Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 141

is easily the most popular for sampling canopy fuels, there are some limitations.
First, there are precious few studies that developed biomass equations for each of
the crown fuel components (burnable canopy fuels; < 3 mm), and most canopy
biomass equations are for mostly western US forest species. Second, many of the
assumptions in the method, such as crown shape and crown fuel distribution, may
not be appropriate for some ecosystems and stand conditions. Third, some tree
lists were created using stand inventory techniques that may be at an inappropri-
ate scale. Plot-less sampling, for example, uses a prism or limiting distance sam-
pling to determine which trees to sample, and these trees are usually above a cer-
tain breakpoint diameter resulting in few of the understory trees being sampled.
The resultant tree list typically underrepresents the understory canopy biomass
important to crown fire transition (Chap. 2). This means that overstory conditions
will be summarized independently of understory conditions at stand level which
ignores the importance of spatial autocorrelation in canopy fuel characteristics at
smaller scales (Keane et al. 2012a ). Sampling trees inside a FAP to create a tree
list, and then using this plot-level tree list to compute canopy fuels provides for a
better representation of canopy fuels than computing canopy fuels from averaged
stand conditions.
The last canopy fuel sampling approach involves using a set of photos to visu-
ally estimate canopy fuel variables ( visual canopy methods ). Scott and Reinhardt
( 2005 ) developed a set of stereo photos of canopies from five western US sites in
four different stand densities and calculated canopy fuel variables from destructive
sampling at each site. These photos can then be compared to canopy conditions ob-
served in the field to estimate the five canopy fuel variables along with other stand
variables (basal area, tree density). Many of the newer photo series publications
mentioned in Sect. 8.3.1.2 now have canopy fuel characteristics as attributes to the
photos that are used to match with field conditions.
Estimating canopy fuel variables using field methods poses a dilemma to the
fuels manager. The coarse resolution of crown fire modeling (Chap. 4) is often at
odds with detailed sampling of canopy fuel characteristics. The coarser methods of
indirect and visual canopy fuel sampling may provide sufficient resolution for the
canopy fuel variables, and the more accurate and precise measurements gathered
from the destructive and allometric methods may not match the coarse resolution
of the canopy biomass in fire models. In fact, Reeves et al. ( 2006 ) created canopy
fuels maps by quantifying canopy fuel variables from the allometric approach using
the FuelCalc model but then had to adjust these precise measurements to use in the
spatial fire prediction packages.

8.5 Challenges

The main challenge in fuel sampling is obtaining precise estimates of loading for
each fuel component given the enormous spatial and structural variability across
the different surface fuel components. With limited resources, it is simply impos-