Assessing Surface Fuel Hazard in Coastal Conifer Forests through the Use of LiDAR Remote Sensing

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dc.contributor.authorKoulas, Christos
dc.contributor.supervisorNiemann, Olaf
dc.date.accessioned2013-12-17T21:13:32Z
dc.date.available2013-12-17T21:13:32Z
dc.date.copyright2013en_US
dc.date.issued2013-12-17
dc.degree.departmentDepartment of Geography
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractThe research problem that this thesis seeks to examine is a method of predicting conventional fire hazards using data drawn from specific regions, namely the Sooke and Goldstream watershed regions in coastal British Columbia. This thesis investigates whether LiDAR data can be used to describe conventional forest stand fire hazard classes. Three objectives guided this thesis: to discuss the variables associated with fire hazard, specifically the distribution and makeup of fuel; to examine the relationship between derived LiDAR biometrics and forest attributes related to hazard assessment factors defined by the Capitol Regional District (CRD); and to assess the viability of the LiDAR biometric decision tree in the CRD based on current frameworks for use. The research method uses quantitative datasets to assess the optimal generalization of these types of fire hazard data through discriminant analysis. Findings illustrate significant LiDAR-derived data limitations, and reflect the literature in that flawed field application of data modelling techniques has led to a disconnect between the ways in which fire hazard models have been intended to be used by scholars and the ways in which they are used by those tasked with prevention of forest fires. It can be concluded that a significant tradeoff exists between computational requirements for wildfire simulation models and the algorithms commonly used by field teams to apply these models with remote sensing data, and that CRD forest management practices would need to change to incorporate a decision tree model in order to decrease risk.en_US
dc.description.proquestcode0799en_US
dc.description.proquestcode0478en_US
dc.description.proquestemailchristos@koulas.caen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/5088
dc.languageEnglisheng
dc.language.isoenen_US
dc.rights.tempAvailable to the World Wide Weben_US
dc.subjectLiDARen_US
dc.subjectRemote Sensingen_US
dc.subjectWatersheden_US
dc.subjectForesten_US
dc.subjectFire Hazarden_US
dc.subjectFuel Hazarden_US
dc.subjectLight Detection and Rangingen_US
dc.subjectFBPen_US
dc.titleAssessing Surface Fuel Hazard in Coastal Conifer Forests through the Use of LiDAR Remote Sensingen_US
dc.typeThesisen_US

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