Comparison of uncertainty in per unit area estimates of aboveground biomass for two selected model sets

• Uncertainty of forest biomass depends on measurement, model, and sampling errors.
• We quantify measurement, model, and sampling error for two sets of equations.
• Sampling error for regional biomass equations contributed 93% of the total error.
• Relative errors for the regional and component ratio models were 20.7% and 36.8%.
• Terrestrial lidar scanning reduce uncertainty in above ground biomass estimates.

Uncertainty in above ground forest biomass (AGB) estimates at broad-scale depends primarily on three sources of error that interact and propagate: measurement error, model error, and sampling error. Using Monte Carlo simulations, we compare the total propagated error for two sets of regional-level component equations for lodgepole pine AGB, and for two sets of high-precision instruments by accounting for all three of these sources of error. The two sets of models compared included a set of newly-developed component ratio method (CRM) equations, and a set of component AGB equations currently used by the Forest Inventory and Analysis (FIA) unit of the United States Department of Agriculture (USDA) Forest Service.Relative contributions for measurement, model, and sampling error using the current regional equations were 5%, 2% and 93%, respectively, and 13%, 55% and 32%, respectively using the CRM equations. Relative standard error (RSE) values for the current regional and CRM equations with all three error types accounted for were 20.7% and 36.8%, respectively. Results for the model comparisons indicate that per acre estimates of AGB using the CRM equations are far less precise than those produced with the current set of regional equations. Results for the instrument comparisons indicate the terrestrial lidar scanning reduce uncertainty in broad-scale estimates of AGB attributed to measurement error.