Estimating Mangrove Aboveground Biomass Using Sentinel-2 and ALOS-2 Imagery: A Case Study of the Matang Mangrove Reserve, Malaysia

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Abstract
Mangroves play a critical role in global carbon sequestration, biodiversity conservation, and climate change mitigation. Accurately quantifying mangrove biomass is essential for sustainable forest management and carbon accounting. Yet, the structural complexity and species diversity of mangrove ecosystems pose significant challenges for accurate estimation. In this study, we developed an integrated model that combines multispectral imagery and radar data. Using Sentinel-2 and ALOS-2 satellite imagery combined with field measurements, these data were used to construct linear regression and random forest models for the Matang Mangrove Reserve, Malaysia. We further analyzed the relationships between vegetation indices, radar polarization modes, and biomass. Results indicate that the average biomass is approximately 146 t/ha. The Optimized Soil-Adjusted Vegetation Index (OSAVI) and horizontal–vertical (HV) polarization showed the strongest correlation with field-measured biomass, with an R2 of 0.735 and a root mean square error (RMSE) of 46.794 t/ha. This study provides a scientific basis and technical support for mangrove carbon stock assessment, ecosystem management, and climate change mitigation strategies, and highlights the potential of integrating optical and radar remote sensing for large-scale mangrove biomass monitoring.
Funding Information
  • Prof Gs Dr Abdul Rashid Bin Mohamed Shariff (ER3A2N545)

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