Compared with other distribution functions, the Weibull distribution has been more widely used in describing diameter distributions because of its fl

Accurate biomass measurements and analyses are critical components in quantifying carbon stocks and sequestration\ud rates, assessing potential impacts due to climate change, locating bio-energy processing plants, and mapping and\ud planning fuel treatments. To this end, biomass equations will remain a key component of future carbon measurements\ud and estimation. As researchers in biomass and carbon estimation, we review the present scenario of aboveground\ud biomass estimation, focusing particularly on estimation using tree-level models and identify some cautionary points that\ud we believe will improve the accuracy of biomass and carbon estimates to meet societal needs. In addition, we discuss\ud the critical challenges in developing or calibrating tree biomass models and opportunities for improved biomass. Some\ud of the opportunities to improve biomass estimate include integration of taper and other attributes and combining\ud different data sources. Biomass estimation is a complex process, when possible, we should make use of already\ud available resources such as wood density and forest inventory databases. Combining different data-sets for model\ud development and using independent data-sets for model verification will offer opportunities to improve biomass\ud estimation. Focus should also be made on belowground biomass estimation to accurately estimate the full forest\ud contribution to carbon sequestration. In addition, we suggest developing comprehensive biomass estimation methods\ud that account for differences in site and stand density and improve forest biomass modeling and validation at a range of\ud spatial scales

Estimating aboveground biomass and its components requires sound statistical formulation and evaluation. Using data collected from 55 destructively sampled trees in different parts of Oregon, we evaluated the performance of three groups of methods to estimate total aboveground biomass and (or) its components based on the bias and root mean squared error (RMSE) that they produced. The first group of methods used an analytical approach to estimate total and component biomass using existing equations and produced biased estimates for our dataset. The second group of methods used a system of equations fitted with seemingly unrelated regression (SUR) and were superior to the first group of methods in terms of bias and RMSE. The third group of methods predicted the proportion of biomass in each component using beta regression, Dirichlet regression, and multinomial log-linear regression. The predicted proportions were then applied to the total aboveground biomass to obtain the amount of biomass in each component. The multinomial log-linear regression approach consistently produced smaller RMSEs compared with both SUR methods. The beta and Dirichlet regressions were superior to both SUR methods except for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) branch biomass, for which the simple SUR method produced smaller RMSE compared with the beta and Dirichlet regressions.