Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 130

often overwhelms differences across broad vegetation types (Keane et al. 2012b ).
Disturbance history (e.g., time since disturbance, severity) is more important than
vegetation to predict fuel loadings (Brown and Bevins 1986 ), but few studies have
explored this relationship. Some have found that a variation of this vegetation as-
sociative technique is useful to create predictive loading equations from measured
vegetation characteristics using statistical methods (Catchpole and Wheeler 1992 ).
Fuel loadings can be correlated to various stand-related characteristics, such as
basal area, Leaf Area Index (LAI), and stand height. Keane et al. ( 2012b ) found
that stand attributes were poorly correlated to surface fuel loadings but were highly
correlated to canopy fuel variables.
Another associative method is using mapped loading values from readily avail-
able digital geospatial products as fuel loading estimates (Chap. 9). The LAND-
FIRE National Project, for example, mapped four fuel classifications across the
United States using satellite imagery (Reeves et al. 2006 ) and many have used the
loading values from these classifications to quantify loadings for a specific project
area. However, Keane et al. ( 2013 ) found low accuracies for fuel loadings from
LANDFIRE fuel maps. Therefore, this practice, while inexpensive, quick, and easy,
is not recommended for fine scale, project-level applications until existing fuel
maps are much improved. Locally created fuel maps may have sufficient quality,
but regional and national maps should only be used for fuel analyses at broad scales,
not at the project level. Depending on the resolution, fuel maps could still be useful
for stratifying the sample area (or target population) into more homogeneous sub-
units to make sampling efforts more efficient.

8.3.1.2 Visual Techniques

Visual techniques involve assessing the loading of fuel components from ocular
estimates. Some fuel specialists feel they can accurately estimate loadings by eye
without any guides or references. This level of resolution and accuracy may be ac-
ceptable for some fuel applications, such as describing fuels to other professionals.
However, it is rare that anyone can accurately and consistently estimate the loadings
of all fuel components by eye, especially for FWD, duff, and litter. One reason for
this is that fuel loadings have high spatial variability over small areas (Chap. 6) and
the ocular estimate is often biased toward smaller portions of the project area; it is
difficult to evaluate a large, heterogeneous area to obtain a truly integrated visual
fuel loading estimate (Sikkink and Keane 2008 ). While visual estimation by eye is
preferable to some of the associative methods presented above providing there is a
high expertise and confidence in the sampler, it is rare that a person can accurately
estimate fuel component loadings across diverse fuelbeds at the same detail. There-
fore, many have resorted to using pictures as guides and references for comparing
loading estimations.
Perhaps the most popular comparative visual technique is the photo series,
which is both a classification (Chap. 7) and a fuel assessment technique. Using
photo series sampling methods, surface fuel loadings are ocularly estimated using
a set of photos that present stand conditions for various vegetation types and site