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Sustainable Development of Our Oceans and Coastal Zones through AI and Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 14488

Special Issue Editors

Prof. Dr. Robin Teigland

E-Mail Website
Guest Editor
Chalmers University of Technology and Ocean Data Factory, Sweden
Interests: Artificial Intelligence; machine learning; emerging technologies; innovation; ocean
Dr. Torsten Linders

E-Mail Website
Guest Editor
University of Gothenburg/SCOOT- Swedish Centre for Ocean Observing Technology, and Ocean Data Factory, Sweden
Interests: physical oceanography; ocean robotics; AI innovation
Dr. Sergio Leandro

E-Mail Website
Guest Editor
MARE – Marine and Environmental Sciences Centre, ESTM, Polytechnic Institute of Leiria, Portugal
Smart Ocean Peniche, Portugal and Ocean Data Factory, Sweden
Interests: biodiversity and ecosystem functioning; aquaculture and fisheries; biotechnology and resources valorization; technological tools for exploration and monitoring
Dr. Ivan Masmitja

E-Mail Website
Guest Editor
Department of Physical and Technological Oceanography, Institut de Ciències del Mar (ICM), Consejo Superior de Investigaciones Científicas (CSIC), 08003 Barcelona, Spain
Interests: underwater target tracking; underwater cabled observatories and autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 2021, we will enter the United Nations Decade of Ocean Science for Sustainable Development (2021–2030). The goal of this proclamation is to encourage efforts across the globe to reverse the decline in ocean health as climate change, fishing, pollution, and other human factors have damaged 96% of the world’s oceans, with more than 40% already seriously damaged. At the same time, other organizations, such as the European Union, are turning to the ocean and coastal environments as a source of economic growth in what is labeled “The Blue Economy”. Opportunities within areas such as energy, food, medicine, transportation, mining, and tourism abound. To reverse the decline while ensuring the sustainable development of the ocean and coastal zones is a considerable challenge, and many are increasingly looking to the application of artificial intelligence to ocean data as a potential solution. On the one hand, the amount of ocean remote sensing data is growing exponentially due to continuous developments in space, sensor, and robotics technologies and expanding data collection possibilities through an increasing number of sensors and arenas, such as deep ocean hydroacoustics. On the other hand, advances in artificial intelligence, in areas such as machine learning and neural networks, offer great promise in the ability to mine and sustainably leverage ocean data. However, data-driven marine and maritime research lags considerably behind land-based research.

To reduce this knowledge gap, we at Ocean Data Factory Sweden and SmartOcean Portugal would like to invite articles for a Special Issue on the “Sustainable Development of Our Oceans and Coastal Zones through AI and Remote Sensing”. Research topics include but are not limited to the collection, preparation, fusion, storage, streaming, and algorithm development and deployment for remote sensing ocean data using signals propagating through space, air, and water in the following areas:

  • Monitoring and forecasting of ocean and coastal environments;
  • Solutions for pollution and climate change in ocean and coastal environments;
  • Sustainable development and management of ocean resources, e.g., renewables, seafood, raw materials;
  • Coastal and maritime spatial planning;
  • Ocean economics applied to marine and maritime activities;
  • Organization of remote sensing data-driven ocean innovation, including open innovation and citizen science.

Prof. Dr. Robin Teigland
Dr. Torsten Linders
Dr. Sergio Leandro
Dr. Ivan Masmitja
Guest Editors

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • AI
  • ML
  • remote sensing
  • sustainability
  • innovation
  • marine
  • maritime
  • ocean

Published Papers (3 papers)

