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Influence of Airborne Ocularly Assessed Stand Density on Wildfire Escapes and Containment Challenges
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- Author / Creator
- Stack, Andrew P
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The relationship between increasing forest density and increasing wildfire behaviour is well established.
However, in the unique case of initial attack wildfire operations, traditional data collection methods to
measure forest density are both impractical and unsafe. As an alternative approach, aerial ocular
assessment to rapidly determine forest density and its impacts on wildfire behaviour is examined.
Wildfire assessment photography, taken during the initial action phase of wildfire suppression in pure
boreal spruce (Picea mariana) forests, served as an excellent opportunistic dataset to develop a series of
ordinal wildfire behaviour and forest density metrics for testing. This approach explored two scenarios:
1) the effects of forest density on containment escapes, where suppression efforts failed to contain a
wildfire to less than 2 hectares before being classified as “being held,” and 2) the effects of forest
density on wildfire containment challenges, where crews arrived at an incident where the wildfire had
already exceeded 0.5 hectares before suppression began. The ocularly assessed metrics were merged
with recorded weather and Fire Weather Index System values from nearby fire weather stations
(altogether totaling 33 variables) and analyzed using logistic regression in the form of bi-directional
generalized linear models. A parallel study, using LiDAR classification of forest structure in lieu of forest
density assessed in wildfire photographs, was also performed.
Results of this study indicate that in three of the four tested scenarios (ocularly assessed density and fire
escapes, LiDAR classified forest structure and fire escapes, and LiDAR classified forest structure and
containment challenges), forest structure and increasing density were found to be significant with
respect to an increase in wildfire behaviour. Further results also highlight the significance of wildfire
smoke column attributes in scenario outcomes. The selected models all performed well with C-statistics0.75, however lacked predictive capability.
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- Subjects / Keywords
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- Graduation date
- Fall 2024
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Library with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.