An approach to optimizing field data collection in an inventory by compartments

This study presents models for the expected error of the total volume and saw timber volume due to sampling errors of stand measurements. The measurements considered are horizontal point sample plots, stem numbers from circular plots, sample tree heights, sample order statistics (i.e., quantile trees), and sample tree heights from the previous inventory. Different measurement strategies were constructed by systematically varying the numbers of these measurements. A model system developed for this study was used in a data set of 170 stands to predict the total volume and saw timber volume of each stand with each measurement strategy. The errors of these volumes were modeled using stand characteristics and the numbers of measurements as predictors. The most important factors affecting the error in the total volume were the number of horizontal point sample plots and height sample trees. In addition, the number of quantile trees had a strong effect on the error of saw timber volume. The errors were slightly reduced when an old height measurement was used. There were significant interactions between stand characteristics and measurement strategies. Thus, the optimal measurement strategy varies between stands. A demonstration is provided of how constrained optimization can be used to find the optimal strategy for any one stand.