Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 163

reason for low fuel map accuracies probably lies in the ecology of fuels rather than
in the limitations of the approaches and data used to map them.
Several ecological reasons are to blame for the low accuracies in most fuel maps.
As with other fuel applications, the high variability of fuel characteristics in space
and time across the diversity of components compromises most fuel-mapping ef-
forts (Chap. 6). In a validation of the LANDFIRE fuel maps, Keane et al. ( 2013 )
found that the inability of a fuel classifications' category to uniquely quantify fuel
loadings was the biggest reason for poor mapping results. This inability to predict
fuel loadings was mainly because of the high variability of loadings across compo-
nents within a classification category (Chap. 6). High variability of loadings across
classification categories is often because fuel components vary at different scales
and are uncorrelated with each other (Keane et al. 2012b ). Keane et al. ( 2000 ) hi-
erarchically assessed accuracy of vegetation and fuel maps by quantifying error in
the field data, vegetation and fuel classifications, and found more than 20 % of map
error resulted from the inherent variability of fuel components attributes sampled
at the stand-level. This high loading variability is also because fuel components are
spatially distributed at different scales and accumulate at different rates (Chap. 6).
In summary, the high variability of fuel attributes, especially loading, often over-
whelms any spectral or biophysical signal used for mapping, resulting in inadequate
discrimination of fuel classification categories and attributes.
Stand disturbance history, expressed as time since last fire for an example, is
perhaps the single most important factor dictating fuel bed characteristics (Chap. 6)
yet there are few ancillary spatial data sources that describe stand history that can be
used in fuel mapping. Vogelmann et al. ( 2011 ) use fire severity maps to update the
LANDFIRE vegetation and fuels data layers, but there are few comprehensive maps
of other disturbances. Past fires both reduce fuel component loadings by consump-
tion and increase loadings by causing plant mortality (Chap. 6). Insects, diseases,
and wind often increase fuel loadings disproportionately across components. With-
out a spatial description of the timing, severity, and extent of past disturbances, it
will always be difficult to map fuels.
There may be other logistical reasons for poor map accuracies. The biggest
limitation in most fuels mapping is the lack of timely, dependable, geo-referenced
field data describing existing fuels conditions. Few comprehensive standardized
fuel-sampling efforts have created the databases needed for fuel-mapping efforts.
For those projects where fuels were actually measured, inadequate training in fuel
model assessment and fuel measurement techniques resulted in questionable field
estimates (Keane et al. 1998b ). Fuel characteristics (e.g., surface fuel model, crown
fuels, stand height) should not be mapped independently or illogical combinations
will inevitably result. All fuel layers must be developed and mapped in parallel so
they are spatially congruent and consistent.
Low fuel map accuracies may be improved by employing newer methods and
better technology, but there are more fundamental challenges in fuel mapping that
need to be addressed first before accurate fuel maps are possible. As mentioned, we
need to view fuels as biomass and understand those ecological processes and condi-
tions that influence biomass properties over time and space. Once we understand