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Vol. 7 No. 1 (2023): APRIL

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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

https://doi.org/10.24259/fs.v7i1.22062
A. A. Md. Ananda Putra Suardana
Research Center for Oceanography, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0002-1682-2768
Nanin Anggraini
Research Center for Remote Sensing, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0003-2460-2359
Muhammad Rizki Nandika
Research Center for Oceanography, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0003-2514-4927
Kholifatul Aziz
Center for Data and Information, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0001-7643-2772
Abd. Rahman As-syakur
Center for Remote Sensing and Ocean Science (CReSOS), Udayana University, Denpasar, Indonesia
https://orcid.org/0000-0003-3881-634X
Azura Ulfa
Research Center for Remote Sensing, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0002-2229-9045
Agung Dwi Wijaya
Center for Data and Information, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0002-2826-5145
Wiji Prasetio
Research Center for Remote Sensing, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0002-3415-2024
Gathot Winarso
Research Center for Remote Sensing, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0002-8296-9354
Ratih Dewanti
Research Center for Remote Sensing, National Research and Innovation Agency (BRIN), Jakarta, Indonesia
https://orcid.org/0000-0001-5927-1372

Corresponding Author(s) : A. A. Md. Ananda Putra Suardana

anandayobi@gmail.com

Forest and Society, Vol. 7 No. 1 (2023): APRIL

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

Carbon Stock Mangrove Forests Sentinel Image Model Vegetation indices Coastal zone
Suardana, A. A. M. A. P., Anggraini, N., Nandika, M. R. ., Aziz, K., As-syakur, A. R., Ulfa, A. ., Wijaya, A. D., Prasetio, W. ., Winarso, G. ., & Dewanti, R. . (2023). 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, 7(1), 116-134. https://doi.org/10.24259/fs.v7i1.22062
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