Comparing simulation methods for modelling the errors of stand inventory data

Forest management planning requires information about the uncertainty inherent in the available data. Inventory data, including simulated errors, are infrequently utilised in forest planning studies for analysing the effects of uncertainty on planning. Usually the errors in the source material are ignored or not taken into account properly. The aim of this study was to compare different methods for generating errors into the stand-level inventory data and to study the effect of erroneous data on the calculation of specieswise and standwise inventory results. The material of the study consisted of 1842 stands located in northern Finland and 41 stands located in eastern Finland. Stand-level ocular inventory and checking inventory were carried out in all study stands by professional surveyors. In simulation experiments the methods considered for error generation were the 1nn-method, the empirical errors method and the Monte Carlo method with log-normal and multivariate log-normal error distributions. The Monte Carlo method with multivariate error distributions was found to be the most flexible simulation method. This method produced the required variation and relations between the errors of the median basal area tree characteristics. However, if the reference data are extensive the 1nn-method, and in certain conditions also the empirical errors method, offer a useful tool for producing error structures which reflect reality.