Human- and lightning-caused wildland fire ignition clusters in British Columbia, Canada
Sean C. P. Coogan A * , Olivia Aftergood A and Mike D. Flannigan B
+ Author Affiliations
- Author Affiliations
A Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2H1, Canada.
B Department of Natural Resource Science, Thompson Rivers University, Kamloops, BC V2C 0C8, Canada.
* Correspondence to: scoogan@ualberta.ca
International Journal of Wildland Fire 31(11) 1043-1055 https://doi.org/10.1071/WF21177
Submitted: 7 December 2021 Accepted: 12 September 2022 Published: 11 October 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Wildland fire is a common occurrence in western Canada, with record-setting area burned recorded in British Columbia (BC) in the past decade. Here, we used the unsupervised machine learning algorithm HDBSCAN to identify high-density clusters of both human- and lightning-caused wildfire ignitions in BC using data from 2006 to 2020. We found that human-caused ignition clusters tended to occur around population centres, First Nations communities, roads and valleys, and were more common in the southern half of the province, which is more populated. Lightning-ignition clusters were generally fewer in number and larger in size than human-caused fires for most hyperparameter settings. There were significant differences (X2 = 1884.8, d.f. = 7, P-value <2.2 × 10−16) in fuels associated with lightning- versus human-caused ignition clusters, with human-ignition cluster fires being more often found within leafless aspen (D1) and ponderosas pine and Douglas fir (C7) fuel types. These high-density clusters highlight regions where the greatest densities of both lightning- and human-caused fires have occurred in the province, thereby identifying regions of potential interest to wildland fire managers, researchers and various communities and industries.
Keywords: Canada, clustering, fuels, HDBSCAN, human-caused fires, interface fires, lightning-caused fires, unsupervised machine learning.
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