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

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 m2·h/km2. Finally, this study provides a scientific basis for TACs project management and a VMS data mining and application expansion standard.

Deep Learning-Based Fishing Ground Prediction Using Asymmetric Spatiotemporal Scales: A Case Study of Ommastrephes bartramii

“…Linear or nonlinear models are then established for the prediction of fishing grounds. Currently, traditional fishing ground prediction methods such as generalized additive model [5], habitat suitability index model [6], and artificial neural network [7] have achieved notable results. However, with the advent of the big data era in ocean remote sensing and fisheries, traditional methods have struggled to extract valuable, sparse information accurately and efficiently from complex and extensive datasets.…”