Estimation and Modelling of Above Ground Biomass of Mangrove Forest in Keti Bunder, Indus Delta

Abstract

Mangroves forest ecosystem, distributed along the coastal belts of Pakistan, are in a constant flux. They play a key role in carbon cycle and support biodiversity. Accurate mangrove forest Above Ground Biomass (AGB) estimation is an integral part of sustainable forest management and help to understand how they are affected by climatic changes and anthropogenic activities. The current study’s aim was to construct a non-destructive allometric equation derived by performing stepwise linear regression on field AGB and vegetation indices to estimate the AGB of mangroves of Keti Bunder. The objectives of the current study were (a) analyze the correlation between field AGB and selected vegetation indices, (b) develop a regression equation based on stepwise linear regression, (c) estimate the amount of carbon stock and CO2 sequestered by the study area. 30 sample plots and 5 vegetation indices were used to analyze the potential of Sentinel-2 to predict AGB. It was found that Modified Simple Ratio (MSR) exhibit strong correlation with field AGB (r = 0.73, r 2 = 0.54) as compared to GNDVI, NDVI, CMRI and NDI45. The estimated AGB of the study area from the predicted model was found to be up to 51 t/ha (r 2 = 0.643). The Root Mean Square Error (RMSE) value was 16.6 t/ha. It was concluded that vegetation indices derived from Sentinel-2 can demonstrate good results in AGB prediction for mangrove forests.