Montane coniferous forests and woodlands in the Front Range of the Colorado Rocky Mountains have been subject to increased wildfire in recent years. The area and intensity of these fires is strongly dependent upon the spatial variability and type of fuels as they are arrayed across the landscape. Considering the size of the patches and the mosaic of fuel materials, high spectral and spatial resolution estimates of vegetation components and fuel types are needed to improve fire risk assessment, especially around the wildland/urban interface. Here we used highly resolved remotely sensed imagery, in combination with several spectral techniques to map major forest components and fuel types in montane coniferous forests in the Colorado Front Range by discriminating the fractional covers of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil at a sub-pixel level. An accuracy assessment based on a dataset including 34 field transects indicated that we could explain fractional cover of 73.5%, 40.3%, and 77.6% for PV, NPV, and soil respectively through the use of hyperspectral indicators. Based on the fractional cover of these components, we were able to assess the spatial patterns of vegetation and fuel characteristics at a landscape scale. Throughout the study areas, PV fractions were dominant (48.7%), followed by NPV (28.8%) and soil (22.5%). However, due to microclimate and disturbances such as fire, insect infestations and forest management practices, the spatial distribution of fractions was highly heterogeneous. There was a high fraction of PV in mature forest and on north-facing slopes, and a high fraction of NPV and bare soil in areas with recent disturbance such as fire or insect infestation. In severely burned areas, bare soil was dominant. Fuel treatments reduced the fraction of PV by 11.7%, and increased fractions of NPV by 7.4% and bare soil by 4.5%. These results suggest that hyperspectral remote sensing can be an excellent indicator of not only fuel fractional cover, but of fuel condition after fire, thereby greatly improving regional fire risk assessment.