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Research

23 pages, 7691 KiB  
Article
Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data
by Ali Jamali and Masoud Mahdianpari
Remote Sens. 2022, 14(2), 359; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14020359 - 13 Jan 2022
Cited by 33 | Viewed by 6302
Abstract
The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in [...] Read more.
The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification. On the other hand, the state-of-the-art natural language processing (NLP) algorithms are transformers. Although the transformers have been studied for a few remote sensing applications, the integration of deep CNNs and transformers has not been studied, particularly in wetland mapping. As such, in this study, we explore the potential and possible limitations to be overcome regarding the use of a multi-model deep learning network with the integration of a modified version of the well-known deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. Moreover, we discuss the potential and limitation of the proposed multi-model technique over several solo models, including a random forest (RF), support vector machine (SVM), VGG-16, 3D CNN, and Swin transformer in the pilot site of Saint John city located in New Brunswick, Canada. In terms of F-1 score, the multi-model network obtained values of 0.87, 0.88, 0.89, 0.91, 0.93, 0.93, and 0.93 for the recognition of shrub wetland, fen, bog, aquatic bed, coastal marsh, forested wetland, and freshwater marsh, respectively. The results suggest that the multi-model network is superior to other solo classifiers from 3.36% to 33.35% in terms of average accuracy. Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing. Full article
(This article belongs to the Special Issue Sustainable Development of Our Oceans and Coastal Zones through AI and Remote Sensing)
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23 pages, 7017 KiB  
Article
Assessing the Impacts of Rising Sea Level on Coastal Morpho-Dynamics with Automated High-Frequency Shoreline Mapping Using Multi-Sensor Optical Satellites
by Naheem Adebisi, Abdul-Lateef Balogun, Masoud Mahdianpari and Teh Hee Min
Remote Sens. 2021, 13(18), 3587; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183587 - 09 Sep 2021
Cited by 8 | Viewed by 3403
Abstract
Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same [...] Read more.
Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same temporal scale as sea-level records. Using a Google Earth Engine (GEE)-enabled Python toolkit, this study conducted shoreline dynamic analysis using high-frequency data sampling to analyze the impact of sea-level rise on the Malaysian coastline between 1993 and 2019. Instantaneous shorelines were extracted from a test site on Teluk Nipah Island and 21 tide gauge sites from the combined Landsat 5–8 and Sentinel 2 images using an automated shoreline-detection method, which was based on supervised image classification and sub-pixel border segmentation. The results indicated that rising sea level is contributing to shoreline erosion in the study area, but is not the only driver of shoreline displacement. The impacts of high population density, anthropogenic activities, and longshore sediment transportation on shoreline displacement were observed in some of the beaches. The conclusions of this study highlight that the synergistic use of multi-sensor remote-sensing data improves temporal resolution of shoreline detection, removes short-term variability, and reduces uncertainties in satellite-derived shoreline analysis compared to the low-frequency sampling approach. Full article
(This article belongs to the Special Issue Sustainable Development of Our Oceans and Coastal Zones through AI and Remote Sensing)
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32 pages, 11570 KiB  
Article
Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018
by Yanan Guan, Jie Zhang, Xi Zhang, Zhongwei Li, Junmin Meng, Genwang Liu, Meng Bao and Chenghui Cao
Remote Sens. 2021, 13(10), 1952; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13101952 - 17 May 2021
Cited by 19 | Viewed by 3646
Abstract
In recent years, concern has increased about the depletion of marine resources caused by the overexploitation of fisheries and the degradation of ecosystems. The Automatic Identification System (AIS) is a powerful tool increasingly used for monitoring marine fishing activity. In this paper, identification [...] Read more.
In recent years, concern has increased about the depletion of marine resources caused by the overexploitation of fisheries and the degradation of ecosystems. The Automatic Identification System (AIS) is a powerful tool increasingly used for monitoring marine fishing activity. In this paper, identification of the type of fishing vessel (trawlers, gillnetters and seiners) was carried out using 150 million AIS tracking points in April, June and September 2018 in the northern South China Sea (SCS). The vessels’ spatial and temporal distribution, duration of fishing time and other activity patterns were analyzed in different seasons. An identification model for fishing vessel types was developed using a Light Gradient Boosting Machine (LightGBM) approach with three categories with a total of 60 features: speed and heading, location changes, and speed and displacement in multiple states. The accuracy of this model reached 95.68%, which was higher than other advanced algorithms such as XGBoost. It was found that the activity hotspots of Chinese fishing vessels, especially trawlers, showed a tendency to move northward through the year in the northern SCS. Furthermore, Chinese fishing vessels showed low fishing intensity during the fishing moratorium months and traditional Chinese holidays. This research work indicates the value of AIS data in providing decision-making assistance for the development of fishery resources and marine safety management in the northern SCS. Full article
(This article belongs to the Special Issue Sustainable Development of Our Oceans and Coastal Zones through AI and Remote Sensing)
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