Estimating mangrove aboveground biomass using Sentinel-2A Vegetation Indices in a tropical coastal ecosystem on the east coast of North Sumatra, Indonesia
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Abstract. Samsuri, Sipahutar RA, Zaitunah A, Samsura A, Budiharta S, Sulistioadi B. 2026. Estimating mangrove aboveground biomass using Sentinel-2A Vegetation Indices in a tropical coastal ecosystem on the east coast of North Sumatra, Indonesia. Asian J For 10 (1): r100131. https://doi.org/10.13057/asianjfor/r100131. Mangrove forests play a vital role in mitigating climate change by sequestering significant amounts of carbon; however, reliably estimating aboveground biomass (AGB) across large, heterogeneous coastal areas remains challenging. The study evaluates the performance of Sentinel-2A satellite imagery in deriving the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Transformed Vegetation Index (TVI) for modeling mangrove AGB in the east coast of North Sumatra, Indonesia. A total of 41 field plot samples were integrated with satellite-derived NDVI, GNDVI, and TVI using regression modeling. Regression analyses demonstrate that models based on GNDVI consistently outperform those based on NDVI and TVI. The best-performing model, a GNDVI-based power function (y = 23.29x³·¹⁵⁸⁵), achieved an R² of 0.60 with a relatively low prediction error (RMSE = 0.70). The spatial application of the selected model revealed an average mangrove AGB of 249.06 t ha-¹, with the highest biomass of 510.68 t ha-¹ in dense stands, indicating a relatively high carbon storage potential. The superior performance of GNDVI is attributed to its greater sensitivity to chlorophyll content in dense, multilayered mangrove canopies, where NDVI tends to saturate. These findings highlight the robustness of GNDVI for estimating mangrove biomass and underscore the utility of Sentinel-2 imagery for carbon stock assessment. The study demonstrates that Sentinel-2-based GNDVI modeling provides a reliable and cost-effective approach for large-scale mangrove biomass estimation, supporting improved carbon assessment and climate mitigation strategies.
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