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Problem Statement

I propose the development of algorithms, software and image obtainment methods which will improve mapping of wildland fire severity.  This research will enable the acquisition, storage and analysis of hyperspatial multi-spectral UAS imagery, utilizing image processing and machine learning techniques to map wildland fire severity in a more responsive, affordable, and safe manner.

Millions of acres of American wildlands are consumed by fire each year, with suppression costs approaching $2 billion annually (NIFC, 2015).  High intensity wildland fires contribute to post fire erosion, soil loss, flooding events and loss of timber resources which result in negative impacts on wildlife habitat, ecosystem resilience, infrastructure, and recreational opportunities.  

Current methods for acquiring imagery, which can be utilized for assessing fire effects, rely on satellites, which in the case of Landsat only have a temporal resolution of 16 days and a spatial resolution of 30 meters (NASA, 2015).  Much of the body of fire history is contained in fire atlases, which omit the spatial extent of small and moderate sized fires (Morgan, 2014).  Accurate historical record of fire history is necessary in order to determine departure of current fire frequency from historic fire frequency, a key metric for determining ecosystem resilience (WFLC, 2014).  Omission of the spatial extent of these small and moderate sized fires, which represent the most ecologically diverse of all fires, adversely affects the accuracy of fire frequency departure metric, hampering its ability to accurately reflect ecosystem resilience.

New advances in Unmanned Aircraft System (UAS) capabilities can enable the acquisition of imagery with a spatial resolution of centimeters and temporal resolution of minutes.  Additionally, UAS imagery can be obtained at one tenth the operational cost associated with manned aircraft (Quirk, 2015).  Utilization of UAS for remotely sensing wildland fire effects will allow the assessment of wildland fire effects in a much more responsive, affordable and safe manner than is possible with current methods.

 

References

Hamilton, D., & Hann, W. (2015). Mapping landscape fire frequency for fire regime condition class. Large Fire Conference, Missoula, MT.

Morgan, P., Heyerdahl, E., Miller, C., Wilson, A., & Gibson, C. (2014). Northern rockies pyrogeography: An example of fire atlas utility. Fire Ecology, 10(1), 14.

National Aeronautics and Space Administration (NASA).Landsat 7. Available: http://geo.arc.nasa.gov/sge/landsat/l7.html

National Interagency Fire Center (NIFC).Federal firefighting costs. . Available: https://www.nifc.gov/fireInfo/fireInfo_documents/SuppCosts.pdf.

Quirk, B.UAS for terrestrial monitoring, Workshop on UAS for Invasive Plant Monitoring Assessment and Management, NASA Ames Research Center, Mountain View, CA, 6/25/2015,.

Wildland Fire Leadership Council (WFLC) (2014). National Cohesive Wildland Fire Management Strategy. www.forestsandrangelands.gov