Predicting Aboveground Biomass Increment of Tropical Forests from Litter-fall

Predicting Aboveground Biomass Increment (ABI) of forests is important in evaluating primary productivity, and hence aboveground C sequestration rate and C policy implementation. Although there are direct methods such as remote sensing to predict the ABI, it is important to develop ground-based indirect methods, particularly for tropical forests, due to their stratification, complex structure and high diversity, which cannot be imaged properly using the direct methods. Present study developed regression models from a global database of tropical forests to predict the ABI from annual litter-fall. The new models could predict up to 92% and 66% of the variability of the ABI of the disturbed/managed and natural tropical forests, respectively, compared to 69% of the variability predicted by previous models, although they have used a part of the present database which was only available at that time. Field prediction of the new models by using a wet zone forest and a dry zone forest in Sri Lanka showed that the ABIs of the two forests (7-8 Mg ha yr) are towards the upper limit (10 Mg ha yr) of the tropical forests of the world. It is clear from this study that the new approach may be a better method for predicting the ABI in future research as well as tropical forest inventories. It is recommended however, that the models should be validated before their wider applications.