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Remote Sensing
Volume 13
Issue 10
10.3390/rs13101952
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Article

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

by
Yanan Guan
1,2,
Jie Zhang
2,3,4,
Xi Zhang
2,3,*,
Zhongwei Li
4,
Junmin Meng
2,3,
Genwang Liu
2,3,
Meng Bao
2,3 and
Chenghui Cao
2,3
1
School of Geosciences, China University of Petroleum, Qingdao 266580, China
2
First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
3
Technology Innovation Center for Ocean Telemetry, Ministry of Natural Resources, Qingdao 266061, China
4
College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(10), 1952; https://doi.org/10.3390/rs13101952
Submission received: 26 March 2021 / Revised: 2 May 2021 / Accepted: 11 May 2021 / Published: 17 May 2021
(This article belongs to the Special Issue Sustainable Development of Our Oceans and Coastal Zones through AI and Remote Sensing)
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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.
Keywords: Automatic Identification System (AIS) data; the northern South China Sea (SCS); vessel type identification; fishing density; seasonal activities analysis Automatic Identification System (AIS) data; the northern South China Sea (SCS); vessel type identification; fishing density; seasonal activities analysis

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MDPI and ACS Style

Guan, Y.; Zhang, J.; Zhang, X.; Li, Z.; Meng, J.; Liu, G.; Bao, M.; Cao, C. 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. Remote Sens. 2021, 13, 1952. https://doi.org/10.3390/rs13101952

AMA Style

Guan Y, Zhang J, Zhang X, Li Z, Meng J, Liu G, Bao M, Cao C. 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. Remote Sensing. 2021; 13(10):1952. https://doi.org/10.3390/rs13101952

Chicago/Turabian Style

Guan, Yanan, Jie Zhang, Xi Zhang, Zhongwei Li, Junmin Meng, Genwang Liu, Meng Bao, and Chenghui Cao. 2021. "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" Remote Sensing 13, no. 10: 1952. https://doi.org/10.3390/rs13101952

APA Style

Guan, Y., Zhang, J., Zhang, X., Li, Z., Meng, J., Liu, G., Bao, M., & Cao, C. (2021). 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. Remote Sensing, 13(10), 1952. https://doi.org/10.3390/rs13101952

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