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Characterizing Fishing Behaviors and Intensity of Vessels Based on BeiDou VMS Data: A Case Study of TACs Project for Acetes chinensis in the Yellow Sea

Author

Listed:
  • Guodong Li

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Jiangsu Marine Fisheries Research Institute, Nantong 226007, China)

  • Ying Xiong

    (Jiangsu Marine Fisheries Research Institute, Nantong 226007, China)

  • Xiaming Zhong

    (Jiangsu Marine Fisheries Research Institute, Nantong 226007, China)

  • Dade Song

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Jiangsu Marine Fisheries Research Institute, Nantong 226007, China)

  • Zhongjie Kang

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Jiangsu Marine Fisheries Research Institute, Nantong 226007, China)

  • Dongjia Li

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Jiangsu Marine Fisheries Research Institute, Nantong 226007, China)

  • Fan Yang

    (College of Fisheries and Life Science, Shanghai Ocean University, Shanghai 201306, China)

  • Xiaorui Wu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Jiangsu Marine Fisheries Research Institute, Nantong 226007, China)

Abstract

The total allowable catch system (TACs) is a basic, widely used system for maintaining marine fishery resources. The vessel monitoring system (VMS) provides a superior method to monitor fishing activities that serve TACs project management. However, few studies have been conducted on this topic. Here, an artificial neural network was used to identify vessel position states based on BeiDou VMS data and fishing logs of vessels under the TACs project for Acetes chinensis in the Yellow Sea in 2021. Furthermore, fishing behaviors and intensity were explored. The results showed significant differences in the speed of vessels in different states ( p < 0.01). Casting occurred during the day, and the azimuth of fishing nets for shrimp ranged from 60 to 90° or 240 to 270°. The length of the fishing nets of each vessel was mostly between 3500 and 4500 m. In addition, the fishing efforts of the vessels showed an obvious aggregated distribution. The main area was at 120°04′–120°16′ E, 34°42′–34°46′ N, whereas fishing intensity ranged from 120,000 to 280,000 m 2 ·h/km 2 . Finally, this study provides a scientific basis for TACs project management and a VMS data mining and application expansion standard.

Suggested Citation

  • Guodong Li & Ying Xiong & Xiaming Zhong & Dade Song & Zhongjie Kang & Dongjia Li & Fan Yang & Xiaorui Wu, 2022. "Characterizing Fishing Behaviors and Intensity of Vessels Based on BeiDou VMS Data: A Case Study of TACs Project for Acetes chinensis in the Yellow Sea," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7588-:d:844687
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    References listed on IDEAS

    as
    1. Walker, E. & Bez, N., 2010. "A pioneer validation of a state-space model of vessel trajectories (VMS) with observers’ data," Ecological Modelling, Elsevier, vol. 221(17), pages 2008-2017.
    2. Joo, Rocío & Bertrand, Sophie & Chaigneau, Alexis & Ñiquen, Miguel, 2011. "Optimization of an artificial neural network for identifying fishing set positions from VMS data: An example from the Peruvian anchovy purse seine fishery," Ecological Modelling, Elsevier, vol. 222(4), pages 1048-1059.
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