Identification of Fishing Vessel Types and Analysis of Seasonal Activities in the Northern South China Sea Based on AIS Data: A Case Study of 2018

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 10; p. 1952
• Main Authors: Guan, Yanan, Zhang, Jie, Zhang, Xi, Li, Zhongwei, Meng, Junmin, Liu, Genwang, Bao, Meng, Cao, Chenghui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2021
• Subjects: Activity patterns; Algorithms; Automatic Identification System (AIS) data; Classification; Commercial fishing; Decision making; Depletion; Ecosystem degradation; Fisheries; Fishery development; Fishery resources; Fishing; fishing density; Fishing vessels; Machine learning; Marine fish; Marine resources; Model accuracy; Neural networks; Overexploitation; Remote sensing; Safety management; Satellites; Sea vessels; seasonal activities analysis; Seiners; Temporal distribution; the northern South China Sea (SCS); Trawlers; vessel type identification; South China Sea
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13101952

In recent years, concern has increased about the depletion of marine resources caused by the overexploitation of fisheries and the degradation of ecosystems. The Automatic Identification System (AIS) is a powerful tool increasingly used for monitoring marine fishing activity. In this paper, identification of the type of fishing vessel (trawlers, gillnetters and seiners) was carried out using 150 million AIS tracking points in April, June and September 2018 in the northern South China Sea (SCS). The vessels’ spatial and temporal distribution, duration of fishing time and other activity patterns were analyzed in different seasons. An identification model for fishing vessel types was developed using a Light Gradient Boosting Machine (LightGBM) approach with three categories with a total of 60 features: speed and heading, location changes, and speed and displacement in multiple states. The accuracy of this model reached 95.68%, which was higher than other advanced algorithms such as XGBoost. It was found that the activity hotspots of Chinese fishing vessels, especially trawlers, showed a tendency to move northward through the year in the northern SCS. Furthermore, Chinese fishing vessels showed low fishing intensity during the fishing moratorium months and traditional Chinese holidays. This research work indicates the value of AIS data in providing decision-making assistance for the development of fishery resources and marine safety management in the northern SCS.