Methods for estimating the component biomass of a single tree and a stand of trees using variable probability sampling techniques

This thesis developed multistage sampling methods for estimating the aggregate biomass of selected tree components, such as leaves, branches, trunk and total, in woodlands in central and western Queensland. To estimate the component biomass of a single tree randomised branch sampling (RBS) and importance sampling (IS) were trialed. RBS and IS were found to reduce the amount of time and effort to sample tree components in comparison with other standard destructive sampling methods such as ratio sampling, especially when sampling small components such as leaves and small twigs. However, RBS did not estimate leaf and small twig biomass to an acceptable degree of precision using current methods for creating path selection probabilities. In addition to providing an unbiased estimate of tree component biomass, individual estimates were used for developing allometric regression equations. Equations based on large components such as total biomass produced narrower confidence intervals than equations developed using ratio sampling. However, RBS does not estimate small component biomass such as leaves and small wood components with an acceptable degree of precision, and should be mainly used in conjunction with IS for estimating larger component biomass. A whole tree was completely enumerated to set up a sampling space with which RBS could be evaluated under a number of scenarios. To achieve a desired precision, RBS sample size and branch diameter exponents were varied, and the RBS method was simulated using both analytical and re-sampling methods. It was found that there is a significant amount of natural variation present when relating the biomass of small components to branch diameter, for example. This finding validates earlier decisions to question the efficacy of RBS for estimating small component biomass in eucalypt species. In addition, significant improvements can be made to increase the precision of RBS by increasing the number of samples taken, but more importantly by varying the exponent used for constructing selection probabilities. To further evaluate RBS on trees with differing growth forms from that enumerated, virtual trees were generated. These virtual trees were created using L-systems algebra. Decision rules for creating trees were based on easily measurable characteristics that influence a tree's growth and form. These characteristics included; child-to-child and children-to-parent branch diameter relationships, branch length and branch taper. They were modelled using probability distributions of best fit. By varying the size of a tree and/or the variation in the model describing tree characteristics; it was possible to simulate the natural variation between trees of similar size and fonn. By creating visualisations of these trees, it is possible to determine using visual means whether RBS could be effectively applied to particular trees or tree species. Simulation also aided in identifying which characteristics most influenced the precision of RBS, namely, branch length and branch taper. After evaluation of RBS/IS for estimating the component biomass of a single tree, methods for estimating the component biomass of a stand of trees (or plot) were developed and evaluated. A sampling scheme was developed which incorporated both model-based and design-based biomass estimation methods. This scheme clearly illustrated the strong and weak points associated with both approaches for estimating plot biomass. Using ratio sampling was more efficient than using RBS/IS in the field, especially for larger tree components. Probability proportional to size sampling (PPS) -size being the trunk diameter at breast height - generated estimates of component plot biomass that were comparable to those generated using model-based approaches. The research did, however, indicate that PPS is more precise than the use of regression prediction ( allometric) equations for estimating larger components such as trunk or total biomass, and the precision increases in areas of greater biomass. Using more reliable auxiliary information for identifying suitable strata would reduce the amount of within plot variation, thereby increasing precision. PPS had the added advantage of being unbiased and unhindered by numerous assumptions applicable to the population of interest, the case with a model-based approach. The application of allometric equations in predicting the component biomass of tree species other than that for which the allometric was developed is problematic. Differences in wood density need to be taken into account as well as differences in growth form and within species variability, as outlined in virtual tree simulations. However, the development and application of allometric prediction equations in local species-specific contexts is more desirable than PPS