The 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. / Graduate / 0799 / 0478 / christos@koulas.ca
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5088 |
| Date | 17 December 2013 |
| Creators | Koulas, Christos |
| Contributors | Niemann, Olaf |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
Page generated in 0.0021 seconds