Estimating forest biomass is an essential aspect of carbon (C) stock estimation and global carbon balance studies. Biomass sampling strategies and estimation techniques were investigated. The first Chapter is a general discussion on biomass modeling and sampling strategies. In the second Chapter, sampling distributions of randomized branch samples were investigated based on the destructive and intensive measurement of slash pine (Pinus elliottii) and red maple (Acer rubrum) trees. In this study, biomass estimates for all possible randomized branch sampling (RBS) paths per tree were determined for each sample tree. We found that sampling distributions are more variable for red maple than for slash pine. When tested with all available trees, we found that RBS alone or RBS in combination with importance sampling (IS) procedure are not suitable for estimating biomass at individual tree level. In the third Chapter, additive systems of individual tree biomass equations were developed for both species. We found that more tree size attributes such as, diameter at breast height (Dbh), tree height (Ht) and diameter at the base of live crown provided better prediction of component biomass than using Dbh only or Dbh and Ht in the model. In the Chapter four, we developed an indirect method, which utilizes inside bark volume predicted from a taper function along with density and specific gravity information and crown component information from an explicit biomass prediction model, to estimate biomass.