SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY

Manessa, Masita Dwi Mandini; Kanno, Ariyo; Sekine, Masahiko; Haidar, Muhammad; Yamamoto, Koichi; Imai, Tsuyoshi; Higuchi, Takaya

doi:10.14710/geoplanning.3.2.117-126

Akses terbuka Jurnal SINTA 1

SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY

Geoplanning: Journal of Geomatics and Planning · Vol. 3 No. 2 (2016) · hlm. 117–126

DOI: 10.14710/geoplanning.3.2.117-126
ISSN: 2355-6544
Terbit: 2016-10-25
Penerbit: Department of Urban and Regional Planning, Diponegoro University
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-nc-sa/4.0

Abstrak

In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy.

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27 penulis · 77 tautan kepenulisan bersama · 45 lintas organisasi

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Universitas Islam Malang
Yamaguchi University
University of Brawijaya
University of Indonesia
Udayana University
The University of Tokyo
Sebelas Maret University
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Rekan penulis	Organisasi	Artikel bersama
Novi Arfarita	Universitas Islam Malang	9
Sativandi Riza	University of Brawijaya	5
Evi Kurniati	University of Brawijaya	3
Budi Prasetya	University of Brawijaya	2
Cahyo Prayogo	University of Brawijaya	2
Djuhari Djuhari	Universitas Islam Malang	1
Masita Dwi Mandini Manessa	University of Indonesia	1
Yusuf Nauval Fadhlurahman	University of Indonesia	1
Jarot Mulyo Semedi	University of Indonesia	1
Anisya Feby Efriana	University of Indonesia	1
N Hidayati	Universitas Islam Malang	1
Masyhuri Machfudz	Universitas Islam Malang	1
Anis Kholifatur Rosyidah	Afiliasi tidak diketahui	1
Mahayu Woro Lestari	Universitas Islam Malang	1
Indiyah Murwani	Universitas Islam Malang	1
Anton Muhibuddin	University of Brawijaya	1
Mohammad Jasa Afroni	Universitas Islam Malang	1
Soni Sugiarto	Universitas Islam Malang	1
Masyhuri Masyhuri	Universitas Islam Malang	1
Sugiarto Sugiarto	Sebelas Maret University	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ejournal.undip.ac.id/index.php/geoplanning/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY</dc:title>
	<dc:creator>Manessa, Masita Dwi Mandini</dc:creator>
	<dc:creator>Kanno, Ariyo</dc:creator>
	<dc:creator>Sekine, Masahiko</dc:creator>
	<dc:creator>Haidar, Muhammad</dc:creator>
	<dc:creator>Yamamoto, Koichi</dc:creator>
	<dc:creator>Imai, Tsuyoshi</dc:creator>
	<dc:creator>Higuchi, Takaya</dc:creator>
	<dc:subject xml:lang="en-US">Satellite-derived bathymetry; Worldview-2; Random Forest; Multiple Linear Regression</dc:subject>
	<dc:description xml:lang="en-US">In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy. </dc:description>
	<dc:publisher xml:lang="en-US">Department of Urban and Regional Planning, Diponegoro University</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2016-10-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ejournal.undip.ac.id/index.php/geoplanning/article/view/12047</dc:identifier>
	<dc:identifier>10.14710/geoplanning.3.2.117-126</dc:identifier>
	<dc:source xml:lang="en-US">Geoplanning: Journal of Geomatics and Planning; Vol 3, No 2 (2016); 117-126</dc:source>
	<dc:source>2355-6544</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ejournal.undip.ac.id/index.php/geoplanning/article/view/12047/Manessa_et_al.</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2016 Geoplanning: Journal of Geomatics and Planning</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
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