Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 161

used seven biophysical gradients based on topography and vegetation to spatially
predict fuel models and loadings in Glacier National Park, Montana. Habeck ( 1976 )
sampled fuels and vegetation in the Selway-Bitterroot Wilderness Area of Idaho and
related fuel loadings to stand age and moisture-temperature gradients. Keane et al.
( 1997 ) developed a protocol for mapping surface fuels from several biogeochemical
and biophysical variables using an extensive network of field plots, and later used
those techniques for mapping canopy fuels (Keane et al. 2006a ).
The value of this approach is that simulated environmental gradients provide an
ecological context in which to understand, explore, and finally, predict fuel dynam-
ics. Low fuel loadings in a stand, for example, may be explained by low precipi-
tation, high evapotranspiration, and low productivity. Furthermore, environmental
gradients can quantify those important ecosystem processes that correlate with fu-
els, such as decomposition, to provide a temporal and spatial framework for creat-
ing dynamic fuels maps. Climate change effects on spatial fuel loadings can be
easily computed by evaluating changes in environmental gradients under the new
climate (Keane et al. 1996 ). Most environmental gradients are scale-independent,
meaning the same gradients might be useful to predict fuel characteristics across
many spatial scales, but the range, distributions, and strengths of the relationships
might change. These models can also be used to update fuel maps by simulating
deposition and decomposition processes to see how the fuels have changed over the
life of the map. And once biophysical layers are developed, they may be used by
land management agencies for many management applications (Keane et al. 2002 ).
One major problem with this approach is that biophysical gradients do not pro-
vide a comprehensive description of existing biotic conditions so remotely sensed
data are often needed to spatially portray the current fuel conditions. Another
disadvantage is that this approach requires abundant field data, complex ecosystem
models, and intensive statistical analyses requiring extensive expertise in ecological
sampling, simulation modeling, and statistical examination. Ecosystem models de-
mand comprehensive initialization, parameterization, calibration, and validation to
be useful, and this often requires extensive data, time, expertise, and computing re-
sources. Biophysical settings are inherently difficult to map because they represent
the complex integration of long-term climatic interactions with vegetation, soils,
fauna, and disturbance (Barrett and Arno 1991 ; Habeck 1976 ; Keane et al. 1996b ).
Moreover, identification of those biophysical processes critical to fuel dynamics
is difficult because most are unknown or unquantifiable, and they are difficult to
identify in the field because of their temporal aspect. Many biophysical layers may
have limited value for mapping fuels because of interacting factors and they are of-
ten correlated with other biophysical processes. And last, all biophysical gradients
affect fuel processes at different scales so it is important that the biophysical layers
are created at the most appropriate scales that influence fuel properties.