Mapping above- and below-ground carbon stocks in the Sundarbans mangrove forest, Bangladesh

Abstract

The study estimated ecosystem carbon stocks in the Bangladesh Sundarbans using field inventory data with species-specific allometric models, carbon fractions and remote sensing data. The plot level ecosystem carbon stocks were interpolated with regression kriging using a forest-type map developed from Sentinel-2 MSI satellite imagery and GEDI-based canopy height data in Google Earth Engine (GEE) platform. Error propagation from the field measurement and allometric models was estimated and interpolated. The study highlighted that both the above-ground carbon (AGC) and soil organic carbon (SOC) were significantly higher in the oligohaline zone, followed by the mesohaline and polyhaline zone. Multiple regression results indicated that soil salinity, organic C: N and tree diameter were the best predictor for the variability of the SOC in the Sundarbans. To understand how individual species affects biomass estimates in mangrove forests, five species-specific and four genus-specific allometric models were developed. At the individual tree level, the generic allometric models overestimated AGB from 22% to 167% compared to the species-specific models. At the plot level, mean AGB significantly differed in all generic models compared to the species-specific models. Using measured species wood density (WD) in the allometric model showed 4.5% to 9.7% less biomass than WD from a published database. When using plot top height and plot average height rather than measured individual tree height, the AGB was overestimated and underestimated by 19.5% and 8.3%, respectively. The total 1 m SOC in the Sundarbans was 21.37 Teragram (Tg) and the total AGC stocks comprised 23.91 Tg. On the other hand, the total ecosystem carbon (TEC) stocks were 62.70 Tg, which is comparatively lower than most mangrove forests in the world. The study demonstrated a methodology that could be used as an IPCC (Intergovernmental Panel on Climate Change) Tier 3 approach for estimating TEC stocks in the Bangladesh Sundarbans and also to monitor TEC stocks in mangroves and other tropical forests. The study also emphasised the importance of spatial conservation planning to safeguard the carbon-rich zones in the Bangladesh Sundarbans from anthropogenic tourism and development activities to support climate change adaptation and mitigation strategies.