Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park
In recent years, there has been an increasing trend of utilizing high-resolution multibeam echosounder (MBES) datasets and supervised classification via machine learning to create marine habitat maps. The purpose of current study was threefold: (1) to extract bathymetric and backscatter derivatives...
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my.utm.1077792024-10-02T07:32:16Z http://eprints.utm.my/107779/ Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park Muhamad, Muhammad Abdul Hakim Che Hasan, Rozaimi Md. Said, Najhan Mohd. Said, Mohd. Shahmy Razali, Raiz G Geography (General) T Technology (General) In recent years, there has been an increasing trend of utilizing high-resolution multibeam echosounder (MBES) datasets and supervised classification via machine learning to create marine habitat maps. The purpose of current study was threefold: (1) to extract bathymetric and backscatter derivatives from a multibeam dataset, (2) to measure the correlation between bathymetric and backscatter derivatives, and (3) to generate a marine habitat map using the Random Forest (RF). Tioman Marine Park (TMP), which is situated Southeast China Sea. MBES surveyed area are encompassed an area of 406 km² and served as the location for the study. Based on results and analysis, fourteen (14) derivative were derived from bathymetry map and backscatter mosaic. The second step involved integrating variables and a total of 152 of habitat ground-truth data were used, derived from underwater imageries, and sediment samples, into an RF model to generate a map of the marine habitat. Based on marine habitat map, six habitat classes including sand, rock, gravel and sand, coral rubble, coral and rock, and coral were classified. The distribution of coral habitat was found to be correlated with the depth of the bathymetry in the shallow water region. Therefore, the study has reached the conclusion that the integration between MBES derivatives, ground-truth data, and RF machine learning algorithm is an effective in classifying the distribution of marine habitats, specifically the coral habitat. 2023 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/107779/1/RozaimiCheHasan2023_MarineHabitatMappingusingMultibeam.pdf Muhamad, Muhammad Abdul Hakim and Che Hasan, Rozaimi and Md. Said, Najhan and Mohd. Said, Mohd. Shahmy and Razali, Raiz (2023) Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park. In: 9th International Conference on Geomatics and Geospatial Technology 2023, GGT 2023, 22 May 2023 - 25 May 2023, Kuala Lumpur, Malaysia. http://dx.doi.org/10.1088/1755-1315/1240/1/012006 |
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G Geography (General) T Technology (General) Muhamad, Muhammad Abdul Hakim Che Hasan, Rozaimi Md. Said, Najhan Mohd. Said, Mohd. Shahmy Razali, Raiz Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park |
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In recent years, there has been an increasing trend of utilizing high-resolution multibeam echosounder (MBES) datasets and supervised classification via machine learning to create marine habitat maps. The purpose of current study was threefold: (1) to extract bathymetric and backscatter derivatives from a multibeam dataset, (2) to measure the correlation between bathymetric and backscatter derivatives, and (3) to generate a marine habitat map using the Random Forest (RF). Tioman Marine Park (TMP), which is situated Southeast China Sea. MBES surveyed area are encompassed an area of 406 km² and served as the location for the study. Based on results and analysis, fourteen (14) derivative were derived from bathymetry map and backscatter mosaic. The second step involved integrating variables and a total of 152 of habitat ground-truth data were used, derived from underwater imageries, and sediment samples, into an RF model to generate a map of the marine habitat. Based on marine habitat map, six habitat classes including sand, rock, gravel and sand, coral rubble, coral and rock, and coral were classified. The distribution of coral habitat was found to be correlated with the depth of the bathymetry in the shallow water region. Therefore, the study has reached the conclusion that the integration between MBES derivatives, ground-truth data, and RF machine learning algorithm is an effective in classifying the distribution of marine habitats, specifically the coral habitat. |
format |
Conference or Workshop Item |
author |
Muhamad, Muhammad Abdul Hakim Che Hasan, Rozaimi Md. Said, Najhan Mohd. Said, Mohd. Shahmy Razali, Raiz |
author_facet |
Muhamad, Muhammad Abdul Hakim Che Hasan, Rozaimi Md. Said, Najhan Mohd. Said, Mohd. Shahmy Razali, Raiz |
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Muhamad, Muhammad Abdul Hakim |
title |
Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park |
title_short |
Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park |
title_full |
Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park |
title_fullStr |
Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park |
title_full_unstemmed |
Marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from Tioman Marine Park |
title_sort |
marine habitat mapping using multibeam echosounder survey and underwater video observations: a case study from tioman marine park |
publishDate |
2023 |
url |
http://eprints.utm.my/107779/1/RozaimiCheHasan2023_MarineHabitatMappingusingMultibeam.pdf http://eprints.utm.my/107779/ http://dx.doi.org/10.1088/1755-1315/1240/1/012006 |
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