Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 149

most fuel maps are used at finer scales, primarily for landscape assessments, be-
cause this is the scale at which most fires can effectively be simulated and managed
(Heyerdahl et al. 2001 ). Landscape fuel maps are used to predict future spread of
wildfires (Finney 2005 ), describe fire hazard and risk (Finney 2006 ), and portray
fire severity (Karau and Keane 2010 ).
Creating wildland fuels maps is quite difficult, especially at landscape to region-
al scales, for a number of reasons (Arroyo et al. 2008 ; Keane et al. 2001 ). The lack
of critical resources, such as limited geo-referenced fuel data and inadequate fuel
classifications, coupled with a variety of ecological concerns, such as fuelbeds be-
ing hidden by the canopy and scale mismatches in field data, imagery, and analysis
techniques, often complicate fuel-mapping efforts. Accurate fuels layers are costly
to build because they require abundant field data, extensive expertise in a wide
variety of spatial fields (remote sensing, geographic information system (GIS), fire
and fuel modeling, image processing, vegetation mapping), and of course, a com-
prehensive knowledge of fuels (Keane et al. 2001 ). But most importantly, fuels
are notoriously difficult to map because of their high variability and disparate spa-
tial distributions across components (Chap. 6). This chapter first summarizes some
critical mapping resources needed for nearly all mapping projects and then presents
some general approaches used to map fuels for fire management at multiple scales.
The challenges of fuel mapping are presented last to explain why most of today's
fuel maps have some major limitations.

9.2 Fuel-Mapping Resources

9.2.1 Field Data

Field data are the most critical resource for mapping fuels, and collecting enough
appropriate field data is often the most costly and time-consuming part of any
mapping effort (see Chap. 8). Ground-based fuel sampling is literally the only way
to realistically, accurately, and consistently describe the fuel characteristics being
mapped (Keane et al. 2013 ) and it would be imprudent to attempt to map fuels
without extensive field sampling. Geo-referenced field data are important for many
reasons. First, field data provide important references for the mapped fuels classes
because the data provide the only detailed descriptions of fuels (loading, classifica-
tion category). Field plot data can also be used to describe polygons that can then be
used as training areas in supervised classifications, or they can be used to describe
unique clusters in unsupervised classifications (Verbyla 1995 ). More importantly,
field data allow the development of statistical models for predicting fuel character-
istics over space using ancillary biophysical spatial layers. Field data also provide a
means for quantifying accuracy and precision of not only the fuel map but also the
classification whose categories are being used as mapping units (Keane et al. 2013 ;
Burgan and Hardy 1994 ). Plot data can be used to design and improve keys for