Prediction of standing tree defect proportion using logistic regression and ordered decision thresholds

Abstract

In forest inventories, it is often of interest to calculate amounts of usable wood volume in trees. This usually requires knowledge of how much of the total volume is unusable (cull) due to form or decay deficiencies. This information is primarily obtained when collecting data on sample plots, although the assessments are often difficult and subjective. To provide an alternative, methods were developed to estimate individual-tree cull attributes. The procedure initially involves classification using logistic regression to assign trees to one of three categories (no cull, intermediate cull, entirely cull) based on probability cut points. Subsequently, trees classified as having intermediate cull are assigned a cull amount predicted from a generalized linear regression model. The best results for cull prediction were obtained using cut points that minimized absolute prediction error; however, better prediction of net cubic volume of trees was realized when the errors were weighted by tree size. The model-based approach may be particularly useful in obtaining temporal consistency, such that trend estimates may better reflect the actual change in forest resources.