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    Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees. However, the uncertainty in the model predictions is generally ignored with the result that the precision of the large area volume estimates is overestimated. The primary study objective was to estimate the effects of model residual variability and model parameter uncertainty on large area volume estimates and their uncertainties for a study area in northeastern Minnesota, USA. Monte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty and the nonlinear nature of the models. Two conclusions were important. First, for this study, the effects of uncertainty in model predictions on the large area volume estimates and their uncertainties were small when the models were calibrated using an average of 100 or more observations per species and when the average proportion of variance explained by the models was at least 0.95. Second, large area estimates and their uncertainties based on coniferous/deciduous and nonspecific models deviated very little from large area estimates based on species-specific models.

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    McRoberts, Ronald E.; Westfall, James A. 2014. Effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science. 60(1): 34-42.


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    allometric model, model parameter uncertainty, residual uncertainty, Monte Carlo simulation

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