Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea

  • 19 Accesses

Abstract

Overfishing is a critical catastrophe to the ecosystem and the global food chain. The leading causes are Illegal Unreported and Unregulated Fishing (IUU Fishing) linked to illegal labor. EU and the US have set up the fisheries policy that emphasis on traceability. The traceability principle is to monitor the entire seafood supply chain (Sea to Table). FAO’s technology gap analysis reveals that there is a lack of reliable and affordable automated systems or a lack of links to traceability. The challenge of traceability is tracing back to the catch source with existing data and technology. This study aims at the novel concept of a combination of global and local features of trajectory data for fishing vessel behavior identification and enabling seafood transparency. We present a new technique on a local feature of time series and transform the trajectory pattern to global features for Deep Learning. We apply this technique to AIS and VMS data of Thai fishing vessels (Surrounding Nets, Trawl, Longliner, and Reefer). Fishing vessel behaviors were classified as Fishing, Non-fishing, and Transshipment. Our proposed method gives a robust average accuracy result (97.50%). This concept could solve the IUU Fishing and enable traceability at sea, including monitoring, maritime, and marine resources conservation systems.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

References

  1. 1.

    Dunn, D. C., Jablonicky, C., Crespo, G. O., McCauley, D. J., Kroodsma, D. A., Boerder, K., et al. (2018). Empowering high seas governance with satellite vessel tracking data. Fish and Fisheries, 19(4), 729–739.

  2. 2.

    Agnew, D. J., Pearce, J., Pramod, G., Peatman, T., Watson, R., Beddington, J. R., et al. (2009). Estimating the worldwide extent of illegal fishing. PLoS ONE, 4(2), e4570.

  3. 3.

    How many fisheries are overfished? Sustainable fisheries UW. https://sustainablefisheries-uw.org/fact-check/how-many-fisheries-are-overfished/.

  4. 4.

    Davies, R., Cripps, S., Nickson, A., & Porter, G. (2009). Defining and estimating global marine fisheries bycatch. Marine Policy, 33(4), 661–672.

  5. 5.

    Introini, C., Mme, A., & Cruz Introini, S. (2018). Traceability in the food supply chain: Review of the literature from a technological perspective. Dirección y Organización, 64, 50–55.

  6. 6.

    Fishsec. (2018). EU fisheries control system factsheet traceability requirements for seafood products importance. NGO. Technical report 2018. https://www.fishsec.org/app/uploads/2019/03/Traceability-factsheet.pdf.

  7. 7.

    Melania, B., & Petter, O. (2016). Seafood traceability systems: Gap analysis of inconsistencies in standards and norms. FAO Fisheries and Aquaculture Circular, C1123, 1.

  8. 8.

    Hosch, G., & Blaha, F. (2017). Seafood traceability for fisheries compliance: Country-level support for catch documentation schemes. Food and Agriculture Organization. http://www.fao.org/3/a-i8183e.pdf.

  9. 9.

    Nicolae, C. G., Isfan, N., Bahaciu, G. V., Marin, M. P., & Moga, L. M. (2016). Case study in traceability and consumer’s choices on fish and fishery products. Agrolife Scientific Journal, 5(2), 103–107.

  10. 10.

    Nilsson, J. A., Fulton, E. A., Johnson, C. R., Haward, M., Nilsson, J. A., Fulton, E. A., et al. (2019). How to sustain fisheries: Expert knowledge from 34 nations. Water, 11(2), 213.

  11. 11.

    Nations, U. (2005). Destructive fishing practices, pp. 78–81. http://www.fao.org/fishery/topic/12353/en.

  12. 12.

    Overfishing & Destructive Fishing—Greenpeace USA. https://www.greenpeace.org/usa/oceans/issues/overfishing-destructive-fishing/.

  13. 13.

    Pramod, G., Nakamura, K., Pitcher, T. J., & Delagran, L. (2014). Estimates of illegal and unreported fish in seafood imports to the USA. Marine Policy, 48, 102–113.

