Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics

Fishing Behavior Detection and Analysis of Squid Fishing Vessel Based on Multiscale Trajectory Characteristics

Zhang, Fan (Wuhan University of Technology)
Yuan, Baoxin (Wuhan University of Technology)
Huang, L. (TU Delft Safety and Security Science; Wuhan University of Technology)
Wen, Yuanqiao (Wuhan University of Technology)
Yang, Xue (National Engineering Laboratory of Application Technology of Integrated Transportation Big Data)
Song, R. (TU Delft Safety and Security Science)
van Gelder, P.H.A.J.M. (TU Delft Safety and Security Science)

2023

Accurate fishing activity detection from the trajectories of fishing vessels can not only achieve high-precision fishery management but also ensure the reasonable and sustainable development of marine fishery resources. This paper proposes a new method to detect fishing vessels’ fishing activities based on the defined local dynamic parameters and global statistical characteristics of vessel trajectories. On a local scale, the stop points and points of interest (POIs) in the vessel trajectory are extracted. Voyage extraction can then be conducted on this basis. After that, multiple characteristics based on motion and morphology on a global scale are defined to construct a logistic regression model for fishing behavior detection. To verify the effectiveness and feasibility of the method, vessel trajectory data, and fishing log data collected from Chinese ocean squid fishing vessels in Argentine waters in 2020 are integrated for fishing operation detection. Multiple evaluation metrics show that the proposed method can provide robust and accurate recognition results. Moreover, further analysis of the temporal and spatial distribution and seasonal changes in squid fishing activities in Argentine waters has been performed. A more refined assessment of the fishing activities of individual fishing vessels can also be provided quantitatively. All the results above can benefit the regulation of fishing activities.

fishing behavior
fishery management
statistical features of trajectory sequences
logistic regression
sliding window

http://resolver.tudelft.nl/uuid:a8f337ce-885d-4262-af8a-13d3520a3724

https://doi.org/10.3390/jmse11061245

2077-1312

Journal of Marine Science and Engineering, 11 (6)

Institutional Repository

journal article

© 2023 Fan Zhang, Baoxin Yuan, L. Huang, Yuanqiao Wen, Xue Yang, R. Song, P.H.A.J.M. van Gelder