Wildfire risk is increasing all over the world, particularly in the western United States and the communities in wildland-urban interface (WUI) areas are at the greatest risk of fire.
Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown.
As wildfires increase in both severity and frequency, understanding the role of risk saliency on human behaviors in the face of fire risks becomes paramount. While research has shown that homebuyers capitalize wildfire risk following a fire, studies of the role that risk saliency plays on residential development is limited.
We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire.
Due to the associated fire risk, the wildland–urban interface (WUI) has drawn the attention of researchers and managers from a range of backgrounds. From a land management point of view, it is important to identify the WUI to determine areas to prioritise for fire risk prevention.
As wildfire risks have elevated due to climate change, the health risks that toxicants from fire smoke pose to wildland firefighters have been exacerbated. Recently, the International Agency for Research on Cancer (IARC) has reclassified wildland firefighters’ occupational exposure as carcinogenic to humans (Group 1).