Fuel Mapping - Wildland Fuel Fundamentals and Applications - page 158

across all pixels. In the unsupervised classification technique, the reflectance values
of all pixels are used in advanced statistical clustering methods to create unique
spectral “signatures” and then various statistical techniques are used to assign the
geo-referenced plot information to the mapped spectral signatures. Numerous other
data layers and spatial information can be augmented with the spectral imagery to
improve both the unsupervised and supervised statistical analyses (see Sect. 9.3.2).
Many types of fuel maps have been created using passive satellite imagery,
mainly from Landsat satellite sensors. The majority of fuel-mapping efforts used
from Landsat MSS and TM imagery to map surface fuel classification categories.
Kourtz ( 1977 ) used Landsat MSS data to map fuel models in Canada. Salas and
Chuvieco ( 1994 ) classified Landsat TM imagery directly to 11 of Anderson's ( 1982 )
fuel models, then assigned vegetation categories to each fuel model to compute fire
risk on a large landscape in Spain. An Anderson ( 1982 ) FBFM map was classified
directly from TM imagery of Camp Lejeune, North Carolina, for simulating pre-
scribed fires with FARSITE (Campbell et al. 1995 ). However, the highest successes
are when total living and dead biomass were directly mapped to spectral signatures.
Direct biomass imagery mapping is more accurate for grasslands and shrublands
(Chladil and Nunez 1995 ; Millington et al. 1994 ; Friedl et al. 1994 ), but less cer-
tain when assessing surface fuels in forested ecosystems because of the canopy
obstruction problem (Elvidge 1988 ; see Sect. 9.4). Merrill et al. ( 1993 ) estimated
living grassland biomass in Yellowstone National Park using regression models on
bands 4, 6, and 7 from Landsat MSS) imagery. Using TM imagery, Peterson et al.
( 2012 ) directly mapped 1-, 10-, and 100-h loadings in Yosemite National Park USA
and Brandis and Jacobson ( 2003 ) mapped total fuel loads in Australia. Large-scale
aerial photography and aerial sketch mapping have been used successfully to esti-
mate natural and slash fuel distributions in a variety of forested settings in Canada
(Belfort 1988 ; Morris 1970 ; Muraro 1970 ).
Other imagery has been successfully used in fuel-mapping efforts. At fine scales,
Lasaponara and Lanorte ( 2007a ) used QuickBird high-resolution imagery (2.9 m)
to map fuel types in Italy. ASTER imagery, having higher spectral (15 bands) and
spatial (15 m) resolution than Landsat TM (7 spectral plus a panchromatic band,
30 m spatial resolution), was used to map Mediterranean fuel types in southern
Italy (Lasaponara and Lanorte 2007b ) and the 13 Anderson ( 1982 ) FBFMs in Idaho,
USA (Falkowski et al. 2005 ). Root and Wagtendonk ( 1999 ) used hyperspectral im-
agery to map fuels in Yosemite National Park, USA, while Jia et al. ( 2006 ) used
AVIRIS hyperspectral imagery to map canopy fuels. Active remote sensors such as
Synthetic Aperture Radar (SAR) that propagate pulses of electromagnetic radiation
and detect the reflective backscatter have shown promise for mapping stand bio-
mass (Rignot et al. 1994 ) so they may be useful for estimating surface fuel models,
crown bulk densities, and canopy dimensions. In Yellowstone National Park in the
USA, Saatchi et al. ( 2007 ) mapped canopy fuel characteristics and Huang et al.
( 2009 ) mapped CWD using SAR and other ancillary data layers. Keramitsoglou
et al. ( 2008 ) fused hyperspectral imagery with ASTER to map fuel types in Greece.
Airborne LiDAR appears to be the most promising remotely sensed product for the
mapping of fuel properties, especially canopy fuel attributes, because it describes the