Agriculture Reference
In-Depth Information
(see Fig. 2.4) are critical to the appropriate management response. Although
progress is being made at remote sensing detection of fire intensity patterns during
the fire, complete coverage of a fire is often hindered by smoke so that the post hoc
fire severity (sometimes known as burn severity) measure of biomass loss and
associated soil impacts is the common metric available for assessing subsequent
ecosystem responses (Keeley
2009
). On most landscapes fire or burn severity is
mapped with remote sensing indices calculated from prefire and postfire images.
This often correlates with on-the-ground measures of fire severity based on biomass
loss (Rogan & Franklin
2001
; Chafer
et al.
2004
; Hammill & Bradstock
2006
;
Keeley
et al.
2008
). Federal agencies in the USA have redefined fire or burn severity
much more broadly to include many other parameters and termed this the Com-
posite Burn Index (CBI) (Key & Benson
2006
). Some studies report correlations
between this field measurement and remote sensing indices of fire severity; however,
it appears to be a poor field measure of fire severity in mediterranean shrublands
(Sikes
et al.
2006
) and some forested ecosystems (Murphy
et al.
2008
). Keeley (
2009
)
contends that many of the components of CBI are important measures of burn
severity but raises serious objections to use of the composite index because it
combines fire severity variables with ecosystem response variables such as postfire
resprouting, and thus using it as a predictor of ecosystem response is circular.
Remote imaging techniques show great promise for detecting different patterns
of fire severity and are increasingly important on large fires (see
Chapter 2
). The
differenced Normalized Burn Ratio (dNBR), which is based on the Landsat TM
sensor, is widely applied with variable success across different vegetation types
(Hammill and Bradstock
2006
). In shrubland ecosystems it is strongly correlated
with field measurement of fire severity; however, due to the extraordinary resili-
ence of these ecosystems neither fire severity nor dNBR predicts ecosystem
response variables such as postfire cover (Keeley
et al.
2008
). In forests interpret-
ing dNBR is more complicated since it often cannot detect understory fire impacts
and thus is a better measure of the impact on tree canopies. Apparently a more
useful metric is the relative dNBR (dNBR/prefire NBR) (Miller & Thode
2007
).
The absolute dNBR is a measure of the actual biomass loss from fire and thus a
good surrogate for fire intensity in crown fire ecosystems but less so in forests with
understory burning. However, the relative dNBR is a measure of change after fire
and in forested ecosystems this is tied to tree mortality. In shrublands, and in other
crown fire ecosystems where 100% aboveground mortality is to be expected, the
relative dNBR is of limited value and unrelated to field measures of fire severity
(Keeley
et al.
2008
).
Currently dNBR indices are widely used as predictors of hydrologic stability;
however, the extent to which different degrees of fire severity are correlated with
postfire hydrologic changes remains unclear. Postfire increases in soil water
repellency due to hydrophobic soil layers is tied, albeit sometimes weakly, to fire
severity (Lewis
et al.
2006
), although in some ecosystems soil hydrophobicity
is unrelated to fire severity (Doerr
et al.
2006
). Although fire per se does affect
hydrological functioning, there is little direct evidence that fire severity is a reliable
