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Description / Abstract
Submitted to the Department of Mathematics and Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Science
Many different machine-learning algorithms have previously been used to map wildland fire effects using satellite imagery from the Landsat satellites with 30-meter spatial resolution. Small-unmanned aircraft systems (sUAS) can capture images with five-centimeter (hyperspatial) resolution. Consequently, the amount of data needing to be stored and analyzed significantly increased. There is a need for more tools that focus on extracting actionable knowledge from hyperspatial imagery and providing timely information for management of wildland fires. This analysis shows that the accurate mapping of fire effects from hyperspatial imagery increased from 56.62% to 93.16% for Burn Extent and 28.4% to 95.94% for Biomass Consumption. The classifier developed to do this analysis uses a support vector machine (SVM) to determine the burn severity by classifying image pixels into canopy crown, surface vegetation, white ash, and black ash. The use of sUAS to map burn severity creates another problem. The flight time of sUAS allows them to have the capability only to map small fires. Classifications were modified to utilize machine-learning algorithms. Images, obtained from Landsat, are analyzed using the new classification. Implementing the new classification allows, not only small fires but, large fires to be modeled as well.Download this Resource
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File Size Small - 1.7 MB
Copyright NotesNon-exclusive right to publish on file at NNU Library. Available on request.
Keywordsmachine-learning algorithms, burn severity mapping, support vector machine
Degree AwardedBachelor of Science
Awarding InstitutionNorthwest Nazarene University
Committee MembersBenjamin Gall, Barry L. Myers
Supervisor or ChairDale Hamilton
Peer Reviewed?No
Collections With This Resource
NNU Computer Science Theses
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