Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 157

example, there were as many as four different FBFMs found in the many of the
combinations of vegetation structure, species composition, and topographic set-
tings classes for maps of the Selway-Bitterroot Wilderness Area, USA. (Keane et al.
1998a ). Stand disturbance history, biophysical setting, and vegetation structure are
significant factors governing fuel characteristics so they should be incorporated into
the fuel model assignment protocols. Also, the scales of the base classifications
may not match the scale of the fuels being mapped or the sample design of the field
data used in the mapping (Keane et al. 2006a ). The vegetation categories in the
Society of American Foresters (SAF) cover-type classification used in the FOFEM
model, for example, are so broad for some cover types that they encompass a wide
variety of fuelbed conditions that overwhelm important local differences (Schmidt
et al. 2002 ). Other disadvantages are compounding errors occurring when the error
inherent in the original base classifications is combined with errors in the fuels clas-
sifications and errors in fuel class assignment (Keane et al. 2013 ).

9.3.3 Remote Sensing

Remote sensing approaches attempt to correlate remotely sensed imagery with fuel
characteristics using statistical modeling to create a fuel map (Keane et al. 2001 ;
Lanorte et al. 2011 ). The imagery can be from any number of passive and active
sensors. Passive sensors include digital aerial photography (Oswald et al. 1999 ),
Landsat Thematic Mapper (TM; Brandis and Jacobson 2003 ), Advanced Space-
borne Thermal Emission and Reflection Radiometer (ASTER; Falkowski et al.
2005 ), and hyperspectral (Jia et al. 2006 ), while active sensors are usually LiDAR
(Andersen et al. 2005 ) and radar (Bergen and Dobson 1999 ). These sensors can be
mounted on any number of platforms including fixed wing aircraft, helicopters,
and satellites to obtain a wide range of resolutions and detail (Xiao-rui et al. 2005 ).
Passive sensors usually measure the reflectance of light in a narrow band of the
electromagnetic spectrum, and some of these sensors, such as Landsat's TM with
a 30 m pixel size, create multiple data layers that represent the reflectance from
multiple spectral bands. Hyperspectral imagery, such as Airborne Visible InfraRed
Imaging Spectrometer (AVIRIS), Hyperion, and HYDICE, may have more than 50
different spectral reflectance layers. Active sensors, such as LiDAR, consist of a
cloud of point measurements of return times and signal strengths that are then used
to statistically model height and loading (Riaño et al. 2003 ).
The central assumption of the remote sensing approach is that there is a cor-
relation between fuel characteristics and the remotely sensed data signal. Fuel at-
tributes, such as loading, canopy bulk density ( CBD ), or classification categories,
either computed from legacy plot data or measured directly on geo-referenced plots,
are related to the reflectance values of the plot location using simple to complex
statistical modeling. Two general statistical methods are used to create fuel maps. In
the supervised classification technique, statistical models that directly predict fuels
information are built from the reflectance values of the imagery and the field data.
Then, fuel maps are then created by employing the developed predictive relationships