Quantifying Area Back Scatter of Marine Organisms in the Arctic Ocean by Machine Learning-Based Post-Processing of Volume Back Scatter
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| Udgivet i: | Sensors vol. 25, no. 10 (2025), p. 3121 |
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| Andre forfattere: | , , , , |
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MDPI AG
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| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Misund Ole Arve |u Norwegian Polar Institute, Fram Centre, 9296 Tromsø, Norway; anna.nikolopoulos@npolar.no (A.N.); vegard.stuerzinger@gmail.com (V.S.); haakon.hop@npolar.no (H.H.); paul.dodd@npolar.no (P.D.) | |
| 245 | 1 | |a Quantifying Area Back Scatter of Marine Organisms in the Arctic Ocean by Machine Learning-Based Post-Processing of Volume Back Scatter | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a As the sea ice reduces in both extent and thickness and the Arctic Ocean opens, there is substantial interest in mapping the marine ecosystem in this remote and until now largely inaccessible ocean. We used the R/V Kronprins Haakon during surveys in the Central Arctic Ocean (CAO) in 2022 and 2023 to record the marine ecosystem using modern fisheries acoustics and net sampling. The 2022 survey reached all the way to the North Pole. In a first, principally manually based post-processing of these acoustic recordings using the Large-Scale Survey Post-processing System (LSSS), much effort was used to remove segments of noise due to icebreaking operations. In a second, more sophisticated post-processing, the KORONA module of LSSS with elements of machine learning was applied for further noise reduction and to allocate the area back-scattering recordings to taxonomic groups as order, families and even species of fish and plankton organisms. These results highlight the need for further advances in post-processing systems to enable the direct allocation of back-scattered acoustic energy to taxonomic categories, including species-level classifications. | |
| 651 | 4 | |a Arctic Ocean | |
| 651 | 4 | |a North Pole | |
| 653 | |a Research ships | ||
| 653 | |a Plankton | ||
| 653 | |a Machine learning | ||
| 653 | |a Taxonomy | ||
| 653 | |a Data collection | ||
| 653 | |a Protocol | ||
| 653 | |a Acoustics | ||
| 653 | |a Fish | ||
| 653 | |a Fisheries | ||
| 700 | 1 | |a Nikolopoulos, Anna |u Norwegian Polar Institute, Fram Centre, 9296 Tromsø, Norway; anna.nikolopoulos@npolar.no (A.N.); vegard.stuerzinger@gmail.com (V.S.); haakon.hop@npolar.no (H.H.); paul.dodd@npolar.no (P.D.) | |
| 700 | 1 | |a Stürzinger Vegard |u Norwegian Polar Institute, Fram Centre, 9296 Tromsø, Norway; anna.nikolopoulos@npolar.no (A.N.); vegard.stuerzinger@gmail.com (V.S.); haakon.hop@npolar.no (H.H.); paul.dodd@npolar.no (P.D.) | |
| 700 | 1 | |a Hop Haakon |u Norwegian Polar Institute, Fram Centre, 9296 Tromsø, Norway; anna.nikolopoulos@npolar.no (A.N.); vegard.stuerzinger@gmail.com (V.S.); haakon.hop@npolar.no (H.H.); paul.dodd@npolar.no (P.D.) | |
| 700 | 1 | |a Dodd, Paul |u Norwegian Polar Institute, Fram Centre, 9296 Tromsø, Norway; anna.nikolopoulos@npolar.no (A.N.); vegard.stuerzinger@gmail.com (V.S.); haakon.hop@npolar.no (H.H.); paul.dodd@npolar.no (P.D.) | |
| 700 | 1 | |a Korneliussen, Rolf J |u Institute of Marine Research, 5005 Bergen, Norway; rolf.korneliussen@hi.no | |
| 773 | 0 | |t Sensors |g vol. 25, no. 10 (2025), p. 3121 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
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