Assessment of Above Ground Biomass and Carbon Sequestration in a Coastal Mangrove Sanctuary Using Vegetation Indices and Field Data Integration

Mangrove forests are globally recognized for their exceptional capacity to sequester carbon, making them vital ecosystems for climate mitigation and coastal resilience. This study assesses Above Ground Biomass (AGB) and corresponding carbon stock in the Tengragiri Wildlife Sanctuary of Bangladesh by integrating field measurements with Landsat-8-derived vegetation indices—NDVI, EVI, and SAVI—over the period 2020 to 2023. A total of 35 georeferenced plots were established to collect data on tree diameter, height, and species-specific wood density, from which AGB was estimated. The results showed that NDVI exhibited the highest correlation with AGB (R² = 0.8309 for polynomial models), followed by SAVI (R² = 0.7553), while EVI showed comparatively weaker performance (R² = 0.3462). Polynomial regression models consistently outperformed linear models, capturing the nonlinear relationship between vegetation indices and biomass in semi-saline, tidally influenced environments. The estimated AGB values ranged from 4806.70 to 6964.97 Mg/ha, which were converted to carbon stock using the IPCC default factor (0.47), and to CO₂ equivalents (3.67 × C), revealing the site’s significant carbon sequestration potential. The study highlights the spatial variability in biomass distribution across the sanctuary, identifying both conservation-priority zones and areas possibly impacted by anthropogenic activities. These findings reinforce the utility of NDVI-based remote sensing models as effective, scalable tools for carbon accounting and ecological monitoring, offering practical value for REDD + implementation, national climate mitigation planning, and sustainable mangrove management in data-scarce regions.