Forest inventory estimates of tree volume for large areas are typically calculated by adding the model predictions of volumes for individual trees at the plot level, calculating the mean over plots, and expressing the result on a per unit area basis. The uncertainty in the model predictions is generally ignored, with the result that the precision of the large-area volume estimate is optimistic. The primary study objective was to assess the performance of a Monte Carlo based approach for estimating model prediction error that had been developed for boreal and temperate forest applications when used for a subtropical forest application. Monte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty, the nonlinear nature of the models, and heteroskedasticity. A related objective was to estimate the effects of model prediction uncertainty due to residual and parameter uncertainty on the large-area volume estimates for the Brazilian state of Santa Catarina. The primary conclusions were fourfold. First, the methodological approach worked well. Second, the effects of model residual and parameter uncertainty on large-area estimates of mean volume per unit area were negligible for the models and calibration datasets used for the study. Third, for the models currently in use in Santa Catarina, the effects of model residual and parameter uncertainty may be ignored when calculating large-area estimates of mean volume per unit area. Fourth, differences were negligible between estimates of the mean and standard error obtained using a single, nonspecific volume model and estimates obtained using both forest-type models and species-specific/species-group models.
Parameter Uncertainty and Model Predictions: A Review of Monte Carlo Results
