Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 124

as is the lack of sampling equipment required for fuel measurement, including the
critical gear that ensures crew safety such as radios, first aid kits, and cell phones.
Expertise of the field crew and the people that will ultimately analyze and inter-
pret the data is important in fuel sampling. Inexperienced field crews will require
intensive training that may reduce the time available for sampling. And similarly,
inexperienced analysts may produce inappropriate statistical summaries and come
to the wrong conclusions, while inexperienced managers may use completed analy-
sis results in inappropriate contexts that don't fit sampling objectives or sampling
designs. Fuel sampling personnel may be highly experienced, who can easily adapt
to any challenge in the field without significant changes in productivity and quality,
to novice student summer temporary hires, who have difficulty navigating in the
field let alone accurately measure fuel characteristics. Effective training is the only
remedy for inexperienced sampling crews.
The last important factor is the level of statistical rigor demanded by the sam-
pling project, which should always be determined in the context of the sampling
objective. One of the most important parameters in the sample design is the number
of sample units ( n ) to establish in the sample area to obtain a statistically credible
loading estimate, often called the sampling intensity. This is done using the follow-
ing formula:

2

σ
=

z

(8.1)

n ,

E

where E is the difference between the sampled mean value (i.e., loading) and
the population mean loading value, σ is the population variance, and z is the z
value for tail of the t distribution for a selected probability value α often selected
as α = 0.05 for most sampling projects. E is estimated by how close the sampler
wants to be to the population mean (e.g., 20 % of the population mean). To cal-
culate n , most sampling projects need an a priori (beforehand) estimation of the
loading variability ( σ ) and the population mean to compute the number of sam-
pling units needed for a statistically credible estimate. The problem is that the
statistical parameters ( E , σ ) for fuel loading depend on the fuel component, and
the variability of each fuel component loading is highly localized and is differ-
ent by region, ecosystem, topographic setting, and time since disturbance (Keane
et al. 2012a ). Therefore, a priori population mean variabilities by component are
difficult to estimate from past projects. Moreover, the factors mentioned above
(resources, expertise) may often overwhelm requirements for statistical rigor in
some sampling projects. Requiring an unachievable number of sample units given
resource limitations to satisfy a statistical requirement may be counterproductive.
Likewise, executing a sampling program that cannot hope to address the project's
objectives because of inadequate precision also makes poor use of available re-
sources. Statistical rigor must be balanced with the other factors to construct a
successful sampling design.
In summary, there are usually several tasks that must be done to design an ef-
fective sampling projects: (1) identify the number of people available and assess