Wildfires in the western United States (US) are increasingly expensive, destructive, and deadly. Reducing wildfire losses is particularly challenging when fires frequently start on one land tenure and damage natural or developed assets on other ownerships.
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.
Climate change is expected to increase fire activity in many regions of the globe, but the relative role of human vs. lightning-caused ignitions on future fire regimes is unclear. We developed statistical models that account for the spatiotemporal ignition patterns by cause in the eastern slopes of the Cascades in Oregon, USA.
Creating a safe workplace for wildland firefighters has long been at the centre of discussion for researchers and practitioners. The goal of wildland fire safety research has been to protect operational firefighters, yet its contributions often fall short of potential because much is getting lost in the translation of peer-reviewed results to potential and intended users.
Growing human and ecological costs due to increasing wildfire are an urgent concern in policy and management, particularly given projections of worsening fire conditions under climate change. Thus, understanding the relationship between climatic variation and fire activity is a critically important scientific question.
Wildland-urban interfaces (WUIs) are areas where urban settlements and wildland vegetation intermingle, making the interaction between human activities and wildlife especially intense. Their relevance is increasing worldwide as they are expanding and are associated with fire risk.