Estimating tree component biomass using variable probability sampling methods

As a signatory to the Kyoto Protocol, Australia is obliged to stabilize gre enhouse gas emissions at 8% above 1990 levels by 2008-2012. To demonstrate achievement of this goal, Australia requires national annual estimates of c hanges in vegetation biomass as greenhouse gas emissions from land use chan ge. These emission estimates are, however, uncertain due largely to the sca rcity of existing allometric equations for calculating biomass. The large i nvestment of time and funding required for harvesting, particularly using t raditional techniques such as double regression and ratio sampling, also pr ecludes the generation of new equations. Alternative techniques for rapid, cost-effective, and reliable estimation of biomass therefore require invest igation. This study, conducted in central Queensland, compared estimates of component biomass that were generated for seven trees of the woodland spec ies Eucalyptus populnea (poplar box) using ratio sampling and variable prob ability sampling techniques, namely randomized branch sampling (RBS) and RB S with importance sampling (IS). Application of randomized branch sampling consistently underestimated the biomass of leaf and small branches (<1 cm i n diameter) and produced weak prediction equations. In contrast, results su ggest that RBS with IS is particularly useful in predicting woody (trunk an d branches >1 cm in diameter) biomass, and prediction equations agreed with existing equations for this species. However, this method tended to overes timate individual tree woody biomass. The study concluded that RBS with IS was a viable alternative to current methods.