Knowledge Discovery of Human Activities at Sea in the Arctic Using Remote Sensing and Vessel Tracking Systems
Adequate knowledge of human activities in the Arctic is fundamental to support safe and secure maritime operations and sustainable development in the area. Such knowledge is often incomplete in terms of activities, geographic area and spatial resolution. For example, in the specific case of the transits over the Arctic shipping routes, such information can be accessed through domain expert knowledge, open source statistics or data from ship reporting systems. Offshore energy and exploration, fishing, and shipping activities can be monitored and/or mapped using surveillance tools such as satellite based remote sensing (e.g. Synthetic Aperture Radar—SAR) and vessel tracking systems (e.g. Automatic Identification Systems—AIS, and Long Range Identification and Tracking—LRIT), supplemented by knowledge discovery approaches. Such data-driven methodology, combined with meteorological and oceanographic information, enables a high level of situational awareness that is otherwise often difficult to access, hard to update or challenging to extract. In this chapter we analyse ways to understand and characterise activities and discover their trends in the Arctic. This new information will assist policy makers and operational authorities when conducting Maritime Spatial Planning and the evaluation of new routing systems and impact assessments of Marine Protected Areas.
KeywordsKnowledge discovery Maritime situational awareness Maritime surveillance Maritime transport
The Norwegian Defence Research Establishment (FFI) and the Norwegian Coastal Administration are thanked for providing access to AISSat-1 and AISSat-2 data that have proved very valuable for this study.
Further AIS data were obtained from MSSIS, courtesy of the Volpe Center of the U.S. Department of Transportation and the U.S. Navy.
Sentinel-1 data are © Copernicus 2014, 2015.
Ice maps were obtained from the U.S. National Ice Center.
The authors would like to thank the reviewers for their valuable contribution in improving this chapter.
- Alessandrini, A., Argentieri, P., Alvarez, M. A., Barbas, T., Delaney, C., Arguedas, V. F., Gammieri, V., Greidanus, H., Mazzarella, F., Vespe, M. and Ziemba, L. (2014). Data driven contextual knowledge from and for maritime situational awareness. In Context-Awareness in Geographic Information Services (CAGIS). Google Scholar
- AMSA Report. (2009). Arctic Marine Shipping Assessment – Arctic Council. Protection of the Arctic Marine Environment – PAME. Retrieved January 17, 2017, from http://www.pame.is/images/03_Projects/AMSA/AMSA_2009_report/AMSA_2009_Report_2nd_print.pdf
- Eriksen, T., & Olsen, Ø. (2015). Vessel tracking using automatic identification system data in the Arctic. In Proceedings of the ShipArc2015 conference “Safe and Sustainable Shipping in a Changing Arctic Environment”. Google Scholar
- Mazzarella, F., Vespe, M., Damalas, D., Osio, G. (2014). Discovering vessel activities at sea using AIS data: Mapping of fishing footprints. In 17th International Conference on Information Fusion (FUSION). Google Scholar
- Sentinel-1 User Handbook. (2013). GMES - S1OP – EOPG – TN – 13 – 0001, 66–67. Retrieved January 17, 2017, from https://sentinel.esa.int/documents/247904/685163/Sentinel-1_User_Handbook