Fuel Sampling - Wildland Fuel Fundamentals and Applications - page 129

8.3.1 Indirect Methods

These methods involve quantifying fuel loadings using techniques that do not di-
rectly involve measuring the fuel property, but rather, use other references or sourc-
es to quantify loadings. This usually involves subjectively assigning loadings by
comparing with existing data (association), inspecting fuel conditions and visually
comparing to reference conditions (visual), or correlating with remotely sensed im-
agery (imagery).

8.3.1.1 Associative Techniques

The most common associative technique involves using existing data or informa-
tion, often collected by someone else from somewhere else, to estimate loading
values for the area of concern or project area. Fuel loading data collected for an-
other area, for example, may be assigned to the area in question if the two areas are
deemed similar, perhaps based on vegetation composition, disturbance histories,
and biophysical site conditions. Catchpole and Wheeler ( 1992 ) call this approach
the comparative yield method and mention that they could be improved by using
statistics, photos, and expertise to aid in the data assignment. The problem with this
technique is that each site and project area is ecologically unique and the extrapola-
tion of loadings from one site to another might ignore those important but subtle
factors that have influenced component loadings, such as differences in basal area,
tree density, disturbance history, topographic setting, and stand structure.
Another commonly used associative technique is to assign fuel loadings to a
sample area based on the sampled area's vegetation characteristics, similar to asso-
ciation approach used in fuel classification (Chap. 7), except, in this case, it is used
to assess actual loadings in the field. Several fuel classifications were built by sum-
marizing plot-based fuel component loadings across categories in vegetation and
related classifications such as structural stage, cover type, and potential vegetation
type. The FOFEM program, for example, includes loading defaults as a summary
of fuel loadings across legacy plots within SAF (Society of American Foresters)
and SRM (Society of Range Management) cover type categories (Reinhardt et al.
1997 ). Many people have used these defaults as a fuels inventory when conducting
various analyses. The Fuel Characteristics Classification System (FCCS) (Ottmar
et al. 2007 ) was specifically designed so that fuel loading data collected for one area
could be used for other areas based on a set of seven vegetation and disturbance-
related stratifications. This indirect method assumes that fuel component loading
values, either individually or as a collective group, correlate to vegetation char-
acteristics. However, as in fuel classification development (Chap. 7), studies have
found that fuel loadings correlate poorly to vegetation types, especially at the fine
spatial scales of project and treatment areas (Brown and Bevins 1986 ; Keane et al.
2012b , 2013 ). Vegetation classification categories may correlate to fuels at coarser
scales (e.g., lifeforms, large regions), but the high variability of fuels at fine scales