Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 151

(Keane et al. 2001 ). Spatial soils data can also be used to describe the biophysical
environment that can then be statistically related to fuel loadings or used in simulation
models to create ancillary biophysical layers. Digital maps that describe social con-
text (population density), transportation routes (roads, trails), utilities (power lines,
gas lines), political (land ownership, management units), and ecological (stand maps,
values at risk) resources can be used as references to characterize local to regional
fuel differences and to stratify fuel assignments (Krasnow 2007 ).
The last set of ancillary data layers are those that are created from simulation
modeling (Rollins et al. 2004 ; Keane et al. 2006a ). Simulation modeling provides
a platform to integrate disparate ancillary biophysical variables, such as climate,
topography, and soils, into one comprehensive, integrated variable that may be
more related to fuel attributes than the other variables separately. A potential evapo-
transpiration (PET) layer, computed from soils and climate data layers using an
ecosystem model, may have a better relationship to fuel loading than the soils or
climate data alone or together (Rollins et al. 2004 ). This simulation approach is
discussed extensively in Sect. 9.3.4.

9.2.3 Fuel Classifications

A comprehensive fuel classification system is indispensable in fuel mapping be-
cause the classification's categories can serve as mapping units in the fuel map
(Chap. 7). It is difficult to map loading, or any other fuel property, for each of
the fuel components because of the high number of components and the fact that
most components are difficult to map remotely, such as duff and litter, because
they are hidden by higher fuel strata such as the forest canopy (see Sect. 9.4). Fuel
classifications simplify the mapping process by providing a means to map all fuel
components at once. Since most classifications were developed for specific fire ap-
plications, creating a map using a classification ensures that it will be useful in fire
management. Finally, most fire managers are somewhat familiar with most exist-
ing fuel classifications, so mapping existing classifications eliminates the need for
additional training to learn newly developed map units.
An ideal fuel classification for mapping should quantify a myriad of fuel charac-
teristics (e.g., loading, size, bulk densities) for all fuel components at the appropriate
mapping scale and resolution (Chap. 7). Fuel classification categories should be
easily, accurately, and consistently identified in the field with comprehensive keys,
and the classification should be related to other standardized vegetation and bio-
physical classifications (Keane 2013 ). The fuel classification should uniquely iden-
tify fuel types based on fuelbed characteristics, not on vegetation attributes or envi-
ronmental descriptions, because the mapped categories must be easily validated in
the field or using existing fuel data (Keane et al. 2013 ). Moreover, the classification
structure should allow hierarchical aggregation and division so fuel categories can
be tailored to match the strengths of the mapping approach, attributes of the re-
motely sensed products, resolution of available field data and imagery, and scale
of eventual fire application. A link to other historical and current land-use maps is