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Description / Abstract
Submitted to the Department of Mathematics & Computer Science in partial fulfillment of the requirements for the degree of Bachelor of Science
Using machine learning algorithms on imagery obtained from small unmanned aircraft systems (sUAS) has been an efficient and accurate way to collect data on postfire forests. This effort applies machine learning to obtain useful information about postfire forests. It uses a mask region-based convolutional neural network (MR-CNN) to as well as a support vector machine (SVM) to tree mortality as well as burn extent. Using machine learning helps automated the process while still having accurate data. Having fast and accurate process to calculate the damage done by a fire helps land managers make a quick and calculated response to aid in forest rehabilitation.
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Non-exclusive right to publish on file at NNU Library. Available on request.
KeywordsMachine learning, Hyperspatial data, Burn severity
Degree AwardedBachelor of Science
Awarding InstitutionNorthwest Nazarene University
Committee MembersDavid Hille, Barry Myers
Supervisor or ChairDale Hamilton
Peer Reviewed?No
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NNU Computer Science Theses
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