LANDFIRE - Forest Canopy Bulk Density (LANDFIRE.US_130CBD)
Dates
Publication Date
2013-03-31
Citation
Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey, 20130331, LANDFIRE.US_130CBD: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey: Sioux Falls, SD, http://www.landfire.gov, http://landfire.cr.usgs.gov/viewer/.
Summary
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 that would burn in a crown fire (Van Wagner 1977; Scott and Reinhardt 2001; Keane et al. 2005). A spatially explicit map of canopy [...]
Summary
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 that would burn in a crown fire (Van Wagner 1977; Scott and Reinhardt 2001; Keane et al. 2005). 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. The CBD mapping process began by deriving field referenced estimates of canopy characteristics through LFRDB plot analysis. Approximately 45,000 plots were acquired throughout the US for estimating CBD. Utilizing these plots, field referenced CBD values were computed for each plot using the canopy fuel estimation software FuelCalc (Reinhardt et al. 2006b). Go to http://www.landfire.gov/participate_acknowledgements.php for more information regarding contributors of field plot data. (Some tree species had no crown biomass equation. In this situation, a published equation for a species with a similar genus was used as a substitute. Not all species were used for computing plot-level CBD. For example, all Acer and Populus spp. were excluded from the canopy fuel profile as these and other broadleaved species are considered relatively inflammable and therefore unavailable.) In an effort to model the relationship between these stand and canopy characteristics the outputs from the FuelCalc computations were analyzed using a gamma log-link generalized linear model (GLM) (McCullagh and Nelder 1983). From this analysis plot level CBD estimates related to canopy cover (CC), stand height (CH) and membership in a pinyon-juniper EVT allowing for CBD to be modeled. (Further explanation and discussion of this GLM can be found in Reeves et al, 2009). The resultant GLM was applied spatially across a mapping zone through the use of the LANDFIRE Fuels Change Mapping Tool, or ToFuDelta, to provide a mapped estimate of CBD. These preliminary CBD data products were finalized after applying a series of post-processing techniques and logic checks ensuring that the canopy fuel products were relevant in the context of the other fuel layers and fire behavior predictions. -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. - Certain types of agriculture that are deemed burnable get a value added by ToFuDelta based on region and type of vegetation. 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. LANDFIRE 2012 (lf_1.3.0) and used LANDFIRE 2010 (lf_1.2.0) data as a launching point to incorporate disturbance and its severity, both managed and natural, which occurred on the landscape after 2010. Specific examples of disturbance are: fire, vegetation management, weather, and insect and disease. Disturbance data used in the updating is the result of several efforts that include data derived in part from remotely sensed land change methods, Monitoring Trends in Burn Severity (MTBS), and the LANDFIRE events data call. Vegetation growth was modeled where disturbance occured.
LANDFIRE data products are designed to facilitate national- and regional-level strategic planning and reporting of management activities. Data products are created at a 30-meter grid spatial resolution raster data set; however, the applicability of data products varies by location and specific use. Principal purposes of the data products include providing, 1) national-level, landscape-scale geospatial products to support fire and fuels management planning, and, 2) consistent fuels data to support fire planning, analysis, and budgeting to evaluate fire management alternatives. Users are advised to evaluate the data carefully for their applications.
