuai Lv, Gang Yang, Zhu Li, Jingbiao Liu and Haibin Yu
J. Mar. Sci. Eng. 2023, 11(4), 705; https://doi.org/10.3390/jmse11040705 - 24 Mar 2023
Viewed by 1058
Abstract
Underwater vision-based detection plays an important role in marine resources exploration, marine ecological protection and other fields. Due to the restricted carrier movement and the clustering effect of some marine organisms, the size of some marine organisms in the underwater image is very [...] Read more.
Underwater vision-based detection plays an important role in marine resources exploration, marine ecological protection and other fields. Due to the restricted carrier movement and the clustering effect of some marine organisms, the size of some marine organisms in the underwater image is very small, and the samples in the dataset are very unbalanced, which aggravate the difficulty of vision detection of marine organisms. To solve these problems, this study proposes a marine organism detection framework with a dataset augmentation strategy and Convolutional Neural Networks (CNN)-Vision Transformer (ViT) fusion model. The proposed framework adopts two data augmentation methods, namely, random expansion of small objects and non-overlapping filling of scarce samples, to significantly improve the data quality of the dataset. At the same time, the framework takes YOLOv5 as the baseline model, introduces ViT, deformable convolution and trident block in the feature extraction network, and extracts richer features of marine organisms through multi-scale receptive fields with the help of the fusion of CNN and ViT. The experimental results show that, compared with various one-stage detection models, the mean average precision (mAP) of the proposed framework can be improved by 27%. At the same time, it gives consideration to both performance and real-time, so as to achieve high-precision real-time detection of the marine organisms on the underwater mobile platform. Full article
(This article belongs to the Special Issue Advances in Ocean Monitoring and Modeling for Marine Biology)
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18 pages, 3146 KiB  
Article
Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S
by Peng Li, Yibing Fan, Zhengyang Cai, Zhiyu Lyu and Weijie Ren
J. Mar. Sci. Eng. 2022, 10(10), 1503; https://doi.org/10.3390/jmse10101503 - 16 Oct 2022
Cited by 7 | Viewed by 1674
Abstract
Marine biological object detection is of great significance for the exploration and protection of underwater resources. There have been some achievements in visual inspection for specific objects based on machine learning. However, owing to the complex imaging environment, some problems, such as low [...] Read more.
Marine biological object detection is of great significance for the exploration and protection of underwater resources. There have been some achievements in visual inspection for specific objects based on machine learning. However, owing to the complex imaging environment, some problems, such as low accuracy and poor real-time performance, have appeared in these object detection methods. To solve these problems, this paper proposes a detection method of marine biological objects based on image enhancement and YOLOv5S. Contrast-limited adaptive histogram equalization is taken to solve the problems of underwater image distortion and blur, and we put forward an improved YOLOv5S to improve accuracy and real-time performance of object detection. Compared with YOLOv5S, coordinate attention and adaptive spatial feature fusion are added in the improved YOLOv5S, which can accurately locate the target of interest and fully fuse the features of different scales. In addition, soft non-maximum suppression is adopted to replace non-maximum suppression for the improvement of the detection ability for overlapping objects. The experimental results show that the contrast-limited adaptive histogram equalization algorithm can effectively improve the underwater image quality and the detection accuracy. Compared with the original model (YOLOv5S), the proposed algorithm has a higher detection accuracy. The detection accuracy AP50 reaches 94.9% and the detection speed is 82 frames per second; therefore, the real-time performance can be said to reach a high level. Full article
(This article belongs to the Special Issue Advances in Ocean Monitoring and Modeling for Marine Biology)
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9 pages, 1439 KiB  
Article
Underwater Biological Detection Based on YOLOv4 Combined with Channel Attention
by Aolun Li, Long Yu and Shengwei Tian
J. Mar. Sci. Eng. 2022, 10(4), 469; https://doi.org/10.3390/jmse10040469 - 26 Mar 2022
Cited by 11 | Viewed by 2223
Abstract
Due to the influence of underwater visual characteristics on the observation of underwater creatures, the traditional object detection algorithm is ineffective. In order to improve the robustness of underwater biological detection, based on the YOLOv4 detector, this paper proposes an underwater biological detection [...] Read more.
