Spatial distribution of fishing intensity of canvas stow net fishing vessels in the East China Sea and the Yellow Sea


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Authors

  • Kaiyang Pei College of Information Technology, Shanghai Ocean University, China, Shanghai, 201306
  • Jiaze Zhang College of Information Technology, Shanghai Ocean University, China, Shanghai, 201306
  • shengmao Zhang Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fishery Research Institute, Chinese Academy of Fishery Sciences, China, Shanghai, 200090
  • Yanming Sui
  • Heng Zhang Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fishery Research Institute, Chinese Academy of Fishery Sciences, China, Shanghai, 200090
  • Fenghua Tang Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fishery Research Institute, Chinese Academy of Fishery Sciences, China, Shanghai, 200090
  • Shenglong Yang Key Laboratory of East China Sea and Oceanic Fishery Resources Exploitation and Utilization, Ministry of Agriculture, P.R. China, East China Sea Fishery Research Institute, Chinese Academy of Fishery Sciences, China, Shanghai, 200090

https://doi.org/10.21077/ijf.2023.70.1.125766-01

Abstract

 Present study used the position data of BeiDou Vessel Monitoring System (VMS) in 2018, with respect to motorised fishing
vessels in the East China Sea and the Yellow Sea to construct a fishing vessel operating status classification model based on
threshold, deep neural network and DBSCAN density clustering algorithm. The geographic grid was divided into cells of
0.1°×0.1° and the average fishing time per square km (h km-2) in each grid was calculated to obtain the spatial distribution
of fishing intensity in the study region in 2018. The results showed that the threshold method could classify fishing vessel
sailing, anchoring and other states with an accuracy of more than 95%. The deep neural network and DBSCAN algorithm
could classify the two states of netting and closing with an accuracy of 94.7%. By classifying the status of fishing vessels,
quantitative monitoring can be carried out to better serve the management of marine fishery resources and marine ecological
protection
Keywords: China, DBSCAN, Deep neural network, Fishing intensity, Spatial distribution, VMS, Voyage extraction

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Submitted

2022-07-17

Published

2023-03-31

Issue

Vol. 70 No. 1 (2023)

Section

Articles

License

The copyright of the articles published in Indian Journal of Fisheries vests with the Indian Council of Agricultural Research, who has the right to enter into any agreement with any organization in India or abroad engaged in reprography, photocopying, storage and dissemination of information contained in these journals. The Council has no objection in using the material, provided the information is being utilized for academic purpose but not for commercial use. Due credit line should be given to the ICAR where information will be utilized.

How to Cite

Pei, K., Zhang, J., Zhang, shengmao, Yanming Sui, Zhang, H., Tang, F., & Yang, S. (2023). Spatial distribution of fishing intensity of canvas stow net fishing vessels in the East China Sea and the Yellow Sea. Indian Journal of Fisheries, 70(1). https://doi.org/10.21077/ijf.2023.70.1.125766-01