Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 136

planning. However, new methods have been designed to use FAP in operational
sampling projects (Keane and Gray 2013 ).
The FAP method may be a preferable and more appropriate method for obtaining
accurate fuel loading estimates for many surface fuel components. Most impor-
tantly, FAP techniques tend to give a better representation of the actual variation
observed in the field for surface fuel components (Keane and Gray 2013 ). FAP sizes
and number can be adjusted to reduce sampling times but may result in reduced
precision of fuel loading estimates. FAP size can also be adjusted to account for
the spatial scaling of loading by fuel size. Larger fuels (CWD), for example, can
be sampled with larger plots to fully account for spatial distributions in sample es-
timates. Moreover, FAP sampling is easily adapted or merged with other protocols
that are commonly used to sample other fuel components or other ecosystem at-
tributes. Microplots used to sample FWD, for example, can also be used to sample
tree seedling densities, herbaceous biomass, and duff depths. Large macroplots
(~400 m 2 ) can be used to sample both logs to compute CWD loading and trees to
compute canopy fuels. Sampling times can be shortened by employing an easier
technique for estimating loading; the photoload method, for example, can be used
to estimate loadings rather than actually measuring particle dimensions or destruc-
tively collecting fuels for weighing. Or, PI techniques can be used to estimate FWD
on transects within a macroplot. And last, it may be more practical to sample fuels
using FAP methods because many fuel components can be linked together in the
same sampling unit.
The main limitation of the FAP sampling method is that there has yet to be a set
of standardized operational FAP protocols for surface fuel sampling. Many fuel
professionals are unfamiliar with the FAP technique and do not have the knowledge
and expertise to create their own FAP methods. Moreover, FAP estimates tend to be
unbiased but imprecise; variabilities of the loading estimates are often quite high.
While sampled high variability often reflects realistic field conditions, it makes the
detection of change across time or difference between areas using statistical tech-
niques difficult, especially when compared against PI methods. Another limitation
is that many fuel particles may extend to outside microplot boundaries making it
difficult to cut particles without disrupting other fuel particles. And, unlike PI, it
may take a high number of FAP microplots to obtain a reliable measure of loading
(Keane and Gray 2013 ).

8.3.2.3 Distance Sampling

Another new method is perpendicular distance sampling (PDS) which samples logs
using probability proportional to volume concepts (Ducey et al. 2013 ; Gove et al.
2012 ; Williams and Gove 2003 ). With PDS, the total volume of the logs on a land-
scape can be estimated from counts of logs at various sample points. Loading can
then be estimated by multiplying volume by particle density (kg m −3 ) estimates.
PDS is named because a log is selected to the sample if a line from a sample point
intersects the central axis of the log at a right angle and the length of this line is less