Due to the influence of underwater visual characteristics on the observation of underwater creatures, the traditional object detection algorithm is ineffective. In order to improve the robustness of underwater biological detection, based on the YOLOv4 detector, this paper proposes an underwater biological detection algorithm combined with the channel attention mechanism. Firstly, the backbone feature extraction network CSPDarknet53 of YOLOv4 was improved, and a residual block combined with the channel attention mechanism was proposed to extract the weighted multi-scale effective features. Secondly, the weighted features were repeatedly extracted through the feature pyramid to separate the most significant weighted features. Finally, the most salient weighted multi-scale features were used for underwater biological detection. The experimental results show that, compared with YOLOv4, the proposed algorithm improved the average accuracy of the Brackish underwater creature dataset detection by 5.03%, and can reach a detection rate of 15fps for underwater creature video clips. Therefore, it is feasible to apply this method to the accurate and real-time detection of underwater creatures. This research can provide technical reference for the exploration of marine ecosystems and the development of underwater robots. Full article
(This article belongs to the Special Issue Advances in Ocean Monitoring and Modeling for Marine Biology)
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21 pages, 9006 KiB  
Article
Unprecedented Outbreak of Harmful Algae in Pacific Coastal Waters off Southeast Hokkaido, Japan, during Late Summer 2021 after Record-Breaking Marine Heatwaves
by Hiroshi Kuroda, Tomonori Azumaya, Takashi Setou and Natsuki Hasegawa
J. Mar. Sci. Eng. 2021, 9(12), 1335; https://doi.org/10.3390/jmse9121335 - 27 Nov 2021
Cited by 22 | Viewed by 3880
Abstract
Unprecedented large-scale harmful algae blooms (HABs) were reported in coastal waters off the south-eastern coast of Hokkaido, Japan, in mid-to-late September 2021, about a month after very intense and extensive marine heatwaves subsided. To understand the physical–biological processes associated with development of the [...] Read more.
Unprecedented large-scale harmful algae blooms (HABs) were reported in coastal waters off the south-eastern coast of Hokkaido, Japan, in mid-to-late September 2021, about a month after very intense and extensive marine heatwaves subsided. To understand the physical–biological processes associated with development of the HABs, we conducted analyses via a combination of realistic ocean circulation models, particle-tracking simulations, and satellite measurements. The satellite-derived chlorophyll concentrations (SCCs) and areal extent of the high SCCs associated with the HABs were the highest recorded since 1998. More specifically, the extent of SCCs exceeding 5 or 10 mg m−3 started to slowly increase after 20 August, when the marine heatwaves subsided, intermittently exceeded the climatological daily maximum after late August, and reached record-breaking extremes in mid-to-late September. About 70% of the SCCs that exceeded 10 mg m−3 occurred in places where water depths were <300 m, i.e., coastal shelf waters. The high SCCs were also tightly linked with low-salinity water (e.g., subarctic Oyashio and river-influenced waters). High-salinity subtropical water (e.g., Soya Warm Current water) appeared to suppress the occurrence of HABs. The expansion of the area of high SCCs seemed to be synchronized with the deepening of surface mixed layer depths in subarctic waters on the Pacific shelves. That deepening began around 10 August, when the marine heatwaves weakened abruptly. However, another mechanism was needed to explain the intensification of the SCCs in very nearshore waters off southeast Hokkaido. Particle-tracking simulations based on ocean circulation models identified three potential source areas of the HABs: the Pacific Ocean east of the Kamchatka Peninsula, the Sea of Japan, and the Sea of Okhotsk east of the Sakhalin Island. Different processes of HAB development were proposed because distance, time, and probability for transport of harmful algae from the potential source areas to the study region differed greatly between the three source areas. Full article
(This article belongs to the Special Issue Advances in Ocean Monitoring and Modeling for Marine Biology)
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