Above ground biomass estimation in the upper Blue Nile basin forests, North-Western Ethiopia

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management.