Estimating annual bole biomass production using uncertainty analysis

Abstract

Two common sampling methodologies coupled with a simple statistical model were evaluated to determine the accuracy and precision of annual bole biomass production (BBP) and inter-annual variability estimates using this type of approach. We performed an uncertainty analysis using Monte Carlo methods in conjunction with radial growth core data from trees in three Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco)-dominated sites (young, mature, and old-growth) in the western Cascades of Oregon. A model based on the mean and standard deviation of annual radial growth from sampled trees was used with and without stratification by tree size to predict radial growth for nonsampled trees. Sample sizes of 64 to 128 trees per stand were required to achieve accuracy and precision within ±10 percent. Without stratification, the model underestimated annual BBP (Mg ha^-1 year^-1) in all three age classes by up to 28 percent, and interannual variability by as much as 26 percent. Applying stratification increased accuracy of estimates at least twofold, and precision of estimates improved by 3 to 10 percent, resulting in decreased sample size requirements. The coefficient of variation of error of estimates was half that of interannual variability over the study period. Thus, this approach can be used to examine patterns of interannual variability of BBP in response to changing climate and land use patterns.

Citation

Woolley, Travis J.; Harmon, Mark E.; O'Connell, Kari B. 2007. Estimating annual bole biomass production using uncertainty analysis. Forest Ecology and Management. 253: 202-210.