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Estimation and Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices in Benoa Bay of Bali Province, Indonesia

Forest and Society · Vol. 7 No. 1 (2023) · hlm. 116–134

DOI
10.24259/fs.v7i1.22062
ISSN
2549-4333, 2549-4724
Terbit
2023-03-13
Penerbit
Forestry Faculty, Universitas Hasanuddin
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Carbon dioxide (CO2) is one of the greenhouse gases that causes global warming with the highest concentration in the atmosphere. Mangrove forests can absorb CO2 three times higher than terrestrial forests and tropical rainforests. Moreover, mangrove forests can be a source of Indonesian income in the form of a blue economy, therefore an accurate method is needed to investigates mangrove carbon stock. Utilization of remote sensing data with the results of the above-ground carbon (AGC) detection model of mangrove forests based on multispectral imaging and vegetation index, can be a solution to get fast, cheap, and accurate information related to AGC estimation. This study aimed to investigates the best model for estimating the AGC of mangroves using Sentinel-2 imagery in Benoa Bay, Bali Province. The random forest (RF) method was used to classified the difference between mangrove and non-mangrove with the treatment of several parameters. Furthermore, a semi-empirical approach was used to assessed and map the AGC of mangroves. Allometric equations were used to calculated and produced AGC per species. Moreover, the model was built with linear regression equations for one variable x, and multiple regression equations for more than one x variable. Root Mean Square Error (RMSE) was used to assess the validation of the model results. The results of the mangrove forests area detected in the research location around 1134.92 ha, with an Overall Accuracy (OA) of 0.984 and a kappa coefficient of 0.961. This study highlights that the best model was the combination of IRECI and TRVI vegetation indices (RMSE: 11.09 Mg/ha) for a model based on red edge bands. Meanwhile, the best results from the model that does not use the red edge band were the combination of TRVI and DVI vegetation indices (RMSE: 13.63 Mg/ha). The use of red edge and NIR bands is highly recommended in building the AGC model of mangrove forests because they can increase the accuracy value. Thus, the results of this study are highly recommended in estimating the AGC of mangrove forests, because it has been proven to be able to increase the accuracy value of previous studies using optical images.

