Risk assessment for wildland fire aerial detection patrol route planning in Ontario, Canada
Colin B. McFayden A G , Douglas G. Woolford B , Aaron Stacey C , Den Boychuk D , Joshua M. Johnston E , Melanie J. Wheatley A and David L. Martell F
+ Author Affiliations
- Author Affiliations
A Ontario Ministry of Natural Resources and Forestry, Aviation, Forest Fire and Emergency Services, Dryden Fire Management Centre, 95 Ghost Lake Road, PO Box 850, Dryden, ON P2N 2Z5, Canada.
B Department of Statistical and Actuarial Sciences, University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada.
C Ontario Ministry of Natural Resources and Forestry, Aviation, Forest Fire and Emergency Services, 300 Water Street, South Tower, Peterborough, ON K9J 3C7, Canada.
D Ontario Ministry of Natural Resources and Forestry, Aviation, Forest Fire and Emergency Services, 400–70 Foster Drive, Sault Ste Marie, ON P6A 6V5, Canada.
E Canadian Forest Service, Great Lakes Forestry Centre, 1219 Queen Street E, Sault Ste Marie, ON P6A 2E5, Canada.
F Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada.
G Corresponding author: Email: colin.mcfayden@ontario.ca
International Journal of Wildland Fire 29(1) 28-41 https://doi.org/10.1071/WF19084
Submitted: 9 June 2019 Accepted: 23 October 2019 Published: 5 December 2019
Journal Compilation © IAWF 2020 Open Access CC BY-NC-ND
Abstract
This study presents a model developed using a risk-based framework that is calibrated by experts, and provides a spatially explicit measure of need for aerial detection daily in Ontario, Canada. This framework accounts for potential fire occurrence, behaviour and impact as well as the likelihood of detection by the public. A three-step assessment process of risk, opportunity and tolerance is employed, and the results represent the risk of not searching a specified area for the detection of wildland fires. Subjective assessment of the relative importance of these factors was elicited from Ontario Ministry of Natural Resources and Forestry experts to develop an index that captures their behaviour when they plan aerial detection patrol routes. The model is implemented to automatically produce a province-wide, fine-scale risk index map each day. A retrospective analysis found a statistically significant association between points that aerial detection patrols passed over and their aerial detection demand index values: detection patrols were more likely to pass over areas where the index was higher.
Additional keywords: decision-making, decision support systems, forest fire detection, uncertainty, wildfire detection.
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