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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
The use of small Unmanned Aircraft Systems (sUAS) for obtaining wildland imagery has enabled the production of more accurate data regarding the effects of fire on forested land. This increase in precision enables accurate detection of trees from hyperspatial imagery, and thus the calculation of canopy cover. When pre-fire data is compared with post-fire data from existing canopy cover products such as the LANDFIRE project, a measure of tree mortality, which is a measure of burn severity, can be calculated from the difference between the two. A mask region-based convolutional neural network (MR-CNN) was trained to classify trees as groups of pixels from a hyperspatial orthomosaic. The tree classification is summarized at 30m in a canopy cover raster. Canopy reduction allows the mapping of burn severity while also identifying where surface, passive, and active crown fire occurred within the observed region. The calculated canopy cover derived from hyperspatial imagery was found to be significantly more accurate when compared with LANDFIRE canopy cover in all observed regions. The hyperspatially derived canopy cover reflected visual observations from respective sUAS imagery and ground truthing.Download this Resource
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Copyright NotesNon-exclusive right to publish on file at NNU Library. Available on request.
KeywordsMachine learning, Wildfire mapping, small Unmanned Aircraft Systems, sUAS
Degree AwardedBachelor of Arts
Awarding InstitutionNorthwest Nazarene University
Committee MembersBenjamin Fischer, Barry L. Myers
Supervisor or ChairDale A. Hamilton
Peer Reviewed?No
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NNU Computer Science Theses
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