The assessment of the uncertainty of updated stand-level inventory data

Abstract

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.

Estimates of stem wood increments in forest resources: comparison of different approaches in forest inventory: consequences for international reporting: case studyof European forests

“…The reliability of statistics will be improved over the next 10-20 years, when remeasurement of permanent plots will be completed in five countries, including the Russian Federation. The results of this study, as well as those from other authors (Kangas et al 2004;Haara and Leskinen 2009;Šmelko et al 2008;Kuliešis et al 2010a), show that amongst the reasons for differences in growing stock are (1) applied models, (2) not updated data and (3) subjectivity of applied methods inventory.…”

Uncertainty in forest simulators and forest planning systems

“…This approach used Bayesian theory and the decision variables were probability distributions instead of point estimates. Yet another approach for evaluating uncertainty in growth predictions is simply to compare predicted growths to observed growths (Välimäki & Kangas 2009, Haara & Leskinen 2009). This type of approach is suitable for the validation and comparison of alternative forest simulators.…”

“…The simulated random error components were added to the increments of H dom and G. This type of growth prediction error simulation was used for all non-seedling stands and for Scots pinedominated seedling stands. The total variation of u, based on results by Haara & Leskinen (2009), was divided into u B and u W by applying the results of Kangas (1999). The value for the correlation coefficient α was calculated also from the models by Haara & Leskinen (2009).…”

“…The total variation of u, based on results by Haara & Leskinen (2009), was divided into u B and u W by applying the results of Kangas (1999). The value for the correlation coefficient α was calculated also from the models by Haara & Leskinen (2009).…”

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

“…Traditionally, canopy layering is assessed by conventional fieldwork in relatively small areas, which is time-consuming and occasionally subjective [20,21]. Advances in Earth observation systems and analysis techniques have greatly improved the ability to characterize canopy structure over large areas in not only the horizontal but also the vertical dimension [22][23][24].…”