Remote
Sensing
Applications
For Fire
Management
By: Whitney Adams
GPHY 426
Photo Courtesy of Kara Fuhrmeister
Fire Risk Analysis
Applications
http://www.nps.gov/fire/wildland-fire/learning-center/fire-in-depth/understanding-fire-danger.cfm
Remote Sensing of Live Fuel Moisture
Content (Yerba et al 2013)
LFMC inverse relationship with probability of ignition
 Benefits of remotely sensed LFMC
-Regional based information
-Historical reference data available
http://www.intechopen.com/books/biomass-and-remote-sensing-of-biomass/introduction-to-remotesensing-of-biomass
Difference in the Mid Infrared helps study LFMC
from remotely sensed data
Common Estimations of LFMC
Yerba et. Al 2013
Better estimation of LFMC can lead to more
accurate fire risk assessment.
Remotely Sensed Identification of
High-Cost Fire Danger Areas
Wildland-urban interface, WUI, is defined as:
“the area where houses meet and intermingle with
undeveloped wildland vegetation” (USDA and USDI
2001)
Classification of WUI can help fire managers focus
mitigation efforts and provide safety information
during a fire (Cleve et. al 2008)
Cleve et. al used high resolution aerial photography
to classify WUI in Napa County, California
Compared pixel-based (unsupervised) and objectbased (supervised) classification
Object-based on the left, pixel-based on the right (Cleve et. al
2008)
Object-based: overall accuracy 80% with Kappa 70%
Pixel-based: overall accuracy 62% with Kappa 47%
Fuel Loading Predictions using LiDar
Canopy bulk density, CBD, and crown fuel weight
(CFW) are important metrics for fire risk
assessment. Three-dimensional analysis of these
metrics is very useful to fire managers worried
about ladder fuels and fuel loading. Mapping CBD
and CFW can help fire managers locate areas in
need of fuel reduction. (Skowronski et. al 2011)
Study area: scanning LiDar acquisitions over three
9km^2 located in the Pinelands National Reserve
in New Jersey
Illustrates CBD at various heights. (Skowronski et. al 2011)
Real-Time Fire
Information
Ikhana unmanned airborne system
(UAS) imaging missions (Ambrosia et.
al 2011)
 Unmanned flights- up 24 hour observation periods
 Carries AMS wildfire sensor: 16 bands with
increased thermal band temp. discrimination
 On-board data processing (auto geo-rectification)
 Near real-time data distribution (10 minutes)
Specific Applications:
 Fire perimeter shape file data
 Hot-spot locations
 True color images of fire area
Post Fire Analysis
http://www.fs.fed.us/eng/rsac/baer/Spectral_Reflectivity_Overview.pdf
AMS sensor can produce burn area response
(BAER) assessment imagery (Ambrosia et. al
2011)
BAER image on left, Normalized Burn Ratio image on the right
(Ambrosia et. al 2011)
Literature Review References
Ambrosia, V.G., S. Wegner, T. Zajkowski, D.V. Sullivan, S. Buechel,
F. Enomoto, B. Lobitz, S. Johan, J. Brass, E. Hinkley. 2011. “The
Ikhana unmanned airborne system (UAS) western states fire imaging
missions: from concept to reality (2006-2010).” Geocarto International
26: 85-101.
Cleve, C., M. Kelly, F. Kearns, M. Moritz. 2008. “Classification of the
wildland–urban interface: A comparison of pixel- and object-based
classifications using high-resolution aerial photography. Computers
Environment and Urban Systems 32: 317–326.
Skowronski N.S., k.l. Clark, M. Duveneck, J. Hom. 2011. “Threedimensional canopy fuel loading predicted using upward and
downward sensing LiDAR systems.” Remote Sensing of Environment
115, 703–714 (2011).
Yebra, M., P. E. Dennison, E. Chuvieco, D. Riaño, P. Zylstra, E. R.
Hunt, F. M. Danson, Y. Qi, and S. Jurdao. 2013. “A global review of
remote sensing of live fuel moisture content for fire danger
assessment: moving towards operational products.” Remote Sensing
of Environment 136: 455–468.
Other References
USDA and USDI. 2001. “Urban wildland-interface communities within
vicinity of federal lands that are at high risk from wildfire.” Fed.
Registry 66: 751-777.
Eureka Fire Photo Courtesy of Chet Smith