Abstract
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-1 yr-1) are towards the upper limit (10 Mg ha-1 yr-1) 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.DOI: http://dx.doi.org/10.4038/cjsbs.v42i1.5897 Ceylon Journal of Science (Bio. Sci.) 42 (1): 35-40, 2013
Highlights
Aboveground Biomass Increment (ABI) is the growth of aerial biomass of forests at a particular time interval. This parameter is important in evaluating primary productivity, and in evaluating aboveground C sequestration rate and C policy implementation
There are several methods applied to measure the ABI, namely remote sensing satellite techniques (Coops and Waring, 2001; Goetz et al, 2009; Zhang et al, 2012), Eddy covariance technique (Zhang et al, 2012) and inverse method (Zhang et al, 2012), which determine above ground biomass or biomass increments more directly
Developing improved indirect techniques is important, since they can be used complementarily with direct techniques for better predictions. This is more important in the case of tropical forests because their complex structure and high diversity cannot be imaged properly from the direct techniques (Drake et al, 2003)
Summary
Aboveground Biomass Increment (ABI) is the growth of aerial biomass of forests at a particular time interval. Researchers have applied indirect modeling techniques to predict the ABI from measurable components such as annual litter-fall (Clark et al, 2001a; Neth et al, 2005). Using 13 tropical forest plots of a global database, Clark et al (2001a) predicted 69% of the variability of the ABI from annual litter-fall.
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