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

Abstract

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.

The Probability of Ship Collision during the Fully Submerged Towing Process of Floating Offshore Wind Turbines

“…The first stage is scheduled to commence commercial power generation in 2024, and the overall project will be completed and connected to the grid by the end of 2027. Due to the absence of freight ports in the vicinity of the FOWF region, as indicated by statistical data from the China Maritime Safety Administration's Automatic Identification System (AIS) [28], and in accordance with research findings by Wang [29,30], Wan [31], and Guan et al [32], it has been observed that within a 50 km radius around the FOWF, there is a lack of major shipping channels. The predominant vessels navigating in this proximity are several cargo vessels, and most of them are small and mid-sized fishing boats, as evidenced by the results presented in Table 3.…”

“…The analysis is confined solely to scenarios of collisions between fishing vessels and shipping fleets. Due to the lack of publicly available monthly statistics on the number of fishing vessels in various sea areas from Chinese official sources, this paper utilizes data for the months of April, June, and September published by Guan et al [32] in their research, with detailed data presented in Table 6. According to the data, the target sea area encompasses approximately 945,000 km 2 .…”

Global Fisheries Responses to Culture, Policy and COVID-19 from 2017 to 2020

“…Improvements are possible by combining AIS and remote sensing data to increase the effectiveness of fishing vessel activity monitoring. The enhanced monitoring will help in the implementation and monitoring of fishery policies [48][49][50][51]. Although AIS and remote sensing data can complement each other, further improvements may be possible through sub-regional scale studies, but it is difficult to find other data sources that can effectively complement AIS on a global scale.…”

Injuries and skin condition of Indo‐Pacific humpback dolphins (Sousa chinensis) in the northern South China Sea

“…The highest prevalence of human‐induced and unspecified injuries in JM, SWH, and SNB could be linked to the different types and intensities of human activities at each location. For example, fishing activities differ among locations (Guan et al, 2021) and experience both seasonal (e.g., Chen et al, 2022; Guan et al, 2021; Li et al, 2021) and annual variations (Lin et al, 2021). These variations, and the fact that it is impossible to date injuries, make it hard to link injury prevalence with a specific time and activity level at a specific location.…”