Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>)
Modern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/9/1371 |
| _version_ | 1797569604608851968 |
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| author | Alexandre C. G. Schimel Craig J. Brown Daniel Ierodiaconou |
| author_facet | Alexandre C. G. Schimel Craig J. Brown Daniel Ierodiaconou |
| author_sort | Alexandre C. G. Schimel |
| collection | DOAJ |
| description | Modern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference noise that typically contaminates water-column data is a major obstacle to the detection of targets lying close to the seabed, such as aquatic vegetation. This article presents an algorithm to filter the noise and artefacts due to interference from the sidelobes of the receive array by normalizing the slant-range signal in each ping. To evaluate the potential of the filtered data for the detection of aquatic vegetation, we acquired a comprehensive water-column dataset over a controlled experimental site. The experimental site was a transplanted patch of giant kelp (<i>Macrocystis pyrifera</i>) forest of known biomass and spatial configuration, obtained by harvesting several individuals from a nearby forest, measuring and weighing them, and arranging them manually on an area of seafloor previously bare. The water-column dataset was acquired with a Kongsberg EM 2040 C multibeam echosounder at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest. The giant kelp plants produced evident echoes in the water-column data at all settings. The slant-range signal normalization filter greatly improved the visual quality of the data, but the filtered data may under-represent the true amount of acoustic energy in the water column. Nonetheless, the overall acoustic backscatter measured after filtering was significantly lower, by 2 to 4 dB on average, for data acquired over the thinned forest compared to the original experiment. We discuss the implications of these results for the potential use of multibeam echosounder water-column data in marine habitat mapping. |
| first_indexed | 2024-03-10T20:12:57Z |
| format | Article |
| id | doaj.art-e8c6f69b5faf40cfbccc0b2db845ece2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:12:57Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e8c6f69b5faf40cfbccc0b2db845ece22023-11-19T22:47:48ZengMDPI AGRemote Sensing2072-42922020-04-01129137110.3390/rs12091371Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>)Alexandre C. G. Schimel0Craig J. Brown1Daniel Ierodiaconou2National Institute of Water and Atmospheric Research (NIWA), Greta Point, Wellington 6021, New ZealandDepartment of Oceanography, Dalhousie University, Halifax, Nova Scotia B3H 4R2, CanadaSchool of Life and Environmental Sciences, Centre for Integrative Ecology, Deakin University, Warrnambool 3280, VIC, AustraliaModern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference noise that typically contaminates water-column data is a major obstacle to the detection of targets lying close to the seabed, such as aquatic vegetation. This article presents an algorithm to filter the noise and artefacts due to interference from the sidelobes of the receive array by normalizing the slant-range signal in each ping. To evaluate the potential of the filtered data for the detection of aquatic vegetation, we acquired a comprehensive water-column dataset over a controlled experimental site. The experimental site was a transplanted patch of giant kelp (<i>Macrocystis pyrifera</i>) forest of known biomass and spatial configuration, obtained by harvesting several individuals from a nearby forest, measuring and weighing them, and arranging them manually on an area of seafloor previously bare. The water-column dataset was acquired with a Kongsberg EM 2040 C multibeam echosounder at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest. The giant kelp plants produced evident echoes in the water-column data at all settings. The slant-range signal normalization filter greatly improved the visual quality of the data, but the filtered data may under-represent the true amount of acoustic energy in the water column. Nonetheless, the overall acoustic backscatter measured after filtering was significantly lower, by 2 to 4 dB on average, for data acquired over the thinned forest compared to the original experiment. We discuss the implications of these results for the potential use of multibeam echosounder water-column data in marine habitat mapping.https://www.mdpi.com/2072-4292/12/9/1371multibeam sonarmultibeam echosounderwater-column dataspecular artefactseabed mappingbenthic habitat |
| spellingShingle | Alexandre C. G. Schimel Craig J. Brown Daniel Ierodiaconou Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>) Remote Sensing multibeam sonar multibeam echosounder water-column data specular artefact seabed mapping benthic habitat |
| title | Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>) |
| title_full | Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>) |
| title_fullStr | Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>) |
| title_full_unstemmed | Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>) |
| title_short | Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (<i>Macrocystis pyrifera</i>) |
| title_sort | automated filtering of multibeam water column data to detect relative abundance of giant kelp i macrocystis pyrifera i |
| topic | multibeam sonar multibeam echosounder water-column data specular artefact seabed mapping benthic habitat |
| url | https://www.mdpi.com/2072-4292/12/9/1371 |
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