Quantification of forest biomass is paramount in understanding biofuel potential for standing forests. In such cases where forestry personnel are not available, biomass estimations from remotely sensed data are common. Methods of biomass estimation and the data they require are reviewed, and this review concludes that for the study site (Heiberg Memorial Forest in Tully, NY), airborne LiDAR data is the most effective sensor option. Following this data choice, a new method of detecting individual tree crowns, crown delineation from hill climbing, object recognition, and treetop identification (CHOT) was devised, using LiDAR data as a sole data source. Biomass estimation was executed using four methods and four estimation schemes. The accuracy of the estimation achieved in this thesis is consistent with published literature, yet affords a greater degree of flexibility and automation through the use of machine learning estimation techniques and the utilization of LiDAR data as the sole input.