  14. 14.

    Leroy, A., Galletti, F., & Chaboud, C. (2016). The EU restrictive trade measures against IUU fishing. Marine Policy, 64, 82–90.

  15. 15.

    Miller, N. A., Roan, A., Hochberg, T., Amos, J., & Kroodsma, D. A. (2018). Identifying global patterns of transshipment behavior. Frontiers in Marine Science, 5, 240.

  16. 16.

    Shaver, A., & Yozell, S. (2015). Casting a wider net. Chemical & Engineering News Archive, 93(41), 32–33. https://doi.org/10.1021/cen-09341-scitech1.

  17. 17.

    Willette, D. A., & Cheng, S. H. (2017). Delivering on seafood traceability under the new US import monitoring program. Ambio, 47(1), 25–30.

  18. 18.

    Thompson, M., Sylvia, G., & Morrissey, M. T. (2005). Seafood traceability in the United States: Current trends, system design, and potential applications. Compr Rev Food Sci Food Saf, 4(1), 1–7.

  19. 19.

    Petersen, A., & Green, D. Seafood traceability: A practical guide for the U.S. industry.

  20. 20.

    UNECE. (2017). Traceability for sustainable trade a framework to design traceability systems for cross border trade. UNECE, Technical report 2017. https://www.unece.org/fileadmin/DAM/trade/Publications/ECE_TRADE_429E_ TraceabilityForSustainableTrade.pdf.

  21. 21.

    WWF. (2015) Tractability principles for wild-caught fish products. WWF, Technical report. http://assets.worldwildlife.org/publications/796/files/ original/WWF_Traceability_Principles_for_Wild-Caugh_Fish_April_2015.pdf? 1430410438&_ga=1.161806972.1776882823.1455309792.

  22. 22.

    USAID Oceans. (2017). USAID oceans CDT101 conceptual overview. USAIDO, Technical report 2017. https://www.seafdec-oceanspartnership.org/wp-content/uploads/USAID Oceans_CDT101_Conceptual Overview_March 2017.pdf.

  23. 23.

    Lewis, S. G., & Boyle, M. (2017). The expanding role of traceability in seafood: Tools and key initiatives. Journal of Food Science, 82, A13–A21.

  24. 24.

    Natale, F., Gibin, M., Alessandrini, A., Vespe, M., & Paulrud, A. (2015). Mapping fishing effort through AIS data. PLoS ONE,. https://doi.org/10.1371/journal.pone.0130746.

  25. 25.

    Shepperson, J. L., Hintzen, N. T., Szostek, C. L., Bell, E., Murray, L. G., & Kaiser, M. J. (2018). A comparison of VMS and AIS data: The effect of data coverage and vessel position recording frequency on estimates of fishing footprints. ICES Journal of Marine Science, 75(3), 988–998.

  26. 26.

    Yin, P., Ye, M., Lee, W. C., & Li, Z. (2014). Mining GPS data for trajectory recommendation. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), Vol. 8444, LNAI, no. PART 2, pp. 50–61.

  27. 27.

    Mao, S., Tu, E., Zhang, G., Rachmawati, L., Rajabally, E., & Huang, G.-B. (2017). An automatic identification system (AIS) database for maritime trajectory prediction and data mining. In Proceedings in adaptation, learning and optimization, pp. 241–257.

  28. 28.

    Watson, J. T., & Haynie, A. C. (2016). Using vessel monitoring system data to identify and characterize trips made by fishing vessels in the United States North Pacific. PLoS ONE, 11(10), e0165173.

  29. 29.

    Stop Illegal Fishing. (2018). The potential use of ’automatic information systems—AIS as a fisheries monitoring tool. Fish-i Africa, pp. 1–21.

