Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental Shelf
Biogenic reefs support marine biodiversity and play a key role in a healthy marine environment. Protecting and enhancing reef-building species, such as <i>Sabellaria spinulosa</i>, require mapping and monitoring strategies. A multi-scale and multi-sensor mapping campaign, including a mul...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/1/74 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588204828524544 |
|---|---|
| author | Timo Constantin Gaida Bas Binnerts Oscar Bos |
| author_facet | Timo Constantin Gaida Bas Binnerts Oscar Bos |
| author_sort | Timo Constantin Gaida |
| collection | DOAJ |
| description | Biogenic reefs support marine biodiversity and play a key role in a healthy marine environment. Protecting and enhancing reef-building species, such as <i>Sabellaria spinulosa</i>, require mapping and monitoring strategies. A multi-scale and multi-sensor mapping campaign, including a multi-beam echosounder, side-scan sonar (SSS), box corer and ROV with an attached video camera, has been carried out in the northern Brown Bank (Dutch Continental Shelf) in August 2023. A semi-automated classification workflow, based on a support vector machine (machine learning), was developed to map <i>Sabellaria</i> reefs using SSS and video data. Elevated <i>Sabellaria</i> reefs were classified with a precision and sensitivity of 52% and 49%, respectively. The classified SSS images were merged into full-coverage percentage maps of <i>Sabellaria</i> reef coverage. Located between the swales of the tidal ridges, it was estimated that the reefs cover an area of 3.8 to 5.7% within the surveyed areas. The maps indicate (1) on the large-scale a preference of <i>Sabellaria spinulosa</i> for settlement to the east of the deepest part of the swale and (2) on the small-scale a preference for the troughs towards the stoss side of the megaripples. The employed survey strategy and the developed classification workflow can be extended to other environmental areas and further developed into a standard monitoring procedure. |
| format | Article |
| id | doaj-art-68b52bc2acd049fa8204733456543fdc |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-68b52bc2acd049fa8204733456543fdc2025-01-24T13:36:46ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-011317410.3390/jmse13010074Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental ShelfTimo Constantin Gaida0Bas Binnerts1Oscar Bos2Department of Acoustic Signatures & Noise Control, TNO, 2597 AK Den Haag, The NetherlandsDepartment of Acoustic Signatures & Noise Control, TNO, 2597 AK Den Haag, The NetherlandsWageningen Marine Research, 1780 AB Den Helder, The NetherlandsBiogenic reefs support marine biodiversity and play a key role in a healthy marine environment. Protecting and enhancing reef-building species, such as <i>Sabellaria spinulosa</i>, require mapping and monitoring strategies. A multi-scale and multi-sensor mapping campaign, including a multi-beam echosounder, side-scan sonar (SSS), box corer and ROV with an attached video camera, has been carried out in the northern Brown Bank (Dutch Continental Shelf) in August 2023. A semi-automated classification workflow, based on a support vector machine (machine learning), was developed to map <i>Sabellaria</i> reefs using SSS and video data. Elevated <i>Sabellaria</i> reefs were classified with a precision and sensitivity of 52% and 49%, respectively. The classified SSS images were merged into full-coverage percentage maps of <i>Sabellaria</i> reef coverage. Located between the swales of the tidal ridges, it was estimated that the reefs cover an area of 3.8 to 5.7% within the surveyed areas. The maps indicate (1) on the large-scale a preference of <i>Sabellaria spinulosa</i> for settlement to the east of the deepest part of the swale and (2) on the small-scale a preference for the troughs towards the stoss side of the megaripples. The employed survey strategy and the developed classification workflow can be extended to other environmental areas and further developed into a standard monitoring procedure.https://www.mdpi.com/2077-1312/13/1/74side-scan sonaracousticsremote sensingSVMmachine learningcomputer vision |
| spellingShingle | Timo Constantin Gaida Bas Binnerts Oscar Bos Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental Shelf Journal of Marine Science and Engineering side-scan sonar acoustics remote sensing SVM machine learning computer vision |
| title | Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental Shelf |
| title_full | Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental Shelf |
| title_fullStr | Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental Shelf |
| title_full_unstemmed | Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental Shelf |
| title_short | Semi-Automated Classification of Side-Scan Sonar Data for Mapping <i>Sabellaria spinulosa</i> Reefs in the Brown Bank, Dutch Continental Shelf |
| title_sort | semi automated classification of side scan sonar data for mapping i sabellaria spinulosa i reefs in the brown bank dutch continental shelf |
| topic | side-scan sonar acoustics remote sensing SVM machine learning computer vision |
| url | https://www.mdpi.com/2077-1312/13/1/74 |
| work_keys_str_mv | AT timoconstantingaida semiautomatedclassificationofsidescansonardataformappingisabellariaspinulosaireefsinthebrownbankdutchcontinentalshelf AT basbinnerts semiautomatedclassificationofsidescansonardataformappingisabellariaspinulosaireefsinthebrownbankdutchcontinentalshelf AT oscarbos semiautomatedclassificationofsidescansonardataformappingisabellariaspinulosaireefsinthebrownbankdutchcontinentalshelf |