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

Abstract

Present study used the position data of BeiDou Vessel Monitoring System (VMS) in 2018, with respect to motorised fishingvessels in the East China Sea and the Yellow Sea to construct a fishing vessel operating status classification model based onthreshold, deep neural network and DBSCAN density clustering algorithm. The geographic grid was divided into cells of0.1°×0.1° and the average fishing time per square km (h km-2) in each grid was calculated to obtain the spatial distributionof fishing intensity in the study region in 2018. The results showed that the threshold method could classify fishing vesselsailing, anchoring and other states with an accuracy of more than 95%. The deep neural network and DBSCAN algorithmcould 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 ecologicalprotectionKeywords: China, DBSCAN, Deep neural network, Fishing intensity, Spatial distribution, VMS, Voyage extraction

Behavior Recognition of Squid Jigger Based on Deep Learning

“…Nonetheless, the study also highlighted that the effectiveness of EMS in identifying objects was highly dependent on the location of the cameras, with identification rates ranging from 56.3% to 98.3% in their experiments. Therefore, there is still considerable room for improvement in the data processing of captured information on board using EMS [5].…”