Shrubs are becoming more abundant across the northern landscapes due to climate change. Shrub growth rate and range of advancement have important ecological implications throughout Alaska. Shrub biomass has not been previously quantified on a regional scale. In this study we used field sampling measurements, allometric equations, remote sensing interrogation, and statistical modeling to 1) quantify tall-shrub biomass using field sampling measurements, and allometric equations, 2) model tall-shrub presence and biomass using point classification and remote sensing interrogation, 3) compare the accuracy of predictive models against observed data, and 4) predict biomass estimates and shrub probability in selected areas of southcentral Alaska. Biomass models were adjusted with shrub probability models to account for tree presence as they were found to overestimate biomass in areas of high tree cover despite a good fit. Shrub probability models were constructed with both a GAM and a GLM. The GAM is the model of choice due to its flexibility and conservative estimates when compared to the GLM. Tile biomass estimations seem reasonable; however, they will need to be validated with field observations. As tall-shrubs continue to advance with climate change into higher elevations, the density of the shrubs will continue to increase over the landscape increasing the need for wide-scale biomass estimates.