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

By Annika S. Kangas, Matti Maltamo. 2000

58 citations recorded by the source corpus.

Diameter distribution is used in most forest management planning packages for 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 methods. In the fi rst case, the distribution is obtained by predicting the parameters of some probability density function. In a distribution-free percentile method, the diameters at certain percentiles of the distribution are predicted with models. In non-parametric methods, the predicted distribution is a linear combination of similar measured stands. In this study, the percentile based diameter distribution is compared to the results obtained with 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. The comparison was made with respect to the accuracy of predicted stand volume, saw timber volume and number of stems. The predicted percentile and Weibull distributions were calibrated using number of stems measured from the stand. The information of minimum and maximum diameters were also used, for re-scaling the percentile based distribution or for parameter recovery of Weibull 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 of stems as a predictor. Calibration improved the results in most cases. However, using the minimum and maximum diameters for parameter recovery proved to be ineffi cient.

Subfields: Environmental Engineering, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/499d4d78f97163702a24c2842421b19a9eb51f87

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Citations in the corpus

Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data (2007, methodology): “Different distribution functions have been proposed to model empirical diameter distributions, where the Weibull is the most frequently used (e.g. Bailey & Dell, 1973; Kangas & Maltamo, 2000b).”
Percentile based basal area diameter distribution models for Scots pine, Norway spruce and birch species. (2000, methodology): “Test results in independent data sets has been presented in the studies by Maltamo and Kangas (1998), Siipilehto (1999) and Kangas and Maltamo (2000b).”
Non-parametric prediction of diameter distributions using airborne laser scanner data (2009, background): “…accuracy of derived forest attributes (diameter distribution, total volume, sawlog volume) is usually excellent owing to the use of exactly known stand attributes as predictors (Kangas & Maltamo, 2000), but in the application phase the accuracy is considerably worse (e.g. Haara & Korhonen, 2004).”
Characterisation of diameter distribution using the Weibull function: method of moments (2006, methodology): “The method of moments is used infrequently and often only when the intention is to compare the applicability of selected analytical methods for the estimation of Weibull-parameters (Kangas and Maltamo 2000; Maltamo et al. 2000; Shiver 1988; Nanang 1998; Ueno and Ôsawa 1987; Zarnoch and Dell 1985; Sh”
Parameter recovery vs. parameter prediction for the Weibull distribution validated for Scots pine stands in Finland (2013, methodology): “This has been performed mostly to improve the accuracy in stem volume and assortments (see Kangas and Maltamo 2000a; Kangas and Maltamo 2002; Mabvirura et al. 2002).”