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Shallow-Water Habitat Mapping using Underwater Hyperspectral Imaging from an Unmanned Surface Vehicle: A Pilot Study

1
Centre for Autonomous Marine Operations and Systems, Department of Biology, Norwegian University of Science and Technology (NTNU), Trondhjem Biological Station, NO-7491 Trondheim, Norway
2
Arctic Biology Department, University Centre in Svalbard (UNIS), P.O. Box 156, NO-9171 Longyearbyen, Norway
3
Centre for Autonomous Marine Operations and Systems, Department of Marine Technology, Norwegian University of Science and Technology (NTNU), Otto Nielsens vei 10, NO-7491 Trondheim, Norway
4
Arctic Technology Department, University Centre in Svalbard (UNIS), P.O. Box 156, NO-9171 Longyearbyen, Norway
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 685; https://doi.org/10.3390/rs11060685
Received: 15 February 2019 / Revised: 15 March 2019 / Accepted: 20 March 2019 / Published: 21 March 2019
(This article belongs to the Section Ocean Remote Sensing)
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Abstract

The impacts of human activity on coastal ecosystems are becoming increasingly evident across the world. Consequently, there is a growing need to map, monitor, and manage these regions in a sustainable manner. In this pilot study, we present what we believe to be a novel mapping technique for shallow-water seafloor habitats: Underwater hyperspectral imaging (UHI) from an unmanned surface vehicle (USV). A USV-based UHI survey was carried out in a sheltered bay close to Trondheim, Norway. In the survey, an area of 176 m2 was covered, and the depth of the surveyed area was approximately 1.5 m. UHI data were initially recorded at a 1-nm spectral resolution within the range of 380–800 nm, but this was reduced to 86 spectral bands between 400-700 nm (3.5-nm spectral resolution) during post-processing. The hyperspectral image acquisition was synchronized with navigation data from the USV, which permitted georeferencing and mosaicking of the imagery at a 0.5-cm spatial resolution. Six spectral classes, including coralline algae, the wrack Fucus serratus, green algal films, and invertebrates, were identified in the georeferenced imagery, and chosen as targets for support vector machine (SVM) classification. Based on confusion matrix analyses, the overall classification accuracy was estimated to be 89%–91%, which suggests that USV-based UHI may serve as a useful tool for high-resolution mapping of shallow-water habitats in the future. View Full-Text
Keywords: underwater hyperspectral imaging (UHI); unmanned surface vehicle (USV); shallow-water habitat mapping; supervised classification; support vector machine (SVM) underwater hyperspectral imaging (UHI); unmanned surface vehicle (USV); shallow-water habitat mapping; supervised classification; support vector machine (SVM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mogstad, A.A.; Johnsen, G.; Ludvigsen, M. Shallow-Water Habitat Mapping using Underwater Hyperspectral Imaging from an Unmanned Surface Vehicle: A Pilot Study. Remote Sens. 2019, 11, 685.

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