Estimating carbon biomass in forests using incomplete data

Historical vegetation studies have been of limited use in total aboveground biomass (AGB) estimation because they only report incomplete data consisting of tree diameter-class distributions or plot-level summaries, rather than data on each tree individual. To address this issue, we assessed an existing method (ST-n) and developed three new methods (ST-p, PL-n and PL-p) for estimating total AGB using only incomplete data from tropical forest plots. ST-n and ST-p apply to studies with tree diameter-class distributions (or stand tables, “ST”), whereas PL-n and PL-p apply to studies with plot-level summary variables (“PL”) in the form of total tree basal area, mean tree diameter, and total number of trees. ST-n and PL-n are non-parametric (“n”) methods that do not impose any form on the underlying distribution of tree diameters. In contrast, ST-p and PL-p are parametric (“p”) methods that involve fitting probability distributions of tree diameter to the data. We applied the methods to incomplete data from 58 1-ha plots in Panama and 300 1-ha pseudo-plots (generated by randomly sampling tree diameters from empirical distributions for three larger plots) in Southeast Asia, and four allometric equations. For these two regions and equations, ST-p gave low total proportional errors (TPEs, as measured by proportional root-mean-square error) of 1%–8%. In contrast, ST-n gave moderate to large TPEs of 10%–66%. PL-n and PL-p gave low to moderate TPEs of 5%–30%. The methods have great potential to expand the pool of large-scale baseline AGB assessments to historical studies with incomplete data.