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

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