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Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado

Jurnal Geografi · Vol. 15 No. 2 (2023) · hlm. 135–144

DOI
10.24114/jg.v15i2.30795
ISSN
2549-7057, 2085-8167
Terbit
2023-06-09
Penerbit
Universitas Negeri Medan
Bahasa
en

Abstrak

Inggris

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 “ 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests

Inggris

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 “ 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests

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  • Universitas Gadjah Mada
  • Ministry of Marine Affairs and Fisheries
  • Indonesian National Institute of Aeronautics and Space
  • University of Bengkulu
  • National Research and Innovation Agency
  • Universitas Islam Negeri Sultan Syarif Kasim Riau
  • Hokkaido University
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Hartono HartonoUniversitas Gadjah Mada5
Totok GunawanUniversitas Gadjah Mada4
Bambang SulistyoUniversity of Bengkulu3
Sanjiwana ArjasakusumaUniversitas Gadjah Mada3
Ratih Dewanti DimyatiNational Research and Innovation Agency2
Kustiyo KustiyoIndonesian National Institute of Aeronautics and Space2
Iswari Nur HidayatiUniversitas Gadjah Mada2
R. SuharyadiUniversitas Gadjah Mada2
Novi Susetyo AdiMinistry of Marine Affairs and Fisheries2
Prima WidayaniUniversitas Gadjah Mada2
Hartono HartonoUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Deha Agus UmarhadiHokkaido University1
Rochmat MartantoUniversitas Gadjah Mada1
Nursida ArifUniversitas Muhammadiyah Gorontalo1
Aisya Jaya DhannahisvaraUniversitas Gadjah Mada1
Hartono HarjoUniversitas Gadjah Mada1
Ferman Setia NugrohoIndonesian National Institute of Aeronautics and Space1
Sigit HerumurtiAfiliasi tidak diketahui1
Prama Ardha AryagunaUniversitas Esa Unggul1
Yanuar Adji NugrohoUniversitas Gadjah Mada1
Arief WicaksonoUniversitas Gadjah Mada1
Faza AdhimahUniversitas Gadjah Mada1
Muhammad KamalUniversitas Gadjah Mada1
. HartonoState University of Semarang1
Junun SartohadiUniversitas Gadjah Mada1
M. Pramono HadiUniversitas Gadjah Mada1
Saparis SoedarjantoAfiliasi tidak diketahui1
Sugeng Juwono MardihusodoUniversitas Gadjah Mada1
Ignatius Salivian Wisnu KumaraUniversitas Gadjah Mada1
Muhammad Afif FauzanUniversitas Gadjah Mada1
Rifka Noviaris YogyantoroUniversitas Gadjah Mada1
Satrio W. SuwardanaUniversitas Gadjah Mada1
Nurul FadhilahUniversitas Gadjah Mada1
Intansania NurmalasariUniversitas Gadjah Mada1
Santi ApriyaniUniversitas Gadjah Mada1
Muhammad DimyatiUniversity of Indonesia1

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Sumber metadata: OAI-PMH (metadata jurnal) http://jurnal.unimed.ac.id/2012/index.php/geo/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado</dc:title>
	<dc:title xml:lang="en-US">Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado</dc:title>
	<dc:creator xml:lang="en">Cera, Fela Pritian</dc:creator>
	<dc:creator xml:lang="en">Danoedoro, Projo</dc:creator>
	<dc:creator xml:lang="en">Wicaksono, Pramaditya</dc:creator>
	<dc:creator xml:lang="en">Yasir, Moh</dc:creator>
	<dc:description xml:lang="en">The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 “ 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests</dc:description>
	<dc:description xml:lang="en-US">The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 “ 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Medan</dc:publisher>
	<dc:date>2023-06-09</dc:date>
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	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://jurnal.unimed.ac.id/2012/index.php/geo/article/view/30795</dc:identifier>
	<dc:identifier>10.24114/jg.v15i2.30795</dc:identifier>
	<dc:source xml:lang="en">JURNAL GEOGRAFI; Vol. 15 No. 2 (2023): JURNAL GEOGRAFI; 135-144</dc:source>
	<dc:source>2549-7057</dc:source>
	<dc:source>2085-8167</dc:source>
	<dc:source>10.24114/jg.v15i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://jurnal.unimed.ac.id/2012/index.php/geo/article/view/30795/21241</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 JURNAL GEOGRAFI</dc:rights>
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