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  • National Research and Innovation Agency
  • University of Indonesia
  • Udayana University
  • Universitas Gadjah Mada
  • Yamaguchi University
  • Sumatera Institute of Technology
  • Ministry of Marine Affairs and Fisheries
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Rekan penulisOrganisasiArtikel bersama
Kuncoro Teguh SetiawanNational Research and Innovation Agency6
Ratih Dewanti DimyatiNational Research and Innovation Agency4
Nugraheni SetyaningrumNational Research and Innovation Agency4
Devica Natalia Br GintingNational Research and Innovation Agency4
Takahiro OsawaYamaguchi University3
Masita Dwi Mandini ManessaUniversity of Indonesia2
Novi Susetyo AdiMinistry of Marine Affairs and Fisheries2
Mohammad Syamsu RosidUniversity of Indonesia2
Wikanti AsriningrumIndonesian National Institute of Aeronautics and Space2
Agustinus Harsono SupardjoUniversity of Indonesia2
Abdul BasithUniversitas Gadjah Mada1
Muhammad Ulin NuhaSumatera Institute of Technology1
Ratna PrastyaniUniversitas Gadjah Mada1
Putu Satya Pratama AtmajaUdayana University1
I. Ketut Aditya Krisna LaharjanaUdayana University1
Mike van KeulenMurdoch University1
Iqbal Putut Ash ShidiqUniversity of Indonesia1
Faris ZulkarnainUniversity of Indonesia1
Ahmad ZubairUniversity of Indonesia1
Triarko NurlambangUniversity of Indonesia1
Babag PurbantoroNational Research and Innovation Agency1
Siswanto SiswantoMeteorological, Climatological, And Geophysical Agency1
Evi AnggraheniUniversity of Indonesia1
Pranita GiardiniUniversity of Indonesia1
Kintan MaulidinaUniversity of Indonesia1
Raisya AfifahUniversity of Indonesia1
Anang MuchlisMinistry of Public Works, Republic of Indonesia1
Andry RustantoUniversity of Indonesia1
Muhammad DimyatiAfiliasi tidak diketahui1
Irma SusantiUniversity of Indonesia1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/fs/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Estimation and Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices in Benoa Bay of Bali Province, Indonesia</dc:title>
	<dc:creator xml:lang="en">Suardana, A. A. Md. Ananda Putra</dc:creator>
	<dc:creator xml:lang="en">Anggraini, Nanin</dc:creator>
	<dc:creator xml:lang="en">Nandika, Muhammad Rizki </dc:creator>
	<dc:creator xml:lang="en">Aziz, Kholifatul</dc:creator>
	<dc:creator xml:lang="en">As-syakur, Abd. Rahman</dc:creator>
	<dc:creator xml:lang="en">Ulfa, Azura </dc:creator>
	<dc:creator xml:lang="en">Wijaya, Agung Dwi</dc:creator>
	<dc:creator xml:lang="en">Prasetio, Wiji </dc:creator>
	<dc:creator xml:lang="en">Winarso, Gathot </dc:creator>
	<dc:creator xml:lang="en">Dewanti, Ratih </dc:creator>
	<dc:subject xml:lang="en">Carbon Stock</dc:subject>
	<dc:subject xml:lang="en">Mangrove Forests</dc:subject>
	<dc:subject xml:lang="en">Sentinel Image</dc:subject>
	<dc:subject xml:lang="en">Model</dc:subject>
	<dc:subject xml:lang="en">Vegetation indices</dc:subject>
	<dc:subject xml:lang="en">Coastal zone</dc:subject>
	<dc:description xml:lang="en">Carbon dioxide (CO2) is one of the greenhouse gases that causes global warming with the highest concentration in the atmosphere. Mangrove forests can absorb CO2 three times higher than terrestrial forests and tropical rainforests. Moreover, mangrove forests can be a source of Indonesian income in the form of a blue economy, therefore an accurate method is needed to investigates mangrove carbon stock. Utilization of remote sensing data with the results of the above-ground carbon (AGC) detection model of mangrove forests based on multispectral imaging and vegetation index, can be a solution to get fast, cheap, and accurate information related to AGC estimation. This study aimed to investigates the best model for estimating the AGC of mangroves using Sentinel-2 imagery in Benoa Bay, Bali Province. The random forest (RF) method was used to classified the difference between mangrove and non-mangrove with the treatment of several parameters. Furthermore, a semi-empirical approach was used to assessed and map the AGC of mangroves. Allometric equations were used to calculated and produced AGC per species. Moreover, the model was built with linear regression equations for one variable x, and multiple regression equations for more than one x variable. Root Mean Square Error (RMSE) was used to assess the validation of the model results. The results of the mangrove forests area detected in the research location around 1134.92 ha, with an Overall Accuracy (OA) of 0.984 and a kappa coefficient of 0.961. This study highlights that the best model was the combination of IRECI and TRVI vegetation indices (RMSE: 11.09 Mg/ha) for a model based on red edge bands. Meanwhile, the best results from the model that does not use the red edge band were the combination of TRVI and DVI vegetation indices (RMSE: 13.63 Mg/ha). The use of red edge and NIR bands is highly recommended in building the AGC model of mangrove forests because they can increase the accuracy value. Thus, the results of this study are highly recommended in estimating the AGC of mangrove forests, because it has been proven to be able to increase the accuracy value of previous studies using optical images.</dc:description>
	<dc:publisher xml:lang="en">Forestry Faculty, Universitas Hasanuddin</dc:publisher>
	<dc:date>2023-03-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/fs/article/view/22062</dc:identifier>
	<dc:identifier>10.24259/fs.v7i1.22062</dc:identifier>
	<dc:source xml:lang="en">Forest and Society; Vol. 7 No. 1 (2023): APRIL; 116-134</dc:source>
	<dc:source>2549-4333</dc:source>
	<dc:source>2549-4724</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/fs/article/view/22062/9476</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Forest and Society</dc:rights>
	<dc:rights xml:lang="en">http://creativecommons.org/licenses/by/4.0</dc:rights>
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