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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) · pp. 116–134

DOI
10.24259/fs.v7i1.22062
ISSN
2549-4333, 2549-4724
Published
2023-03-13
Publisher
Forestry Faculty, Universitas Hasanuddin
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

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.

Keywords

Collaboration network of the authors

40 authors · 223 co-authorship links · 19 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • National Research and Innovation Agency
  • University of Indonesia
  • Udayana University
  • Universitas Gadjah Mada
  • Hasanuddin University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Kuncoro Teguh SetiawanAffiliation unknown6
Ratih Dewanti DimyatiUniversitas Gadjah Mada4
Nugraheni SetyaningrumAffiliation unknown4
Devica Natalia Br GintingAffiliation unknown4
Takahiro OsawaAffiliation unknown3
Masita Dwi Mandini ManessaAffiliation unknown2
Novi Susetyo AdiAffiliation unknown2
Mohammad Syamsu RosidAffiliation unknown2
Wikanti AsriningrumAffiliation unknown2
Harsono SupardjoAffiliation unknown2
Muhammad Ulin NuhaAffiliation unknown1
Abdul BasithAffiliation unknown1
Ratna PrastyaniAffiliation unknown1
Putu Satya Pratama AtmajaAffiliation unknown1
I Ketut Aditya Krisna LaharjanaAffiliation unknown1
Mike Van KeulenAffiliation unknown1
Muhammad DimyatiUniversity of Indonesia1
Faris ZulkarnainUniversity of Indonesia1
Siswanto SiswantoHasanuddin University1
Irma SusantiAffiliation unknown1
Evi AnggraheniAffiliation unknown1
Pranita GiardiniAffiliation unknown1
Kintan MaulidinaAffiliation unknown1
Babag PurbantoroAffiliation unknown1
Raisya AfifahAffiliation unknown1
Anang MuchlisAffiliation unknown1
Andry RustantoAffiliation unknown1
Ahmad ZubairAffiliation unknown1
Triarko NurlambangAffiliation unknown1
Iqbal Putut Ash-ShidiqAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/fs/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (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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