Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 159

vertical profile of the fuelbed. LiDAR estimates distance to an object by measur-
ing the time delay between the transmission of a pulse of light and the detection of
the reflected light from a target. This process, in a vegetative setting, can result in
millions of points in an area that describe the fuel strata. The point distances can
be used to calculate elevations to map a fuelbed in three dimensions if the spatial
density of laser measurements is high. The distribution of elevations can be used
as a signal to map fuels and the strength of the return signal is also useful for deter-
mining the surface condition that may be related to certain fuel types. Some have
used LiDAR to map surface FBFMs with some success (Mutlu et al. 2008 ), but the
real strength of LiDAR is in the mapping of canopy fuels (Andersen et al. 2005 ;
Erdody and Moskal 2010 ) because the number of LiDAR distance measurements
within the canopy profile is often correlated to CBD and canopy base height (CBH;
Riaño et al. 2003 ). However, LiDAR also has its problems. While it can accurately
produce a canopy height profile, it has limited ability in differentiating the material
that reflected the laser intercept; it is difficult to tell if the piece of biomass hit by the
laser was a leaf, twig, or log. The canopy obstruction problem is also a factor in that
upper canopies obscure lower canopy strata and thereby collect a disproportionate
number of LiDAR hits. Loadings for those fuel components that contain the major-
ity of dead biomass, logs, litter, and duff are also difficult to sense from LiDAR
because their size or depth is nearly impossible to measure using LiDAR.
There are advantages to using a remote sensing approach (Arroyo et al. 2008 ).
First, unlike all other approaches, remotely sensed data provide a spatial description
of existing landscape conditions and act as a snapshot of the landscape. As such,
these data can be useful for the detection of changes in fuel conditions through time
and space. Most imagery products are easy to obtain but their cost is highly variable
ranging from free to quite expensive. Remotely sensed imagery can be obtained for
a wide variety of resolutions allowing appropriate scaling of the imagery to fuel
component distributions.
Logistical concerns, however, may limit many remotely sensed fuel-mapping
projects. Expertise in image processing, GIS analysis, and statistical modeling is
rare and expensive, and combined with expertise in fuel science and fire behavior
modeling, the number of people qualified for fuel-mapping projects are scarce. Ab-
solutely critical to remotely sensed fuel-mapping projects are surface and canopy
fuel data which are often limiting in most areas. The analysis of the imagery also
demands high computing resources which may be restrictive for many fire manag-
ers. Finally, many of the remotely sensed products, such as LiDAR, ASTER, and
SAR, may be too expensive for operational fuel mapping across large domains and
require specialized expertise in data processing.
There are also important ecological limitations of remote sensing approaches for
fuel mapping. As mentioned, some fuel component attributes, such as CBH, FWD,
and herbs, are obscured by the canopy in most forest and some shrubland ecosystems
(Keane et al. 2001 ). Even if the fuel components were visible from above, the remote-
ly sensed imagery probably would probably have low correlation to many attributes
that are being mapped, such as loading, because of the mismatch in scales. Logs and
FWD are too small to be sensed by most imagery products with 30-m pixel resolution,