Geoscience Reference
In-Depth Information
Fig. 3.1
LANDFIRE data mapping Washington State's Fire Regime Groups, which represent an
integration of frequency and severity. These groups are intended to characterize the presumed
historical fire regimes within landscapes based on interactions between vegetation dynamics, fire
spread, fire effects, and spatial context. (Graphics by Jacob Lesser)
categorizes fire regimes into five very simple classes representing combinations of
only fire frequency and expected fire severity (Fig.
3.1
). However, rather than only
two variables, fire regimes traditionally involve five variables that are characterized
here as; fuel types and their structures, fire intensity, fire severity, fire timing, and
the spatial scale of the fires.
The definitions of each of these variables can be somewhat elusive, and there
has been considerable debate about how to understand, quantify, and map each of
them. Even the terms themselves have been controversial. For example, Lentile et al.
have suggested that “fire intensity” be replaced with “active fire characteristics” and
“fire severity” be replaced with “post-fire effects” (Lentile et al.
2006
). Additionally,
Keeley has recommended differentiating between what he calls “fire severity” and
“burn severity.” For an excellent history and discussion of several of the terms asso-
ciated with fire regimes see Keeley (
2009
). Rather than providing an exhaustive
review, this chapter will simply attempt to capture the broad characteristics that
make up each of these elements of fire regimes while explaining some of the sub-
tleties and current trends, geotechnical tools, and techniques that are being used in
Pyrogeography.
3.4 Fuels and Fuel Models
We often map wildland fuels with raster data sets. Each pixel is assigned a value
that represents some particular structure and arrangements of living and dead fuels.
However, fire burns fuels not pixels. Mapping, modeling, and understanding the
spatial patterns of these fuels can be devilishly complicated. Obviously, the possible
combinations of species and structures of biomass are essentially infinite. Therefore,
it has been practical to develop sets of “fuel models” that represent a simplified set of
all the possible combinations of fuels and species. As these models were originally
developed to aid in the prediction of fire behavior, it has also been important that
