U.S. Joint Fire Science Program


Date of this Version


Document Type



Fire Science Brief, Issue 141, September 2011


US government work.


Managers of most coniferous forests in the western United States aim to create and maintain forest structures that are less susceptible to the initiation and spread of crown fire. To achieve this end, they use models that predict potential fire behavior, and these models rely on accurate estimates of canopy structure, including canopy base height (CBH) and canopy bulk density (CBD). Managers predict CBD through use of the Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS). However, the equations used by FFE-FVS to predict crown mass are based on estimates solely from northern Montana and Idaho, and therefore likely do not capture the variability in crown mass between different geographic regions. Moreover, an underlying assumption critical to the prediction of CBD in FFE-FVS is that crown biomass is distributed uniformly along the entire length of a tree’s crown; however, in actuality, CBD is likely to be low at the top and bottom and highest somewhere in the middle, and the place where CBD is the highest is important because this is the place where crown fire would most likely spread. Thus, the procedure currently implemented in FFE-FVS can underestimate CBD because of (1) use of crown mass equations outside their appropriate geographic range and (2) the assumption of a uniform distribution of crown mass within the live crown. This project focused on ponderosa pine forests in the Black Hills National Forest (BHNF) of South Dakota and tested the effects of modifi ed estimators of canopy fuel and canopy fuel distribution on the determination of canopy bulk density, canopy base height, and potential crown fire behavior as compared to the current methods of prediction in FFE-FVS. The project team developed improved methods—that can be applied throughout the United States—for estimating both the amount and vertical distribution of canopy fuels from forest inventory data that can be integrated into FFE-FVS.