Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 152

also desirable. Another desirable trait of useful fuel-mapping classifications is that
the categories in the classification are easily and effectively discriminated by the
diverse approaches used to map fuels (see Sect. 9.3).
Nearly all the fuel classifications mentioned in Chap. 7 have been used in fuel-
mapping efforts. Perhaps the most mapped classifications are the fire behavior fuel
models (FBFMs) which are needed to simulate wildfire in the USA. Reeves et al.
( 2009 ) created fine-scale (30 m) FBFM maps for both the Scott and Burgan ( 2005 )
and Anderson ( 1982 ) classifications for the contiguous USA. Root et al. ( 1985 )
mapped FBFMs for North Cascades in the US Pacific Northwest, while Peterson
et al. ( 2012 ) produced FBFM maps for Yosemite National Park and Falkowski et al.
( 2005 ) for northern Idaho. McKenzie et al. ( 2007 ) mapped FCCS fuelbeds at 1 km
for a national US scale and at 30 m for the Wenatchee National Forest, Washington,
USA. Hawkes et al. ( 1995 ) mapped the fuel types in the Canadian Fire Behav-
ior Prediction system for landscapes in British Columbia, Canada. The National
Fire Danger Rating System (NFDRS) fuel types were mapped at a coarse scale by
Burgan et al. ( 1998 ) for the USA and by Chuvieco and Salas ( 1996 ) for Spain.
There is a fundamental problem with using FBFMs as mapping units. The iden-
tification of FBFMs in the field is entirely subjective because it is based on an in-
dividual's perception of fire behavior under assumed weather rather than on actual
measurements of fuel loadings (Chap. 7). Many field technicians find it difficult to
consistently identify FBFMs on the ground because it requires knowledge of the
fuel characteristics important to fire behavior, expertise in forecasting fire behav-
ior in the field, and familiarity with the FBFMs. Even more important is that it is
impossible to uniquely identify a FBFM from extant or legacy field data because a
visual inspection of the fuelbed is absolutely essential for evaluating potential fire
behavior (Anderson 1982 ). The FuelCalc program (Reinhardt et al. 2006 ) contains
a routine that attempts to assign a FBFM from fuel loading data, but the routine
has never been evaluated for accuracy and consistency. As a result, it is impossible
to assess map accuracy for any of the FBFM classifications; one would have to
observe fire behavior at a burning pixel to properly evaluate FBFM map accuracy.
Reeves et al. ( 2009 ) addressed this subjectivity by holding calibration workshops
attended by fire behavior specialists to evaluate fuel maps and adjust values where
needed (Keane and Reeves 2011 ). And since most FBFMs quantify only a fraction
of all dead and live biomass pools, they are rarely useful for most other fire applica-
tions such as smoke estimation and carbon cycling simulation.

9.3 Fuel-Mapping Approaches

Today's fuel maps are created by a complex merging of technologies and integra-
tion of analysis techniques (Arroyo et al. 2008 ). In general, there are four general
approaches used to map fuels at multiple scales: field assessment, association,
remote sensing, and biophysical modeling (Table 9.1 ). Early attempts at mapping
fuels often used only one or two of these approaches, but as computing resources