The problem of estimating stand tables in stands with few sample points is considered. The usual point-sampling estimate of trees per hectare by diameter class is examined, along with two alternative estimators: A precision-weighted composite estimator and a pseudo-Bayes estimator. A large-scale forest inventory is simulated, and stand tables are estimated for each stand with each of the three estimators. Both the composite and pseudo-Bayes estimator appear superior (in terms of mean absolute error and mean squared error) to the usual estimator. The pseudo-Bayes estimator appears to perform the best (with an 80% reduction in mean squared error). This estimator also is easier to use than the composite estimator because it does not require within diameter class variance estimates.
All Science Journal Classification (ASJC) codes
- Global and Planetary Change