Uncertainty assessment of large-scale forest growth predictions based on a transition-matrix model in Catalonia

Abstract

When predicting forest growth at a regional or national level, uncertainty arises from the sampling and the prediction model. Using a transition-matrix model, we made predictions for the whole Catalonian forest over an 11-year interval. It turned out that the sampling was the major source of uncertainty and accounted for at least 60 % of the total uncertainty. With the development of new policies to mitigate global warming and to protect biodiversity, there is a growing interest in large-scale forest growth models. Their predictions are affected by many sources of uncertainty such as the sampling error, errors in the estimates of the model parameters, and residual errors. Quantifying the total uncertainty of those predictions helps to evaluate the risk of making a wrong decision. In this paper, we quantified the contribution of the sampling error and the model-related errors to the total uncertainty of predictions from a large-scale growth model in Catalonia. The model was based on a transition-matrix approach and predicted tree frequencies by species group and 5-cm diameter class over an 11-year time step. Using Monte Carlo techniques, we propagated the sampling error and the model-related errors to quantify their contribution to the total uncertainty. The sampling variance accounted for at least 60 % of the total variance in smaller diameter classes, with this percentage increasing up to 90 % in larger diameter classes. Among the few possible options to reduce sampling uncertainty, we suggest improving the variance–covariance estimator of the predictions in order to better account for the multivariate framework and the changing plot size.