Quantifying Area Back Scatter of Marine Organisms in the Arctic Ocean by Machine Learning-Based Post-Processing of Volume Back Scatter
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 <i>R/V Kronprins Haakon</i> during surveys in the Central Arctic Ocean (CAO)...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/3121 |
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| Summary: | 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 <i>R/V Kronprins Haakon</i> 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. |
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| ISSN: | 1424-8220 |