Characterization of forest fuels is key to successful implementation of any fire management system. Great strides have been made in the characterization of forest canopy fuels by the use of remote sensing technology. Remote sensing of surface fuels is, however, limited by the physical intervention of the overlaying canopy. This limitation underscores the importance of exploring alternative approaches that relate site environment characteristics to the production and accumulation of understory fuels. This study predicts downed woody fuel loadings based on variables such as topography, fire history, and vegetation type in the forested area of the Rincon Mountains in southern Arizona. We used classification and regression trees (CART) to make these predictions. Results show that fine woody fuel loadings are predicted best by vegetation type and slope. Coarse woody fuels are predicted best by differences in elevation.