The use of fire simulation models for hazard assessment in topographically diverse and fire sensitive regions such as Colorado's Front Range has been used effectively. The Hayman Fire, which burned over an area of more than 138,000 acres, highlighted the need for accurate hazard assessment in such areas. In this study, a new methodology is implemented, which addresses the issues of differential illumination and delineation of spectrally similar fuels categories as they relate to generating the critical fuels data layer necessary for effective fire simulations.;The methods are designed to be streamlined, resource efficient, and robust. Although fuels data are already being generated by the Forest Service for much of the United States, the resources employed to do so are not representative of those of many individuals and organizations who may have a desire to quantify the fire hazard present in their area. This research develops and tests a methodology which is capable of generating similarly effective fuels data in a much more streamlined and less resource intensive way. The result is a functionally similar fuels layer to that developed by the Forest Service. A statistical comparison of the two layers shows a 76% accuracy rate in fuel model delineation.