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volume (m
-3
) by bulk density estimates (kg m
-3
) for the sample unit. Bulk densities
for litter, duff, shrub, and herb components can be found in the literature (Brown
1981
; Keane et al.
2012b
) or destructively sampled for a small proportion of the
plots. This is often the only operational method for estimating fuel loadings for
these complex fuel components. Sneeuwjagt (
1973
) used a variation of this ap-
proach when he developed equations that predicted loading from height for both
litter and shrubs. While canopy cover is used extensively in plant ecology studies
(Mueller-Dombois and Ellenberg
1974
), it requires extensive training to visually
estimate cover of multiple, overlapping fuel components to the accuracy and preci-
sion needed for fuel sampling.
8.3.1.3
Imagery Techniques
Imagery techniques involve using advanced statistical analysis to correlate fuel
loadings to the digital signatures in the digital imagery. This imagery is taken in
the field and is different from the airborne or satellite imagery used for fuel map-
ping (Chap. 9). A potentially useful imagery technique is the quantification of fuel
loads using
image processing
techniques or software. Many years ago, Fahnestock
(
1971
) calculated loading for several fuel components using a dot grid projected
on color photographs of a cross-section of bayberry shrub fuel layer. Today, there
are more sophisticated image processing approaches that use computer software.
The stereoscopic vision technique (SVT), for example, involves taking stereoscop-
ic photos of the fuelbed in the field then inputting the digital photos into comput-
er-image recognition software to identify woody fuels and then compute loading
volume (Arcos et al.
1998
; Sandberg et al.
2001
). Others have taken pictures of
the fuelbed and then attempted to quantify loading using advanced image process-
ing techniques (Jin
2004
). Photographic methods are still under development and
there needs to be major gains in image processing to discriminate between fuel
components and compute volumes. Its primary use is in quantifying CWD loading
(Arcos et al.
1998
), but it may find some eventual use for measuring FWD, shrub,
and grass loading.
Another emerging technology is the use of
ground-based LiDAR
to estimate fuel
loads for some fuelbeds (Loudermilk et al.
2009
). Here, a terrestrial scanning Li-
DAR (TSL) unit is mounted on a truck or some other vehicle to obtain scan distanc-
es for ground fuels at subcentimeter scales. The LiDAR signal can then be related
to loading by constructing statistical models where destructively sampled loadings
for various components are correlated to the LiDAR imagery scan distances. It is
sometimes difficult to differentiate between fuel components using TSL in hetero-
geneous fuelbeds, but it is still possible. This technique may only be possible for
research purposes in the near future because the TSL instrument is rather expensive
(> $ 40,000), demands a high level of expertise to use and analyze, and it is also dif-
ficult to transport and use in complex terrain.
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