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Using AIS data to mine the dynamic characteristics of fishery resource exploitation helps to carry out scientific management of fishery and realize the sustainable development of marine resources. We proposed a framework that integrates multiple AIS data processing and analysis modules, which can efficiently divide fishing voyages, determine the fishing activities and identify fishing types, and provide near real-time analysis results on the number of fishing vessels, fishing duration, voyages and so on. The framework was applied to 1.68 billion AIS trajectory data points of approximately 588,000 fishing vessels. We selected China’s sea areas overall and six fishing grounds as the research area, explored the characteristics of fishing vessel activities in winter and spring of 2019, and analyzed the impact of COVID-19 on winter-spring fishing in China in 2020. In 2019, our results showed that the number of fishing vessels in China’s sea areas gradually increased over time, with the Chinese New Year holiday affecting fishing activities at the corresponding time but having little impact on the entire month. We found that the changing laws of the fishing duration and voyages in the inshore fishing grounds were similar to those of the number of fishing vessels, which increased to varying degrees over time. Gillnetters were the most numerous fishing vessel type operating in the inshore fishing grounds with increased in spring, while seiners had an absolute advantage in the Xisha-Zhongsha fishing ground. In 2020, during the occurrence period of COVID-19, the fishing activities in China’s sea areas was almost unaffected. During the outbreak period, the number, distribution range, activity intensity, and fishing duration of fishing vessels all experienced a relatively large decline. After the epidemic was effectively controlled, they were rapidly increased. In addition, we found that compared with the Government Response Stringency Index, the number of fishing vessels and the number of new confirmed cases showed a more obvious negative correlation. By processing, mining and analyzing AIS data with high spatial-temporal granularity, this study can provide data support for the reasonable development of fishery resources, and help fishery practitioners make wise decisions when responding to unexpected emergencies (e.g. pandemics).

Study on the Activity Laws of Fishing Vessels in China’s Sea Areas in Winter and Spring and the Effects of the COVID-19 Pandemic Based on AIS Data