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A new method for ecological surveying of the abyss using autonomous underwater vehicle photography

A new method for ecological surveying of the abyss using autonomous underwater vehicle photography
A new method for ecological surveying of the abyss using autonomous underwater vehicle photography


The extent and speed of marine environmental mapping is increasing quickly with technological advances, particularly with optical imaging from autonomous underwater vehicles (AUVs). This contribution describes a new deep-sea digital still camera system that takes high-frequency (>1 Hz) color photographs of the seafloor, suitable for detailed biological and habitat assessment, and the means of efficient processing of this mass imagery, to allow assessment across a wide range of spatial scales from that of individual megabenthic organisms to landscape scales (>100 km2). As part of the Autonomous Ecological Surveying of the Abyss (AESA) project, the AUV Autosub6000 obtained > 150,000 seafloor images (~160 km total transect length) to investigate the distribution of megafauna on the Porcupine Abyssal Plain (4850 m; NE Atlantic). An automated workflow for image processing was developed that corrected nonuniform illumination and color, geo-referenced the photographs, and produced 10-image mosaics ('tiles,' each representing a continuous strip of 15-20 m2 of seafloor), with overlap between consecutive images removed. These tiles were then manually annotated to generate biological data. This method was highly advantageous compared with alternative techniques, greatly increasing the rate of image acquisition and providing a 10-50 fold increase in accuracy in comparison to trawling. The method also offers more precise density and biodiversity estimates [Coefficient of variation (CV) < 10%] than alternative techniques, with a 2-fold improvement in density estimate precision compared with the WASP towed camera system. Ultimately, this novel system is expected to make valuable contributions to understanding human impact in the deep ocean.
1541-5856
795-809
Morris, Kirsty J.
4640fbf5-0c92-476c-a35f-281ccf41d6b0
Bett, Brian J.
61342990-13be-45ae-9f5c-9540114335d9
Durden, Jennifer M.
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Huvenne, Veerle A.I.
f22be3e2-708c-491b-b985-a438470fa053
Milligan, Rosanna
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Jones, Daniel O.B.
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McPhail, Stephen D.
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Robert, Katleen
49e4bfa2-0999-41ec-b50d-65c0f8896583
Bailey, David M.
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Ruhl, Henry A.
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Morris, Kirsty J.
4640fbf5-0c92-476c-a35f-281ccf41d6b0
Bett, Brian J.
61342990-13be-45ae-9f5c-9540114335d9
Durden, Jennifer M.
a65f5d1f-2009-476a-a8c6-3c32683d9eb9
Huvenne, Veerle A.I.
f22be3e2-708c-491b-b985-a438470fa053
Milligan, Rosanna
e95e696a-5d20-4b4c-9285-2927d93f2524
Jones, Daniel O.B.
44fc07b3-5fb7-4bf5-9cec-78c78022613a
McPhail, Stephen D.
58ac4bcd-26a6-4845-8e81-6d6a8f18aed7
Robert, Katleen
49e4bfa2-0999-41ec-b50d-65c0f8896583
Bailey, David M.
6888df28-2844-45f7-97c6-c4fa3cd745ab
Ruhl, Henry A.
177608ef-7793-4911-86cf-cd9960ff22b6

Morris, Kirsty J., Bett, Brian J., Durden, Jennifer M., Huvenne, Veerle A.I., Milligan, Rosanna, Jones, Daniel O.B., McPhail, Stephen D., Robert, Katleen, Bailey, David M. and Ruhl, Henry A. (2014) A new method for ecological surveying of the abyss using autonomous underwater vehicle photography. Limnology and Oceanography: Methods, 12 (11), 795-809. (doi:10.4319/lom.2014.12.795).

Record type: Article

Abstract



The extent and speed of marine environmental mapping is increasing quickly with technological advances, particularly with optical imaging from autonomous underwater vehicles (AUVs). This contribution describes a new deep-sea digital still camera system that takes high-frequency (>1 Hz) color photographs of the seafloor, suitable for detailed biological and habitat assessment, and the means of efficient processing of this mass imagery, to allow assessment across a wide range of spatial scales from that of individual megabenthic organisms to landscape scales (>100 km2). As part of the Autonomous Ecological Surveying of the Abyss (AESA) project, the AUV Autosub6000 obtained > 150,000 seafloor images (~160 km total transect length) to investigate the distribution of megafauna on the Porcupine Abyssal Plain (4850 m; NE Atlantic). An automated workflow for image processing was developed that corrected nonuniform illumination and color, geo-referenced the photographs, and produced 10-image mosaics ('tiles,' each representing a continuous strip of 15-20 m2 of seafloor), with overlap between consecutive images removed. These tiles were then manually annotated to generate biological data. This method was highly advantageous compared with alternative techniques, greatly increasing the rate of image acquisition and providing a 10-50 fold increase in accuracy in comparison to trawling. The method also offers more precise density and biodiversity estimates [Coefficient of variation (CV) < 10%] than alternative techniques, with a 2-fold improvement in density estimate precision compared with the WASP towed camera system. Ultimately, this novel system is expected to make valuable contributions to understanding human impact in the deep ocean.

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Published date: November 2014
Organisations: Geology & Geophysics, Ocean and Earth Science, Marine Biogeochemistry, Marine Geoscience, Ocean Technology and Engineering

Identifiers

Local EPrints ID: 374163
URI: http://eprints.soton.ac.uk/id/eprint/374163
ISSN: 1541-5856
PURE UUID: cb647993-3870-45c7-8b63-8b8af2ac2057
ORCID for Veerle A.I. Huvenne: ORCID iD orcid.org/0000-0001-7135-6360

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Date deposited: 06 Feb 2015 11:35
Last modified: 17 Dec 2019 01:48

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Contributors

Author: Kirsty J. Morris
Author: Brian J. Bett
Author: Jennifer M. Durden
Author: Veerle A.I. Huvenne ORCID iD
Author: Rosanna Milligan
Author: Daniel O.B. Jones
Author: Stephen D. McPhail
Author: Katleen Robert
Author: David M. Bailey
Author: Henry A. Ruhl

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