Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 153

Table 9.1 Summary of the approaches used to map fuels for fire management

Approach Description Advantages Disadvantages

Field assessment Using ground-based surveys and

field reconnaissance to assign fuel
attributes across an area

Mapping actual observations; no introduced mod-
eling or analysis error; limited number of steps;
easily modified and refined; easily augmented with
field sampling

Costly and time-consuming; inconsistency
between mappers; somewhat subjective; bias
toward mountainous terrain

Association Assigning fuel attributes to cat-

egories in extant classifications,
often vegetation classifications

Simple, direct, and easy; no need for additional
mapping; most extant classifications are well
known and easily understood; can assign many
fuel attributes to one category; can use many
classifications to fine-tune fuel assignments;
create robust maps useful for other natural
resource applications

Fuels are often unrelated to vegetation
categories; scale and resolution of extant clas-
sification does not match scale of fuel data
or subsequent application; high redundancy
in fuel attributes across extant classification
categories

Remote sensing Correlating remotely sensed imag-

ery with fuel characteristics

Readily available; provides snapshot of exist-
ing conditions; well accepted and long history of
resource mapping using remote sensing products;
many products available at different resolutions
and detail

Fuels often are uncorrelated to imagery signals;
scale of imagery may not match scale of fuel
distribution; requires extensive expertise in
remote sensing, GIS, statistical modeling, and
wildland fuel science; difficult to understand
reasons for mapping successes or failures

Biophysical
modeling

Calculating or simulating bio-
physical gradients to correlate to
fuel attributes

Relating those processes that control fuel dynamics
to fuel mapping; provides context for interpret-
ing fuel maps; can often simulate environmental
gradients at multiple time and space scales; can be
used to map many other ecological characteristics
to augment fuel mapping

Best describes potential rather than exist-
ing fuel conditions; fuel attributes often are
uncorrelated to biophysical variables; difficult
to decide the type, detail, and scale of the
biophysical gradient that best represents fuel
dynamics; scale of simulated gradient may
not match scale of fuel distribution or fuel
processes; requires extensive expertise in
ecosystem modeling, GIS, statistical modeling,
and wildland fuel science; demands extensive
data for initialization and parameterization