Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 132

photoload methods for measuring duff and litter loading. The photoload technique
differs from photo series in that assessments are made by comparing field fuel con-
ditions to smaller-scale downward-pointing photographs of graduated fuel loadings.
Photoload methods are much faster and easier than more complicated techniques
(Sect. 8.3.2) with comparable accuracies (Sikkink and Keane 2008 ), and they can
be used in multistage sampling strategies where a fraction of the total plots are also
destructively sampled and correlated to photoload samples to develop a means for
correcting all photoload estimates (Keane et al. 2012b ). However, Keane and Gray
( 2013 ) found the photoload technique requires extensive training to be used ef-
fectively; inexperienced users often could not consistently and accurately estimate
high fuel loads.
Fuel classifications can also be used as an inventory and monitoring method
(Chap. 7). In this technique, a fuel classification class is visually identified in the
field, and the loadings assigned for that class are used as the sampled loadings.
Those fuel classifications that use vegetation to classify fuelbeds are probably the
most suspect, while classifications that contain dichotomous keys for identifying
classes based on fuelbed properties, such as the Fuel Loading Model (FLM) clas-
sification (Lutes et al. 2009b ), are best for fuel assessment because they can be used
in the field by inexperienced crews to estimate fuel loadings with moderate accura-
cies (Keane et al. 2013 ).
Another effective visual fuel sampling method uses fuel hazard assessments
across different fuel strata to obtain loading estimates for various components in
the fuelbed. Originally developed by Gould et al. ( 2008 ) for Australia, this method
involves making hazard assessments for the overstory and intermediate canopy lay-
ers, and then elevated, high, and low surface fuel layers. Each layer is given a score
from 0 to 4 based on a variety of fuelbed attributes including percent canopy cover,
presence of stringy bark, and suspended dead material. These scores are then sum-
marized and the summaries are correlated to actual fuel loadings using statistical
techniques (Gould et al. 2011 ). This rapid technique produced moderately accurate
loadings with minimal training. Techniques that successfully link visually distinc-
tive signatures, such as canopy cover, with fuel component loadings might be effec-
tive for operational fuel sampling in the future because it balances and integrates the
elements of hazard assessment into the sampling design.
One last visual technique involves using cover-volume methods to calculate
loadings from visually estimated canopy cover and height. In this technique, can-
opy cover is estimated by eye for those components with small and variable fuel
particles that are grouped together into one component, such as shrubs, herbs, and
trees, and an estimate of measured or ocularly estimated height is also made in a
fixed-area sample unit for those components. Some fuel sampling packages, such
as FIREMON (Lutes et al. 2006 ), describe how to estimate canopy cover in 10 %
classes (e.g., 1-5, 5-15, 15-25 %, and so on) and how to visually estimate height.
Volumes of the assessed components (volume includes air pockets) are then cal-
culated by multiplying the proportion cover (percentage cover divided by 100) by
height (m) and sampling area (m 2 ). Fuel loadings are then estimated by multiplying