Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 160

yet they comprise the majority of loading in some environments. Duff and litter
loading, as another example, depends on their depth on the ground, and this depth
is rarely correlated to imagery signals (Asner 1998 ). Most imaging sensors were
designed to differentiate vegetation characteristics, so vegetation conditions may
often overwhelm any fuel signal, and most fuel components, such as woody fuels,
have similar reflective properties making it difficult for their differentiation.
Another limitation is that it is often difficult to quantify fuelbed characteristics
for each component with only one unique spectral signature, unless, of course, a
fuel classification is being mapped, but then few fuel classifications are highly cor-
related to imagery (Keane et al. 2013 ). Conversely, if fuel components are mapped
separately, there is a good chance that each component map will be spatially in-
congruent or inconsistent, and impossible combinations may result. And, since fuel
components are spatially distributed at different scales, using only one imagery
product with one resolution ensures some fuels may always be mapped at an inap-
propriate scale (Keane et al. 2012a ; Chap. 6); fine fuels important for fire spread
are too small to be detected accurately by most passive imagery products. It is also
difficult to detect the vertical distribution of fuels with passive imagery; the sensed
FWD might actually be suspended above the ground.

9.3.4 Biophysical Modeling

This last approach relates fuel attributes to measured or simulated biophysical gra-
dients using statistical modeling. Biophysical gradients describe those ecological
phenomena that may directly or indirectly influence fuel dynamics (Chap. 6), such
as climate, productivity, and disturbance. Spatial data representing these gradients
can be (1) measured directly, such as climate, soils, and topography, (2) measured
indirectly by correlating with imagery, or (3) simulated using biophysical models.
The direct and indirect gradients are often used as inputs into biophysical models to
create additional gradients.
Ecosystem models have vastly improved over the past two decades and there
are a wide variety of models for application at coarse (e.g., MAPPS, Lenihan et al.
1998 ), regional (e.g., BIOME-BGC, Thornton et al. 2002 ), and fine scales (e.g.,
FireBGCv2, Keane et al. 2011 ). These models simulate those ecosystem processes
known to govern fuel dynamics and these simulated processes can then be mapped
and used to predict fuel characteristics across space. Relationships between biophysi-
cal processes and organic matter accumulation and decomposition, for example, can
be used to predict fuel characteristics (Gosz 1992 ; Ohmann and Spies 1998 ). Rollins
et al. ( 2004 ) developed a prototype system to link remote sensing, gradient model-
ing, and ecosystem simulation into a package for mapping those characteristics im-
portant to land management, and then used the system to map FBFMs (Keane et al.
2006a ). Biophysical layers can be topographical (e.g., elevation, aspect, slope), bio-
logical (e.g., successional stages), geological (e.g., soils, landform), or biogeochem-
ical (i.e., evapotranspiration, productivity, nutrient availability). Kessell ( 1976 )