Localizing a predicted diameter distribution using sample information

Abstract

This study presents a new method for predicting the diameter distribution of a stand, utilizing the percentile-based diameter distribution. The expected diameter percentiles are first predicted using stand measurements. Subsequently, the distribution is calibrated (localized) for the stand using sample order statistics, which consist of one or more diameters of sample trees and their ranks on the sample plot(s). These measurements can be quickly carried out in the field, because the rank can be assessed visually. The sample order statistics can be interpreted as measured sample percentiles. The expectations, variances, and covariances of the measured sample order statistics are derived using the theory of order statistics. Regression models are used to predict the conditional expectations of predefined percentiles, which are then combined with the measured percentiles using the best linear unbiased predictor. The method was tested in a real data set using simulated sample plots. The test showed that, even with a small number of sample measurements, the relative root mean square error and bias of volume per hectare could be decreased remarkably. Furthermore, increasing the number of measurements steadily improved the prediction. The proposed method seems to be a promising tool in the prediction of diameter distributions of various forms, including those found in complex stands. Copyright © 2005 by the Society of American Foresters.