%0 Research article
%T The assessment of the uncertainty of updated stand-level inventory data
%A Haara, Arto
%A Leskinen, Pekka
%D 2009
%J Silva Fennica
%V 43
%N 1
%R doi:10.14214/sf.219
%U https://silvafennica.fi/article/219
%X Predictions of growth and yield are essential in forest management  planning. Growth predictions are usually obtained by applying complex  simulation systems, whose accuracy is difficult to assess. Moreover, the  computerised updating of old inventory data is increasing in the  management of forest planning systems. A common characteristic of  prediction models is that the uncertainties involved are usually not  considered in the decision-making process. In this paper, two methods  for assessing the uncertainty of updated forest inventory data were  studied. The considered methods were (i) the models of observed errors  and (ii) the k-nearest neighbour method. The derived assessments of  uncertainty were compared with the empirical estimates of uncertainty.  The practical utilisation of both methods was considered as well. The  uncertainty assessments of updated stand-level inventory data using both  methods were found to be feasible. The main advantages of the two  studied methods include that bias as well as accuracy can be assessed.