Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 150

the vegetation and fuels classifications being mapped. And most importantly, field
data provide a means for interpreting fuel maps; inaccuracies or inconsistencies in
mapping results can be explored using detailed plot data. A mapped shrub-herb
category, for example, might be poorly mapped because the sampled cover of bare
soil and rock was high on field plots.

9.2.2 Ancillary Spatial Data Layers

Fuel maps can be dramatically improved if supplementary spatial data are integrated
into the mapping process (Keane et al. 2001 ). These ancillary spatial data often
describe the biophysical environment to provide ecological context to the mapping
process and to represent those processes that control fuel dynamics to increase pre-
dictive potential (Chap. 6). The most important ancillary GIS layer is the digital
elevation model (DEM) that is used to describe the topography (e.g., slope, aspect,
position) and indirectly represent the biophysical environment (e.g., climate). Many
important topographic products can be derived from the DEM, such as slope posi-
tion, stream corridors, and drainage basins (Skidmore 1989 ), to use as independent
variables in statistical predictive models that create fuel maps. Moreover, it is possi-
ble to use the DEM as input to simulation models to create other biophysical layers,
such as radiation, exposure, and microsite temperatures, and these new biophysical
layers can be used to developed predictive relationships for mapping fuels (see
Sect. 9.3.4). The DEM also is useful in delineating broad biophysical settings that
can be used to stratify statistical modeling and fuel-mapping processes.
Perhaps the next most used ancillary data layers are digital maps of potential and
existing vegetation classification systems, such as cover type, potential vegetation
type, and structural stage maps (Menakis et al. 2000 ). Even though fuel loadings are
rarely correlated to vegetation (Chap. 6), these maps are be important because they
provide valuable context for assigning fuels to known settings, information on bio-
physical environment, and important linkages to other land management concerns.
Vegetation layers are most useful if they were created across multiple scales using
standardized, hierarchical classifications so that categories can be merged or split
based on the ability of remote sensing to discriminate differences (Loveland et al.
1993 ; McKenzie et al. 2007 ). The most commonly used vegetation maps are ones
that describe species composition (cover type), structure (vertical canopy layers),
and some expression of potential vegetation (i.e., biophysical site; Menakis et al.
2000 ) because these three maps can be used to simulate vegetation development
and therefore possibly fuel succession (Keane et al. 2006b ).
Many other existing data layers have been used to map fuels. Spatial chronose-
quences of ecosystem characteristics, such as leaf area index (LAI), created from
updated satellite imagery (e.g., MODIS), can be integrated in map development to
quantify available biomass, represent fuel models, and correlate to many other fuel
attributes (Rollins et al. 2004 ). Climate layers that integrate long-term weather into
quantitative summaries that relate to fuel dynamics are also valuable ancillary layers