  30. 30.

    de Souza, E. N., Boerder, K., Matwin, S., & Worm, B. (2016). Improving fishing pattern detection from satellite AIS using data mining and machine learning. PLoS ONE, 11(7), e0158248.

  31. 31.

    Russo, T., Dandrea, L., Parisi, A., Martinelli, M., Belardinelli, A., Boccoli, F., et al. (2016). Assessing the fishing footprint using data integrated from different tracking devices: Issues and opportunities. Ecological Indicators, 69, 818–827.

  32. 32.

    Longépé, N., Hajduch, G., Ardianto, R., Joux, R., Nhunfat, B., Marzuki, M. I., et al. (2017). Completing fishing monitoring with spaceborne Vessel Detection System (VDS) and Automatic Identification System (AIS) to assess illegal fishing in Indonesia. Marine Pollution Bulletin, 131, 33–39.

  33. 33.

    Soleymani, A. (2016). Cross-scale analysis in classification and segmentation of movement. Ph.D. dissertation, University of Zurich.

  34. 34.

    Dodge, S., Weibel, R., & Forootan, E. (2009). Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33(6), 419–434.

  35. 35.

    Teimouri, M., Indahl, U., Sickel, H., & Tveite, H. (2018). Deriving animal movement behaviors using movement parameters extracted from location data. ISPRS International Journal of Geo-Information, 7(2), 78.

  36. 36.

    Benhamou, S. (2004). How to reliably estimate the tortuosity of an animal’s path: Straightness, sinuosity, or fractal dimension? Journal of Theoretical Biology, 229(2), 209–220.

  37. 37.

    Postlethwaite, C. M., Brown, P., & Dennis, T. E. (2013). A new multi-scale measure for analysing animal movement data. Journal of Theoretical Biology, 317, 175–185.

  38. 38.

    Dodge, S., Weibel, R., & Lautenschütz, A.-K. (2008). Towards a taxonomy of movement patterns. Information Visualization, 7, 240–252.

  39. 39.

    Yoon, H., & Shahabi, C. (2008). Robust time-referenced segmentation of moving object trajectories. In Proceedings—IEEE international conference on data mining, ICDM, pp. 1121–1126.

  40. 40.

    Wan, Y., Zhou, C., & Pei, T. (2017). Semantic-geographic trajectory pattern mining based on a new similarity measurement. ISPRS International Journal of Geo-Information, 6(7), 212.

  41. 41.

    Ying, J. J.-C., Lee, W.-C., & Tseng, V. S. (2013). Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM Transactions on Intelligent Systems and Technology, 5(1), 1–33.

  42. 42.

    Hatami, N., Gavet, Y., & Debayle, J. (2018). Classification of time-series images using deep convolutional neural networks. In A. Verikas, P. Radeva, D. Nikolaev, J. Zhou (Eds.), Tenth international conference on machine vision (ICMV 2017), vol. 10696, International Society for Optics and Photonics. SPIE, pp. 242–249. https://doi.org/10.1117/12.2309486.

  43. 43.

    Karim, F., Majumdar, S., & Darabi, H. (2019). Insights into LSTM fully convolutional networks for time series classification. IEEE Access, 7, 67-718–67-725.

  44. 44.

    Hua, Y., Zhao, Z., Li, R., Chen, X., Liu, Z., & Zhang, H. (2019). Deep learning with long short-term memory for time series prediction. IEEE Communications Magazine, 57(6), 114–119.

  45. 45.

    McMaster, R. B. (1986). A statistical analysis of mathematical measures for linear simplification. The American Cartographer, 13(2), 103–116.

  46. 46.

    Rucklidge, W. J. (1997). Efficiently locating objects using the hausdorff distance. International Journal of Computer Vision, 24(3), 251–270. https://doi.org/10.1023/A:1007975324482.

  47. 47.

    Buchin, M., & Purves, R. S. (2013). Computing similarity of coarse and irregular trajectories using space-time prisms. In Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems—SIGSPATIAL’13, ACM Press, New York, pp. 456–459.

