Next Article in Journal
A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset
Previous Article in Journal
Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends
Article Menu

Article Menu


Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project

Akvaplan-niva AS, 9007 Tromsø, Norway
Institute of Marine Research, 9007 Tromsø, Norway
The Norwegian College of Fishery Science, Faculty of Fisheries and Bioeconomics, UiT—The Arctic University of Norway, 9037 Tromsø, Norway
Faculty for Bioscience and Aquaculture, Nord University, 8026 Bodø, Norway
Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
R&D Department, Norwegian Meteorological Institute, 0371 Oslo, Norway
NORCE Norwegian Research Center, 5008 Bergen, Norway
Kongsberg Maritime Germany GmbH, 22529 Hamburg, Germany
Kongsberg Digital, 3616 Kongsberg, Norway
Aanderaa Xylem, 5225 Nesttun, Norway
Cyprus Sub Sea Consulting & Services, 2326 Nicosia, Cyprus
ConocoPhillips Skandinavia AS, 4056 Tananger, Norway
Marin Biogeochemistry and Oceanography, NIVA, 0579 Oslo, Norway
Offshore Sensing AS, 5072 Bergen, Norway
Maritime Robotics AS, 7010 Trondheim, Norway
Author to whom correspondence should be addressed.
This paper is an extension version of the conference paper: Camus, L.; Pedersen, G.; Falk-Petersen, S.; Dunlop, K.; Daase, M.; Basedow, S.L.; Bandara, K.; Tverberg, V.; Pederick, J.; Peddie, D.; et al. Autonomous surface and underwater vehicles reveal new discoveries in the Arctic Ocean. In Proceedings of the OCEANS 2019—Marseille, Marseille, France, 17–20 June 2019.
Sensors 2021, 21(20), 6752;
Received: 11 August 2021 / Revised: 4 October 2021 / Accepted: 8 October 2021 / Published: 12 October 2021
(This article belongs to the Section Sensors and Robotics)
Effective ocean management requires integrated and sustainable ocean observing systems enabling us to map and understand ecosystem properties and the effects of human activities. Autonomous subsurface and surface vehicles, here collectively referred to as “gliders”, are part of such ocean observing systems providing high spatiotemporal resolution. In this paper, we present some of the results achieved through the project “Unmanned ocean vehicles, a flexible and cost-efficient offshore monitoring and data management approach—GLIDER”. In this project, three autonomous surface and underwater vehicles were deployed along the Lofoten–Vesterålen (LoVe) shelf-slope-oceanic system, in Arctic Norway. The aim of this effort was to test whether gliders equipped with novel sensors could effectively perform ecosystem surveys by recording physical, biogeochemical, and biological data simultaneously. From March to September 2018, a period of high biological activity in the area, the gliders were able to record a set of environmental parameters, including temperature, salinity, and oxygen, map the spatiotemporal distribution of zooplankton, and record cetacean vocalizations and anthropogenic noise. A subset of these parameters was effectively employed in near-real-time data assimilative ocean circulation models, improving their local predictive skills. The results presented here demonstrate that autonomous gliders can be effective long-term, remote, noninvasive ecosystem monitoring and research platforms capable of operating in high-latitude marine ecosystems. Accordingly, these platforms can record high-quality baseline environmental data in areas where extractive activities are planned and provide much-needed information for operational and management purposes. View Full-Text
Keywords: glider; remote sensing; ecosystem monitoring; Lofoten–Vesterålen glider; remote sensing; ecosystem monitoring; Lofoten–Vesterålen
Show Figures

Figure 1

MDPI and ACS Style

Camus, L.; Andrade, H.; Aniceto, A.S.; Aune, M.; Bandara, K.; Basedow, S.L.; Christensen, K.H.; Cook, J.; Daase, M.; Dunlop, K.; Falk-Petersen, S.; Fietzek, P.; Fonnes, G.; Ghaffari, P.; Gramvik, G.; Graves, I.; Hayes, D.; Langeland, T.; Lura, H.; Marin, T.K.; Nøst, O.A.; Peddie, D.; Pederick, J.; Pedersen, G.; Sperrevik, A.K.; Sørensen, K.; Tassara, L.; Tjøstheim, S.; Tverberg, V.; Dahle, S. Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project. Sensors 2021, 21, 6752.

AMA Style

Camus L, Andrade H, Aniceto AS, Aune M, Bandara K, Basedow SL, Christensen KH, Cook J, Daase M, Dunlop K, Falk-Petersen S, Fietzek P, Fonnes G, Ghaffari P, Gramvik G, Graves I, Hayes D, Langeland T, Lura H, Marin TK, Nøst OA, Peddie D, Pederick J, Pedersen G, Sperrevik AK, Sørensen K, Tassara L, Tjøstheim S, Tverberg V, Dahle S. Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project. Sensors. 2021; 21(20):6752.

Chicago/Turabian Style

Camus, Lionel, Hector Andrade, Ana S. Aniceto, Magnus Aune, Kanchana Bandara, Sünnje L. Basedow, Kai H. Christensen, Jeremy Cook, Malin Daase, Katherine Dunlop, Stig Falk-Petersen, Peer Fietzek, Gro Fonnes, Peygham Ghaffari, Geir Gramvik, Inger Graves, Daniel Hayes, Tor Langeland, Harald Lura, Trond K. Marin, Ole A. Nøst, David Peddie, Joel Pederick, Geir Pedersen, Ann K. Sperrevik, Kai Sørensen, Luca Tassara, Sigurd Tjøstheim, Vigdis Tverberg, and Salve Dahle. 2021. "Autonomous Surface and Underwater Vehicles as Effective Ecosystem Monitoring and Research Platforms in the Arctic—The Glider Project" Sensors 21, no. 20: 6752.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop