IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9706-d1173362.html
   My bibliography  Save this article

Spaceborne SAR-Based Detection of Ships in Suez Gulf to Analyze the Maritime Traffic Jam Caused Due to the Blockage of Egypt’s Suez Canal

Author

Listed:
  • Ananya Sonkar

    (Department of Electronics and Communication Engineering, Indira Gandhi Delhi Technical University for Women, Delhi 110006, India)

  • Shashi Kumar

    (Photogrammetry and Remote Sensing Department, Indian Institute of Remote Sensing (IIRS), Indian Space Research Organisation (ISRO), Dehradun 248001, India)

  • Navneet Kumar

    (Department of Ecology and Natural Resource Management, Center for Development Research (ZEF), University of Bonn, Genscherallee 3, 53113 Bonn, Germany)

Abstract

With the convenience and connectedness of the oceans in recent years, there has been an increase in naval traffic, which has prompted maritime surveillance to attract special attention due to its significant application in marine operations. Ships, because of their uneven and rugged design, appear as a brighter patch, which aids in their identification by the Synthetic Aperture Radar (SAR), an active remote sensing technique. In this study, Sentinel-1 and Sentinel-2 datasets are used to detect vessels in the Gulf of Suez in order to examine the increasing maritime traffic induced by the Suez Canal blockage caused by the Ever Given ship becoming stranded in the canal on 23 March 2021 and being freed after 6 days on 29 March 2021. The usefulness of dual-pol spaceborne SAR datasets in ship detection is also determined. The analysis was performed within a time window spanning before, during, and after the blockage event. On the basis of the experimental results, Sentinel-1 images proved to be more effective compared to Sentinel-2 images for ship detection due to the all-weather capability of the Sentinel-1 dataset. Furthermore, the ship detection results obtained in dual polarization were substantially more accurate than the results obtained in a single polarization.

Suggested Citation

  • Ananya Sonkar & Shashi Kumar & Navneet Kumar, 2023. "Spaceborne SAR-Based Detection of Ships in Suez Gulf to Analyze the Maritime Traffic Jam Caused Due to the Blockage of Egypt’s Suez Canal," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9706-:d:1173362
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9706/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/12/9706/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Du, Lei & Goerlandt, Floris & Kujala, Pentti, 2020. "Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    2. Gast, Johannes & Binsfeld, Tom & Marsili, Francesca & Jahn, Carlos, 2021. "Analysis of the Suez Canal blockage with queueing theory," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 943-959, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cai, Mingyou & Zhang, Jinfen & Zhang, Di & Yuan, Xiaoli & Soares, C. Guedes, 2021. "Collision risk analysis on ferry ships in Jiangsu Section of the Yangtze River based on AIS data," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Puisa, Romanas & Montewka, Jakub & Krata, Przemyslaw, 2023. "A framework estimating the minimum sample size and margin of error for maritime quantitative risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2022. "Maritime traffic probabilistic prediction based on ship motion pattern extraction," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Zhang, Weibin & Feng, Xinyu & Goerlandt, Floris & Liu, Qing, 2020. "Towards a Convolutional Neural Network model for classifying regional ship collision risk levels for waterway risk analysis," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    5. Silveira, P. & Teixeira, A.P. & Figueira, J.R. & Guedes Soares, C., 2021. "A multicriteria outranking approach for ship collision risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    6. Mauro, Francesco & Vassalos, Dracos & Paterson, Donald, 2022. "Critical damages identification in a multi-level damage stability assessment framework for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    7. Szlapczynski, Rafal & Szlapczynska, Joanna, 2021. "A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    8. Ruponen, Pekka & Montewka, Jakub & Tompuri, Markus & Manderbacka, Teemu & Hirdaris, Spyros, 2022. "A framework for onboard assessment and monitoring of flooding risk due to open watertight doors for passenger ships," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    9. Gil, Mateusz & Kozioł, Paweł & Wróbel, Krzysztof & Montewka, Jakub, 2022. "Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    10. Rong, H. & Teixeira, A.P. & Guedes Soares, C., 2021. "Spatial correlation analysis of near ship collision hotspots with local maritime traffic characteristics," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    11. Xin, Xuri & Liu, Kezhong & Loughney, Sean & Wang, Jin & Yang, Zaili, 2023. "Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    12. Zhao, Jiansen & Lu, Jinquan & Chen, Xinqiang & Yan, Zhongwei & Yan, Ying & Sun, Yang, 2022. "High-fidelity data supported ship trajectory prediction via an ensemble machine learning framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    13. Yang, Zhisen & Wan, Chengpeng & Yang, Zaili & Yu, Qing, 2021. "Using Bayesian network-based TOPSIS to aid dynamic port state control detention risk control decision," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    14. Liu, Kezhong & Yu, Qing & Yang, Zhisen & Wan, Chengpeng & Yang, Zaili, 2022. "BN-based port state control inspection for Paris MoU: New risk factors and probability training using big data," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    15. Abaei, Mohammad Mahdi & Hekkenberg, Robert & BahooToroody, Ahmad, 2021. "A multinomial process tree for reliability assessment of machinery in autonomous ships," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    16. Pauer, Gábor & Török, à rpád, 2022. "Introducing a novel safety assessment method through the example of a reduced complexity binary integer autonomous transport model," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    17. Zhang, Mingyang & Montewka, Jakub & Manderbacka, Teemu & Kujala, Pentti & Hirdaris, Spyros, 2021. "A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    18. Murray, Brian & Perera, Lokukaluge Prasad, 2021. "An AIS-based deep learning framework for regional ship behavior prediction," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    19. Montewka, Jakub & Manderbacka, Teemu & Ruponen, Pekka & Tompuri, Markus & Gil, Mateusz & Hirdaris, Spyros, 2022. "Accident susceptibility index for a passenger ship-a framework and case study," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    20. Xin, Xuri & Liu, Kezhong & Yang, Zaili & Zhang, Jinfen & Wu, Xiaolie, 2021. "A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9706-:d:1173362. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.