Sequential sampling and modelling for mean dominant height estimation

Abstract

A method of checking the validity of model predictions of the mean dominant height {MDH) for a cutting unit using sequential sampling is presented. Inventory data provided by the New South Wales Forestry Commission were used to test the impact of decision criteria and acceptable type I and type 2 error levels on average sample size. Using sequential sampling to check model predictions of cutting unit MDH lowered the standard error of the prediction and reduced the maximum error by over half that of the using model predictions alone. The average sample size for the model with sequential sampling varied with the decision criteria and acceptable error levels. Using sequential sampling to check the validity of model predictions can reduce the number of heights that need to be measured during an inventory without greatly increasing the error in estimating MDH for a cutting unit.

Projecting native forest inventory estimates from public to private tenures

“…the predictions are significantly different or not usefully close to the ESV found at the sample point) would quickly indicate that the public forest model is inadequate and should not be used in privately managed forests. Brack and Marshall ( 1990) concluded that, depending on the error levels acceptable to the user, as few as three sample points could accept or reject a model as useful. If the test proves that the model is inadequate, the proposed approach allows the sample data to be reused to help determine an unbiased estimate of the new population without recourse to the invalidated model.…”