The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy bulk density (CBD) is defined as the mass of available canopy fuel per unit canopy volume (Scott and Reinhardt 2001). A spatially explicit map of canopy bulk density supplies information used in fire behavior models such as FARSITE (Finney 1998) to determine the spread characteristics of crown fires across the landscape. It should be noted that LANDFIRE layers will not include canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen to reflect these conditions. This is because LANDFIRE assumes that the potential burnable biomass in the shorter tree canopies has been accounted for in the surface fuel model parameters. For example, maps of areas dominated by young or short conifer stands where the trees are represented by a shrub type fuel model will not include canopy characteristics. The map of canopy bulk density was generated using a predictive modeling approach to relate satellite imagery and spatially explicit environmental variables to CBD values calculated from field plots. CBD was calculated using a program developed by E. Reinhardt at the Missoula Fire Sciences Laboratory. Regression trees were used to link the calculated reference CBD to 30-meter Landsat satellite imagery and a series of 30-meter spatially-explicit gradient layers representing climate, soil, topography, and biophysical phenomena, such as net and gross primary productivity. The models were built using the commercially available regression tree machine-learning algorithm Cubist (Quinlan 1993; Rulequest Research 2006). These models are spatially applied in the ERDAS Imagine image processing system. CBD values above 0.4 kg m-3 cannot be predicted with a reasonable level of accuracy; therefore, the CBD data represented in this layer are continuous from 0 to 0.40 kg m-3 (0.025 lb ft-3 ) (to the nearest 0.01 kg m-3), whereas all values > 0.4 kg m-3 are binned into a single thematic class. This single thematic class is represented on the map as 45 (0.45 kg m-3 * 100). The rationale for this decision is that there are few stands with CBD > 0.4 kg m-3, and it is therefore difficult to predict such high values with a small sample size. Users should realize that the data are not accurate to the level of precision indicated on the layer. For example, by mapping CBD continuously to the nearest 0.01 kg m-3, it is not implied that the data can be mapped to that level of accuracy. Indeed, this level of mapping is false precision, but users can bin the data according to their specific needs. It is therefore the user's responsibility to use the data appropriately. All non - forest values, including herbaceous and most shrub systems and non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0. Some stands dominated by broadleaf species which typically do not permit initiation of crown fire (e.g. Populus spp.) are coded with a CBD of 0.01 kg m-3. Since crown fire is rarely observed in most hardwood stands, the lowest CBD value possible was used to prevent false simulation of crown fire in these areas. The time period for this data set is not applicable. In other words, it is not possible to characterize this data set with a single date, nor is it logical to use a range.