Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 155

simulate burning in the fuel complex. Observers are easily able to visually assess
highly variable fuel conditions across large areas to estimate an average or represen-
tative value providing there is extensive training. Any special conditions that arise
in the field, such as the identification of a new fuel type or the elimination of a rare
fuel type, can be easily integrated into the mapping scheme. And this approach can
easily be augmented with field sampling to increase accuracies and map detail. It
can also be scaled to specific projects creating anywhere from high-resolution maps
for small areas to coarse-resolution maps for large regions.
The great amount of effort involved in a successful field approach would
probably preclude its use in most large-scale operational fuel-mapping projects
today. The majority of time and money spent on any fuel-mapping effort is usually
in field assessments of fuel conditions so assessing the entire map area would be
impractical. Another drawback is that there are always inconsistencies between field
observers because of differences in their expertise and knowledge of fuels and fire
(Sikkink and Keane 2008 ). And there is a sampling bias toward mountainous terrain
since most of the reconnaissance mapping efforts are done from observation points
on high, burned-over vistas, so locations not directly seen from these observation
areas were probably mapped with less accuracy (Brown and Davis 1973 ). This
approach would be more valuable if it were integrated with field sampling to create
the field reference datasets to augment with other fuel-mapping approaches.

9.3.2 Association

In the association approach, fuel maps are developed by assigning fuel attributes
to the categories or mapping units of maps of other land classifications, similar to
the associative fuel classification (Chap. 7) and associative fuel-sampling (Chap. 8)
approaches. There are a number of readily available, well-known spatial data lay-
ers of vegetation, topography, and land use that can be used either alone or in
combination to associate fuel characteristics to each classification category or com-
bination (McKenzie et al. 2007 ). In the association process, fuel attributes are usu-
ally quantified or selected from a synthesis of field data across extant classification
categories. These fuel attributes are then assigned to that category to create the fuel
map from the existing map. Satellite imagery and other remotely sensed products
are better suited for differentiating between vegetation types than fuel types (Keane
et al. 2001 ). Keane et al. ( 1998a ), for example, overlaid maps of vegetation and
topography classifications with plot-level geo-referenced FBFM assessments and,
for each vegetation and topography class combination, they assigned the modal
FBFM of all field plots within that combination. A fuel type group map was created
by averaging fuel loadings for each of eight fuel components for all USFS Forest
Inventory and Analysis plots in each forest-type group category (Keane et al. 2013 ).
This approach may also be used with expert knowledge techniques that assign fu-
el-classification categories to other map categories using the experiences of fire
professionals (Keane and Reeves 2011 ) or statistical analysis of field data to build
empirical models that assign fuel characteristics to other classification categories