1Marine Technology Study Program, Department of Marine Science and Technology, Faculty of Fisheries and Marine Sciences, IPB University, Indonesia
2Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University, Indonesia
3Department of Computer Science, Faculty of Mathematics and Natural Science, IPB University, Indonesia
4 Department of Generative AI, Faculty of Artificial Inteligence, Universiti Teknologi Malaysia, Malaysia
BibTex Citation Data :
@article{IK.IJMS67893, author = {Dadang Handoko and Henry Manik and Totok Hestirianoto and Karlisa Priandana and Rozaimi Che Hasan}, title = {Acoustic Sediment Classification Using High-Frequency (400 kHz) Multibeam Data in Pari Water of Seribu Island, Indonesia}, journal = {ILMU KELAUTAN: Indonesian Journal of Marine Sciences}, volume = {30}, number = {1}, year = {2025}, keywords = {Pari Island; Multibeam Echosounder System (MBES); Backscatter; Seafloor Classification}, abstract = { Seafloor classification is essential for understanding sediment distribution, marine habitat characteristics, and resource management. Therefore, this study aimed to classify seafloor sediment in the Pari water, Indonesia using high-frequency (400 kHz) backscatter data obtained through the Multibeam Echosounder T-50P. The Angular Range Analysis (ARA) method was applied to analyze backscatter intensity variations across different incidence angles, to enhance the accuracy of sediment classification in this shallow marine environment. Data acquisition was collected using the T-50P, which captured high-resolution acoustic signals from varying angles to generate angular response curves. Analysis was conducted in the curves were then analyzed to differentiate sediment types, with ground-truth sediment samples collected to validate classification outcomes. The result showed that backscatter intensity mosaic had an intensity range of -27 dB to -37.5 dB. Applying ARA enabled the identification of 12 sediment classes, including sandy silt, coarse silt, and clayey sand. Sediment distribution maps, generated via FMGT and visualized with ArcGIS, indicated a predominance of fine-grained sediments. The FMGT-based classification tended to prioritize finer sediment categories, likely due to the acoustic limitations in detecting granular details. Conversely, the in-situ analysis of 15 sediment samples revealed medium sand as the predominant sediment type, accompanied by smaller proportions of coarse sand and coral fragments. The discrepancies between the in-situ sampling and FMGT results were primarily due to the operational frequency of the MBES system, which limits the acoustic signal's penetration to the surface of the seabed. This highlights the importance of in-situ sampling to complement acoustic data, especially in accurately seabed characterization. }, issn = {2406-7598}, pages = {135--144} doi = {10.14710/ik.ijms.30.1.135-144}, url = {https://ejournal.undip.ac.id/index.php/ijms/article/view/67893} }
Refworks Citation Data :
Seafloor classification is essential for understanding sediment distribution, marine habitat characteristics, and resource management. Therefore, this study aimed to classify seafloor sediment in the Pari water, Indonesia using high-frequency (400 kHz) backscatter data obtained through the Multibeam Echosounder T-50P. The Angular Range Analysis (ARA) method was applied to analyze backscatter intensity variations across different incidence angles, to enhance the accuracy of sediment classification in this shallow marine environment. Data acquisition was collected using the T-50P, which captured high-resolution acoustic signals from varying angles to generate angular response curves. Analysis was conducted in the curves were then analyzed to differentiate sediment types, with ground-truth sediment samples collected to validate classification outcomes. The result showed that backscatter intensity mosaic had an intensity range of -27 dB to -37.5 dB. Applying ARA enabled the identification of 12 sediment classes, including sandy silt, coarse silt, and clayey sand. Sediment distribution maps, generated via FMGT and visualized with ArcGIS, indicated a predominance of fine-grained sediments. The FMGT-based classification tended to prioritize finer sediment categories, likely due to the acoustic limitations in detecting granular details. Conversely, the in-situ analysis of 15 sediment samples revealed medium sand as the predominant sediment type, accompanied by smaller proportions of coarse sand and coral fragments. The discrepancies between the in-situ sampling and FMGT results were primarily due to the operational frequency of the MBES system, which limits the acoustic signal's penetration to the surface of the seabed. This highlights the importance of in-situ sampling to complement acoustic data, especially in accurately seabed characterization.
Article Metrics:
Last update:
Last update: 2025-03-26 08:09:01
Copy this form and after filling it, please send it to ijms@live.undip.ac.id:
COPYRIGHT TRANSFER STATEMENT
When this article is accepted for publication, its copyright is transferred to ILMU KELAUTAN Indonesian Journal of Marine Sciences, UNDIP. The copyright transfer covers the non exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, microform, electronic form (offline, online) or any other reproductions of similar nature.
The author warrants that this article is original and that the author has full power to publish. The author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. In regard to all kind of plagiarism in this manuscript, if any, only the author(s) will take full responsibility. If the article is based on or part of student’s skripsi, thesis or dissertation, the student needs to sign as his/her agreement that his/her works is going to be published.
Title of article :...........................................................................................................................Name of Author(s) :...........................................................................................................................Author’s signature :...........................................................................................................................Date :...........................................................................................................................
View My Stats