Structure-based nonparametric target definition and assessment procedures with an application to riparian forest management

Forest policy makers increasingly desire the use of quantitative descriptions to define desirable forest characteristics as a target for forest management. A framework for quantitative, multivariate target definition and assessment is described. The framework uses the joint distribution of multiple forest structure attributes to describe a set of desired forest structures and to identify a target region. The target region contains the most likely attribute values and its extent is controlled by choosing a probability of acceptance or acceptance level.Nonparametric procedures implementing the target definition and assessment framework have been developed and are described. The implemented procedures were used with a real data set representing 129 riparian stands in western Washington State, U.S.A. to define a three-dimensional target for riparian forest management in the region using stand density, quadratic mean diameter, and average tree height.A bootstrap simulation and a 50–50 split representative sample were used to evaluate the consistency of the implemented procedures by testing the null hypothesis that attribute value distributions for a target data set and an observation data set, both randomly drawn from a common distribution, were statistically indistinguishable. Chi-squared goodness of fit tests with α = 0.05 were used to compare observed mean acceptance percentages from the bootstrap simulation and observed acceptance percentages from the 50–50 split representative sample to the targeted acceptance levels of 95%, 90%, 80%, and 50%. Evaluation results indicated that the target definition and assessment procedures were consistent by failing to reject the null hypothesis for each evaluation method, with p-values of p = 0.963 for the bootstrap simulation and p = 0.866 for the 50–50 split representative sample.