  48. 48.

    Chen, L., & Ng, R. (2004). On the marriage of LP-norms and edit distance. In Proceedings of the thirtieth international conference on very large data bases, Vol. 30, ser. VLDB ’04. VLDB Endowment, pp. 792–803. http://dl.acm.org/citation.cfm?id=1316689.1316758.

  49. 49.

    Vlachos, M., Kollios, G., & Gunopulos, D. (2002). Discovering similar multidimensional trajectories. In Proceedings 18th international conference on data engineering, pp. 673–684.

  50. 50.

    Kim, S.-W., Park, S., & Chu, W. W. (2004). Efficient processing of similarity search under time warping in sequence databases: An index-based approach. Information Systems, 29(5), 405–420. http://www.sciencedirect.com/science/article/pii/S0306437903000371.

  51. 51.

    Mikolov, T., Karafiát, M., Burget, L., & Khudanpur, S. (2010). Recurrent neural network based language model. Proceedings of INTERSPEECH, pp. 1045–1048.

  52. 52.

    Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.

  53. 53.

    Switonski, A., Josinski, H., & Wojciechowski, K. (2018). Dynamic time warping in classification and selection of motion capture data. Multidimensional Systems and Signal Processing, 2018, 1–32.

  54. 54.

    Yuan, J., Douzal-Chouakria, A., Varasteh Yazdi, S., & Wang, Z. (2019). A large margin time series nearest neighbour classification under locally weighted time warps. Knowledge and Information Systems, 59(1), 117–135. https://doi.org/10.1007/s10115-018-1184-z.

  55. 55.

    Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.

  56. 56.

    Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43–49.

  57. 57.

    Keogh, E. J., & Pazzani, M.J. (2001). Derivative dynamic time warping, SIAM Publications. https://doi.org/10.1137/1.9781611972719.1.

  58. 58.

    Abraham, Z., & Tan, P.-N. (2010). An integrated framework for simultaneous classification and regression of time-series data. In Proceedings of the 2010 SIAM international conference on data mining, Society for Industrial and Applied Mathematics, Philadelphia, pp. 653–664.

  59. 59.

    Díaz, S. P., & Vilar, J. A. (2010). Comparing several parametric and nonparametric approaches to time series clustering: A simulation study. Journal of Classification, 27(3), 333–362.

  60. 60.

    Montero, P., & Vilar, J. A. (2014). TSclust: An R package for time series clustering. Journal of Statistical Software, 62(1), 1–43.

  61. 61.

    Vaughan, N., & Gabrys, B. (2016). Comparing and combining time series trajectories using dynamic time warping. Procedia Computer Science, 96, 465–474.

  62. 62.

    Huang, K., Tan, T., & Zhang, Z. (2006). Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In 2006 18th International conference on pattern recognition, Vol. 3. IEEE Computer Society, Los Alamitos, CA, USA,pp. 1135–1138. https://doi.org/10.1109/ICPR.2006.392.

  63. 63.

    Li, Y., Hu, H., Wen, Y., & Zhang, J. (2016). Power series classification: A hybrid of LSTM and a novel advancing dynamic time warping. CoRR. arXiv.org/abs/1608.04171.

  64. 64.

    Łuczak, M. (2016). Combinseasean catch documentation scheme for marine capture fisheries. Journal of Intelligent & Fuzzy Systems, 1, 373–380.

  65. 65.

    Yuan, J., Zheng, Y., & Xie, X. (2012). Discovering regions of different functions in a city using human mobility and pois. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’12, ACM, New York, NY, USA, pp. 186–194. https://doi.org/10.1145/2339530.2339561

  66. 66.

    Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27–34.

  67. 67.

    Fayyad, U., Piatetsky-shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3), 37–54.

  68. 68.

    Gullo, F. (2015). From patterns in data to knowledge discovery: What data mining can do. Physics Procedia, 62, 18–22.

