Remote Sensing-Based Estimation of Mangrove Above-Ground Carbon Using Sentinel-2 Vegetation Indices and Random Forest

Aulia Lanudia Fathah; Bambang Semedi; Fitri Candra Wardana; Andik Isdianto

doi:10.48084/etasr.14335

Authors

Aulia Lanudia Fathah Master Program of Environmental Management & Development, Brawijaya University, Indonesia https://orcid.org/0009-0005-0443-5241
Bambang Semedi Master Program of Environmental Management & Development, Brawijaya University, Indonesia https://orcid.org/0009-0009-9074-2924
Fitri Candra Wardana Master Program of Environmental Management & Development, Brawijaya University, Indonesia
Andik Isdianto Department of Marine Science, Brawijaya University, Indonesia https://orcid.org/0000-0002-4808-7868

Volume: 15 | Issue: 6 | Pages: 29598-29604 | December 2025 | https://doi.org/10.48084/etasr.14335

Received: 26 August 2025 | Revised: 10 September 2025, 23 September 2025, and 28 September 2025 | Accepted: 6 October 2025 | Online: 8 December 2025

Corresponding author: Andik Isdianto

Abstract

Mangrove ecosystems are critical blue carbon reservoirs. Accurate estimation of Above-Ground Carbon (AGC) is essential for climate change mitigation and coastal management. This study applies Sentinel-2 multispectral imagery and vegetation indices with a Random Forest model to estimate mangrove AGC in fragmented coastal ecosystems of Ujungpangkah, Indonesia. Field-based AGC from 15 plots, derived using species-specific allometry, was integrated with Google Earth Engine processing of spectral bands and indices. The classification mask achieved high accuracy (OA = 0.946; = 0.810). Among predictors, the Shortwave Infrared Bands (SWIR; B11–B12) and the Transformed Vegetation Index (TRVI) were the most effective, outperforming traditional red-edge indices. External validation showed reliable performance (R² = 0.65; RMSE ≈ 1.05 tons C/ha), while the mapped mangroves averaged 25.3 tons C/ha, totaling ~2,036.5 tons C. SWIR was negatively correlated with AGC due to canopy water and structure, whereas TRVI captured canopy variability and reduced saturation. These findings highlight the potential of integrating Sentinel-2 imagery and vegetation indices with machine learning to provide cost-effective and replicable blue carbon monitoring for mangrove management.

Keywords:

blue carbon, Google Earth Engine, SWIR, TRVI, random forest modeling, coastal carbon mapping, tropical mangrove monitoring

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