Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 142

sible to sample to the same level of precision for all fuel components, and for some
fuel components, it is incredibly difficult to obtain a precise estimate of loading
without extensive sampling. The problem is that fuel component properties have
unique spatial distributions that dictates the size and shape of the sampling unit.
Small woody fuels (FWD) vary at scales that are much smaller than logs (CWD)
(Keane et al. 2012a ). Therefore, sampling designs must accommodate these spatial
distributions along with the properties of the fuel component by using hierarchi-
cally nested sampling unit designs (e.g., nanoplots nested within microplots nested
within macroplots).
Some fuel types are often ignored in most sampling projects for logistical, cost,
and time reasons. Conifer seedling loading, for example, may comprise a signifi-
cant portion of the fuelbed and contribute to fire ignition and spread (Fig. 3.4), yet
few sampling designs include effective methods for sampling seedlings (Riccardi
et al. 2007 ). Squirrel middens, animal scat, and pollen cones (Chap. 3) are other
examples of fuel types that have few sampling methods and are rarely tied to fuel
components (Ottmar et al. 2007 ).
Woody fuel loading should be stratified in statistically and ecologically appropri-
ate size classes that still provide value in predicting fire behavior. Keane and Gray
( 2013 ) found the highest sampling uncertainty occurred when FWD were strati-
fied by the conventional, nonuniform time-lag moisture sizes (e.g., 1, 10, 100, and
1000 h) rather than actually measuring particle diameters or measuring diameters to
1 cm size classes. As mentioned in Chap 3, the unbalanced Fosberg et al. ( 1970 ) size
classes that get wider with larger particle diameters ignore subtle but important dif-
ferences between species, degree of rot, and stand structure. Moreover, aggregating
loadings of all log sizes into one class makes accurate decomposition predictions
nearly impossible because of the great ecological importance of log size in various
ecosystem processes (Harmon et al. 1986 ).
Four major biological factors are responsible for high levels of uncertainty in
most sampling methods. First, wood density is highly variable both within and
across the fuel particles, so the assumption of a constant density across all particles
may be flawed. An assessment of density during sampling might improve loading
estimates, but it would be difficult at this time to expect sampling crews to estimate
particle density because there isn't any technology or standardized method as yet.
Keane et al. ( 2012b ) found high variability in wood density within a woody fuel
component and an even higher variability within a sample site. Some woody fuel
sampling protocols use the decay classes of Maser et al. ( 1979 ) to key to different
wood densities (Lutes et al. 2009a ; Lutes et al. 2006 ), but rarely are estimates of
wood density actually measured in the field along with loading and rarely is the
Maser et al. ( 1979 ) key applied to FWD. Second, woody fuel particles are not cyl-
inders, but rather complicated volumes of highly variable cross-sections and con-
torted lengths (Chap. 3). Therefore, assumptions that woody fuel particle shapes
can be approximated by frustums or cylinders using diameters and lengths may be
oversimplified and techniques for measuring fuel diameters using rulers and gauges