For the past several decades, prescribed fire has proven to be a valuable tool for managing federal lands. It is an economical and efficient way to reduce accumulated fuel loads resulting from prolonged policies of suppressing wildfires, Prescribed fire helps to control the spread of some exotic species, increase vegetative diversity, and facilitate the regeneration of native grasses and forage for livestock and wildlife. For at least 45 years, natural resource managers have been employing prescribed fire as a range management tool in grassland/shrub ecosystems. Although generally effective for this application, more precise information is needed for comparing the actual response of the ecological landscape to the initial objectives set out in prescribed fire plans. We propose to apply several newly developed remote sensing techniques to accurately describe the temporal dynamics of vegetation community composition in a grassland/shrub ecosystem following prescribed fire treatments. The proposed work is new and innovative but based on a strong fundamental framework. To characterize the outcome of prescribed fires in more detail beyond vegetation classification, there is a need for more accurate geospatial information on vegetation. We seek to apply a recent advance in remote sensing technology, imaging spectroscopy, to estimate both the biomass and moisture content of vegetation canopies, specifically, grassland and shrub fuel types. We propose to develop this state-of-the-art information as input into fire behavior and fire danger rating models such as BEHAVE, FARSITE, and NFDRS.