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dc.contributor.advisorChao, Haiyang
dc.contributor.authorGoyer, Jacksen
dc.date.accessioned2023-06-25T20:18:56Z
dc.date.available2023-06-25T20:18:56Z
dc.date.issued2022-12-31
dc.date.submitted2022
dc.identifier.otherhttp://dissertations.umi.com/ku:18645
dc.identifier.urihttps://hdl.handle.net/1808/34434
dc.description.abstractThis thesis focuses on the generation of a new grass fire aerial image dataset and development of novel methods for near-infrared (NIR) imagery-based fire front identification and fire depth estimation using small unmanned aircraft systems (sUAS). The procedure for collection and creation of the Grass Fire Front and near-Infrared (NIR) and Thermal Imagery (GRAFFITI) dataset is introduced first including two levels of data: synced raw thermal and red, green and near-infrared (RGNIR) image pairs and processed image pairs of the same overlapping field-of-view. A novel NIR imagery-based fire detection and fire front identification algorithm is then proposed and validated against manually labeled ground truth, using the GRAFFITI dataset. A comparative study is further performed on the problem of grass fire front location and flame depth estimation using thermal and NIR imagery. Finally, recommendations are made to future researchers who are interested in wildland fire sensing using thermal or NIR imagery.
dc.format.extent80 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsCopyright held by the author.
dc.subjectAerospace engineering
dc.subjectComputer science
dc.subjectfire front identification
dc.subjectfire image dataset
dc.subjectfire metric measurements
dc.subjectnear infrared imagery
dc.subjectsUAS fire detection
dc.titleNIR Imagery-based Grass Fire Detection and Metrics Measurement using Small UAS
dc.typeThesis
dc.contributor.cmtememberTaghavi, Ray
dc.contributor.cmtememberMcLaughlin, Craig
dc.thesis.degreeDisciplineAerospace Engineering
dc.thesis.degreeLevelM.S.
dc.identifier.orcid
dc.rights.accessrightsopenAccess


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