Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 140

not containing canopy biomass), LAI, and canopy cover, and these simple mea-
surements are then correlated to canopy fuel variables. Keane et al. ( 2005 ), for ex-
ample, measured gap fraction using five different instruments for the destructively
sampled plots of Reinhardt et al. ( 2006b ) and then developed statistical models that
predicted CBD from gap fraction for each instrument. These predictive relation-
ships were then used by Poulos et al. ( 2007 ) to estimate CBD for stands in Texas,
USA. Another alternative to this approach is using simple stand-level measure-
ments to estimate canopy fuel variables. Alexander and Cruz ( 2014 ) created tables
that show values for the five canopy fuel variables for various stand basal area and
tree density classes for four western US forest types. These indirect techniques are
relatively quick and cheap, but the instruments may be expensive, especially a ter-
restrial scanning LiDAR. However, the statistical models that predict canopy fuel
characteristics from indirect measurement are generally not robust and are most
accurate for stand types that are similar to the ones destructively sampled (Keane
et al. 2005 ).
The most commonly used canopy sampling approach is estimating the canopy
fuel variables using stand inventory data and modeling canopy biomass using al-
lometric relationships ( allometric methods ). This technique uses an inventory of
trees in a stand to compute the five canopy fuels variables. The inventory is often
represented by a “tree list,” which is a list of tree cohorts in the stand on a per area
basis. Six attributes are usually measured for each tree cohort in most stand inven-
tory protocols: tree density (trees per unit area), species, condition (live vs. dead),
diameter breast height (DBH), height, and height to live crown base. There can be
any number of tree cohorts in the tree list. The tree list is then used to compute the
amount of canopy material in vertical canopy layers of specified thicknesses. This
is often done by first computing burnable canopy biomass from the empirically de-
rived allometric biomass equations for each crown fuel component (Brown 1978 ).
This burnable canopy biomass is then distributed across the vertical crown length
for each tree by assuming a crown shape and then using tree height and live crown
base height to allocate biomass into each layer based on geometric analysis. The
biomass is then summed across all trees for each layer and this sum is then divided
by the volume of that layer (plot area multiplied by layer thickness) to calculate
CBD . CBH and CH are calculated as the layer height at which the CBD exceeds
or goes below a threshold value (Chap. 4). CFL is simply the sum of all burnable
biomass over all layers divided by plot area. This technique is programmed into a
computer application called FuelCalc (Reinhardt et al. 2006a ).
One great advantage of the allometric method is that it can be used with any of
the diverse stand inventories commonly conducted by natural resource manage-
ment agencies. Moreover, the sampling techniques and methods for measuring
trees using timber inventory techniques are widely known and many field crews
are familiar with the protocols so training may be minimal. There are also many
databases that contain tree lists that can be used to quantify canopy fuels char-
acteristics; the US Forest Service's Forest Inventory and Analysis program has
tree lists for thousands of plots across the USA. However, while this technique
has been used for many fuel projects (Keane et al. 2006 ; Reeves et al. 2006 ) and