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(Reeves et al.
2009
). The associative approach is easily the most commonly used
approach for developing fuel maps.
Examples of this approach can be presented by spatial scale. Coarse-scale im-
agery is often used to discriminate broad vegetation types or land cover classes,
and these classes sometimes correlate with fuels because vegetation categories are
so broad they generally have unique fuel characteristics. Burgan et al. (
1998
) used
Omernik (
1987
) ecoregions and the Loveland et al. (
1991
) AVHRR land-cover
classification to develop an NFDRS fuel model map of the conterminous USA.
Landsat imagery was used to map vegetation on 100 million ha in Alaska, and then
fuel models were assigned to each vegetation category (Willis
1985
). McKenzie
et al. (
2007
) mapped FCCS fuelbeds to vegetation and disturbance classification
categories, and the FCCS fuelbeds of Ottmar et al. (
1994
) were assigned to combi-
nations of vegetation cover and structure types for the Interior Columbia Basin Eco-
system Management Project (Quigley et al.
1996
). Menakis et al. (
2000
) expounded
on the “vegetation triplet” approach where fuel models or classes are assigned to
categories in three classifications: potential vegetation, vegetation composition, and
vegetation structure. Jain et al. (
1996
) intensively sampled fuels for all categories
of a forest-type map created from Linear Image Self Scanning (LISS II) imagery to
create a fuel map for Rajaji National Park in India. In Canada, the Canadian Forest
Fire Behaviour Prediction System (FBP, Forestry Canada Fire Danger Group 1992)
fuel types were assigned to vegetation categories on maps created from Landsat
Multi-Spectral Scanner (MSS) data for Wood Buffalo National Park (Wilson et al.
1994
), Quebec (Kourtz
1977
), British Columbia (Hawkes et al.
1995
), and Mani-
toba (Dixon et al.
1985
).
The association approach is used for many reasons. The most common reason is
that it is relatively easy, quick, and economical to create fuel maps from other maps
because they can be done by anybody for any location where there is an associative
map. There are many vegetation classification maps available to associate fuel char-
acteristics (Anderson et al.
1998
; Grossman et al.
1998
), and most people can easily
identify the vegetation-type categories of these classifications in the field. There
are also many field data sets that contain assessments of these extant classification
categories at the plot level that can augment fuel mapping. Since extant classifica-
tion maps are used extensively in resource management, the assignment of fuel
attributes are easily understood by managers, and the resultant fuel maps can be
linked to other resource concerns. Many fuel attributes can be assigned to an extant
category allowing the creation of many types of fuel maps, such as surface fuel
maps and canopy fuel maps (Keane et al.
2000
). Finally, associative maps often pro-
vide a context for interpreting fuel distributions across a landscape. For example, it
is helpful to know that a polygon was assigned a needle and litter FBFM because it
was a ponderosa pine stand.
The major disadvantage of association in fuel mapping is that fuels are not
always correlated with vegetation characteristics or land-use categories so statistical
relationships between fuel and the associated layers may be too weak to develop
useful predictive models (Chap. 6). An example of this lack of relationship is the
redundancy of fuel classes across the associated mapped classification classes. For
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