Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone...

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Autores principales:	Bisman Nababan, La Ode Khairum Mastu, Nurul Hazrina Idris, James P. Panjaitan
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	drone mapping benthic habitat OBIA SVM Wakatobi Science Q
Acceso en línea:	https://doaj.org/article/33304b3d872641b3baae3513c5d54064
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spelling	oai:doaj.org-article:33304b3d872641b3baae3513c5d540642021-11-11T18:57:26ZShallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses10.3390/rs132144522072-4292https://doaj.org/article/33304b3d872641b3baae3513c5d540642021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4452https://doaj.org/toc/2072-4292Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.Bisman NababanLa Ode Khairum MastuNurul Hazrina IdrisJames P. PanjaitanMDPI AGarticledronemappingbenthic habitatOBIASVMWakatobiScienceQENRemote Sensing, Vol 13, Iss 4452, p 4452 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	drone mapping benthic habitat OBIA SVM Wakatobi Science Q
spellingShingle	drone mapping benthic habitat OBIA SVM Wakatobi Science Q Bisman Nababan La Ode Khairum Mastu Nurul Hazrina Idris James P. Panjaitan Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses
description	Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.
format	article
author	Bisman Nababan La Ode Khairum Mastu Nurul Hazrina Idris James P. Panjaitan
author_facet	Bisman Nababan La Ode Khairum Mastu Nurul Hazrina Idris James P. Panjaitan
author_sort	Bisman Nababan
title	Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses
title_short	Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses
title_full	Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses
title_fullStr	Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses
title_full_unstemmed	Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses
title_sort	shallow-water benthic habitat mapping using drone with object based image analyses
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/33304b3d872641b3baae3513c5d54064
work_keys_str_mv	AT bismannababan shallowwaterbenthichabitatmappingusingdronewithobjectbasedimageanalyses AT laodekhairummastu shallowwaterbenthichabitatmappingusingdronewithobjectbasedimageanalyses AT nurulhazrinaidris shallowwaterbenthichabitatmappingusingdronewithobjectbasedimageanalyses AT jamesppanjaitan shallowwaterbenthichabitatmappingusingdronewithobjectbasedimageanalyses
_version_	1718431634986893312

Shallow-Water Benthic Habitat Mapping Using Drone with Object Based Image Analyses

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