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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 Arts
A variety of machine learning algorithms have been used to map wildland fire effects, but previous attempts to map post-fire effects have been conducted using relatively low-resolution satellite imagery. Small unmanned aircraft systems (sUAS) provide opportunities to acquire imagery with much higher spatial resolution than is possible with satellites or manned aircraft. This effort investigates improvements achievable in the accuracy of post-fire effects mapping with machine learning algorithms that use hyperspatial (sub-decimeter) UAS imagery. Spatial context using a variety of texture metrics were also evaluated to determine the inclusion of spatial context as an additional input to the analytic tools along with the three-color bands. This analysis shows that the addition of texture as an additional fourth input increases classifier accuracy when mapping post-fire effects.Download this Resource
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Copyright NotesNon-exclusive right to publish on file at NNU Library. Available on request.
Keywordshyperspatial imagery, small unmanned aircraft systems, sUAS, wildfire mapping
Degree AwardedBachelor of Arts
Awarding InstitutionNorthwest Nazarene University
Committee MembersStephen Riley, Barry L. Myers
Supervisor or ChairDale Hamilton
Peer Reviewed?No
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NNU Computer Science Theses
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