Performance of percentile based diameter distribution prediction and Weibull method in independent data sets

Diameter distribution is used in most forest management planning packages f or predicting stand volume, timber volume and stand growth. The prediction of diameter distribution can be based on parametric distribution functions, distribution-free parametric prediction methods or purely non-parametric m ethods. In the first case, the distribution is obtained by predicting the p arameters of some probability density function. In a distribution-free perc entile method, the diameters at certain percentiles of the distribution are predicted with models. In non-parametric methods, the predicted distributi on is a linear combination of similar measured stands. In this study, the p ercentile based diameter distribution is compared to the results obtained w ith the Weibull method in four independent data sets. In the case of Scots pine, the other methods are also compared to k-nearest neighbour method. Th e comparison was made with respect to the accuracy of predicted stand volum e, saw timber volume and number of stems. The predicted percentile and Weib ull distributions were calibrated using number of stems measured from the s tand. The information of minimum and maximum diameters were also used, for re-scaling the percentile based distribution or for parameter recovery of W eibull parameters. The accuracy of the predicted stand characteristics were also compared for calibrated distributions. The most reliable results were obtained using the percentile method with the model set including number o f stems as a predictor. Calibration improved the results in most cases. How ever, using the minimum and maximum diameters for parameter recovery proved to be inefficient.