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 height describes the average height of the top of the canopy for a stand. A spatially-explicit map of canopy height supplies information for fire behavior models such as FARSITE (Finney 1998) to determine the starting point for embers, calculate wind reductions, and compute the volume of crown fuels. In FARSITE, canopy characteristics are used to compute shading, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for transitions from a surface to crown fire. Canopy characteristics refer to the tree canopy. Where there are tree canopies, i.e. existing vegetation types that are forest and woodland, LANDFIRE has attributed the grid with canopy characteristics with some exceptions. There will be no 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 as such. This is because LANDFIRE assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model parameters. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. The map of canopy height was generated using a predictive modeling approach to relate satellite imagery and spatially-explicit environmental variables to calculated values of average dominant height from field training sites. We used regression trees (Breiman et al. 1984) to link the calculated reference data to 30-meter Landsat ETM satellite imagery and a series of 30-meter, spatially-explicit gradient layers representing climate, soil, and topography. The models were built using Cubist, a machine-learning algorithm (Quinlan 1993), and applied within an ERDAS Imagine interface. All non-forest values, including non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0. 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.