  69. 69.

    FAO Fisheries and Aquaculture Department. (2001). Fishing gear types: Bottom trawls. http://www.fao.org/fishery/geartype/205/en.

  70. 70.

    Bertrand, S., Burgos, J., Gerlotto, F., & Atiquipa, J. (2005). Levy trajectories of Peruvian purse-seiners as an indicator of the spatial distribution of anchovy. ICES Journal of Marine Science, 62(3), 477–482.

  71. 71.

    Lee, J., South, A. B., & Jennings, S. (2010). Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data. ICES Journal of Marine Science, 67(6), 1260–1271.

  72. 72.

    Gong, L., Hitomi Sato, B., Toshiyuki Yamamoto, B., Tomio Miwa, B., & Takayuki Morikawa, B. (2015). Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines. Journal of Modern Transportation, 23(3), 202–213.

  73. 73.

    Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Technical report, University of Munich.

  74. 74.

    Fabio Mazzarella, G. O., Vespe, M., Damalas, D. (2014). Discovering vessel activities at sea using AIS data: Mapping of fishing footprints. In 17th International conference on information fusion (FUSION).

  75. 75.

    Dodge, S., Laube, P., & Weibel, R. (2012). Movement similarity assessment using symbolic representation of trajectories. International Journal of Geographical Information Science, 26(9), 1563–1588.

  76. 76.

    Shamoun-Baranes, J., Bouten, W., Camphuysen, C. J., & Baaij, E. (2011). Riding the tide: Intriguing observations of gulls resting at sea during breeding. IBIS, 153(2), 411–415.

  77. 77.

    Cheung, A., Zhang, S., Stricker, C., & Srinivasan, M. V. (2007). Animal navigation: The difficulty of moving in a straight line. Biological Cybernetics, 97(1), 47–61.

  78. 78.

    Laube, P., & Purves, R. S. (2011). How fast is a cow? Cross-scale analysis of movement data. Transactions in GIS, 15(3), 401–418.

  79. 79.

    Marzuki, M. I., Gaspar, P., Garello, R., Kerbaol, V., & Fablet, R. (2017). Fishing gear identification from vessel-monitoring-system-based fishing vessel trajectories. IEEE Journal of Oceanic Engineering, 43(3), 3–7.

  80. 80.

    Li, X. (2014). Using complexity measures of movement for automatically detecting movement types of unknown GPS trajectories. American Journal of Geographic Information System, 3(2), 63–74.

  81. 81.

    Kitamura, T., & Imafuku, M. (2015). Behavioural mimicry in flight path of Batesian intraspecific polymorphic butterfly Papilio polytes. Proceedings of Biological Sciences, 282(1809), 20150483.

  82. 82.

    Shamble, P. S., Hoy, R. R., Cohen, I., & Beatus, T. (2017). Walking like an ant: A quantitative and experimental approach to understanding locomotor mimicry in the jumping spider Myrmarachne formicaria. Proceedings of the Royal Society B: Biological Sciences, 284(1858), 20170308.

  83. 83.

    McLean, D. J., & Skowron Volponi, M. A. (2018). trajr: An R package for characterisation of animal trajectories. Ethology, 124(6), 440–448.

  84. 84.

    Zhang, Z., Tavenard, R., Bailly, A., Tang, X., Tang, P., & Corpetti, T. (2017). Dynamic time warping under limited warping path length. Information Sciences, 393, 91–107.

Download references

Author information

Correspondence to Supaporn Kiattisin.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chuaysi, B., Kiattisin, S. Fishing Vessels Behavior Identification for Combating IUU Fishing: Enable Traceability at Sea. Wireless Pers Commun (2020). https://doi.org/10.1007/s11277-020-07200-w

Download citation

Keywords

  • Traceability at sea
  • Fishing vessels behavior
  • Trajectory and time series analysis
  • Global and local features
  • KNN for MLP