BIOMASS ESTIMATION MODEL AND CARBON DIOXIDE SEQUESTRATION FOR MANGROVE FOREST USING SENTINEL-2 IN BENOA BAY, BALI

Authors

  • A. A. Md. Ananda Putra Suardana Research Center for Oceanography, National Research and Innovation Agency (BRIN)
  • Nanin Anggraini Research Center for Remote Sensing, National Research and Innovation Agency (BRIN)
  • Kholifatul Aziz Center for Data and Information, National Research and Innovation Agency (BRIN)
  • Muhammad Rizki Nandika Research Center for Oceanography, National Research and Innovation Agency (BRIN)
  • Azura Ulfa Research Center for Remote Sensing, National Research and Innovation Agency (BRIN)
  • Agung Dwi Wijaya Center for Data and Information, National Research and Innovation Agency (BRIN)
  • Abd. Rahman As-syakur Center for Remote Sensing and Ocean Science (CReSOS), Udayana University
  • Gathot Winarso Research Center for Remote Sensing, National Research and Innovation Agency (BRIN)
  • Wiji Prasetio Research Center for Remote Sensing, National Research and Innovation Agency (BRIN)
  • Ratih Dewanti Research Center for Remote Sensing, National Research and Innovation Agency (BRIN)

DOI:

https://doi.org/10.30536/j.ijreses.2022.v19.a3797

Keywords:

Mangroves, remote sensing, vegetation indices, biomass, CO2 sequestration

Abstract

Remote sensing technology can be used to find out the potential of mangrove forests information. One of the potentials is to be able to absorb three times more CO2 than other forests. CO2 absorbed during the photosynthesis process, produces organic compounds that are stored in the mangrove forest biomass. Utilization of remote sensing technology is able to detect mangrove forest biomass using the density level of the vegetation index. This study focuses on determining the best AGB model based on the vegetation index and the ability of mangrove forests to absorb CO2. This research was conducted in Benoa Bay, Bali Province, Indonesia. The satellite image used is Sentinel-2. Classification of mangroves and non-mangroves using a multivariate random forest algorithm. Furthermore, the mangrove forest biomass model using a semi-empirical approach, while the estimation of CO2 sequestration using allometric equations. Mean Absolute Error (MAE) is used to evaluate the validation of the model results. The classification results showed that the detected area of Benoa Bay mangrove forest reached 1134 ha (OA: 0.98, kappa: 0.95). The best AGB estimation result is the DVI-based AGB model (MAE: 23,525) with a value range of 0 to 468.38 Mg/ha. DVI-based AGB derivatives are BGB with a value range of 0 to 79.425 Mg/ha, TAB with a value range of 0 to 547.8 Mg/ha, TCS with a value range of 0 to 257.47 Mg/ha, and ACS with a value range of 0 to 944.912 Mg/ha.

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Published

2025-11-25

Issue

Vol. 19 No. 1 (2022)

Section

Articles

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Copyright (c) 2022 Author (S)

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