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

文献类型： 外文期刊

作者： Pei, Kaiyang 1 ; Zhang, Jiaze 1 ; Zhang, Shengmao 1 ; Sui, Yanming 3 ; Zhang, Heng 1 ; Tang, Fenghua 1 ; Yang, Shenglong 1 ;

作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fishery Res Inst, Key Lab East China Sea & Ocean Fishery Resources E, Minist Agr, Shanghai 200090, Peoples R China

2.Shanghai Ocean Univ, Coll Informat Technol, Shanghai 201306, Peoples R China

3.Yancheng Inst Technol, Sch Marine & Biol Engn, Yancheng 224002, Peoples R China

关键词： protection; China; DBSCAN; Deep neural network; Fishing intensity; Spatial distribution; VMS; Voyage extraction

期刊名称：INDIAN JOURNAL OF FISHERIES （ 影响因子：0.5； 五年影响因子：0.6 ）

ISSN： 0970-6011

年卷期： 2023 年 70 卷 1 期

页码：

收录情况： SCI

摘要： 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 degrees x0.1 degrees 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

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