Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 162

9.3.5 Integrating Approaches

Most mapping projects integrate all approaches to create state-of-the-art fuel maps.
Peterson et al. ( 2012 ) statistically modeled live and dead woody fuel component
loadings using regression classification procedures with a suite of climate, topog-
raphy, imagery, and fire history-independent variables. Varga and Asner ( 2008 )
merged LiDAR with hyperspectral imagery to map surface fuels in Hawaii. A
knowledge-based system of neural networks was used to search for unique fuel
patterns on a large landscape in Portugal from land-use, vegetation, satellite im-
agery, and elevation information (Vasconcelos et al. 1998 ). Pierce et al. ( 2012 )
used intensive field sampling to describe surface fuels for spectral clusters in an
unsupervised approach and correlated canopy fuel characteristics to topography
(elevation, slope, aspect) and Landsat TM imagery using Random Forests statisti-
cal modeling. And, in the most extensive fuel-mapping effort in the USA, Reeves
et al. ( 2009 ) mapped canopy fuel attributes ( CBD, CBH ) for the contiguous USA
by creating regression models from Landsat TM reflectance imagery, biophysical
gradients simulated by an ecosystem process model, and topographic variables cal-
culated from the DEM. They also mapped four surface fuel classifications using an
associative approach where categories were assigned to combinations of vegeta-
tion cover, structure, and biophysical classifications using statistical modeling and
expert opinion. The merging of multiple approaches has resulted in some of the
most useful and accurate fuel maps.

9.4 Challenges

The accuracy of fuels maps varies widely, but generally, most fuel maps have
low accuracies. When accuracy assessments were reported, they usually ranged
between 5 and 85 % correct, regardless of fuel-mapping approach or integrative
strategy (Keane et al. 2013 ). Fuel map accuracies often reflect the approaches used
to create the maps; maps created with the associative approach, for example, tend
to have the same accuracies as the core maps used to associate fuel attributes. Low
map accuracies, however, don't always mean the fuel map is worthless, especially
considering the high variability and complexity of fuels. Alternative management
strategies can be effectively compared by assessing the relative differences in fuel
conditions between sites in fuel maps with precision. Low fuel map accuracies may
be a result of a number of inherent sampling and analysis errors that are out of
the mapper's control, such as (1) scale differences in field data and mapped ele-
ments, (2) improper geo-registration, (3) erroneous field identification or measure-
ment of a mapped attribute, (4) improper use of vegetation or fuels classifications,
(5) mistakes in field data entry, (6) differences in sampling error across fuel compo-
nents, and (7) inappropriate fuel-sampling methods and designs. However, the main