Forest disturbances such as wildfires can dramatically alter forest structure and composition, increasing the likelihood of ecosystem changes. Up-to-date and accurate measures of post-disturbance forest recovery in managed forests are critical, particularly for silvicultural planning.
Fuel treatments are commonly applied to increase resilience to wildfire in dry and historically frequent-fire forests of western North America. The long-term effects of fuel treatments on forest structure, fuel profiles (amount and configuration of fuels), and potential wildfire behavior are not well known relative to short-term effects.
Wildfires can be devastating for social and ecological systems, but the recovery period after wildfire presents opportunities to reduce future risk through adaptation.
In response to increasing fire, fuel-reduction treatments are being used to minimize large fire risk. Although biocrusts are associated with reduced cover of fire-promoting, invasive grasses, the impact of fuel-reduction treatments on biocrusts is poorly understood.
The science of post-wildfire debris flows has evolved rapidly over the past few decades as wildfires have become more frequent, larger, and more severe, impacting human health and the developed landscape. Post-wildfire debris flow science encompasses a wide variety of fields, including hydrology, geomorphology, engineering, remote sensing, atmospheric science, ecology, and data science.
As a result of climate change and past management practices, wildfires are becoming larger and occurring more frequently than ever before in the Western U.S. In order to mitigate the effects of this growing threat, fire management agencies such as the U.S.
Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data.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.
The behaviour of wildland fires and the dispersion of smoke from those fires can be strongly influenced by atmospheric turbulent flow. The science to support that assertion has developed and evolved over the past 100+ years, with contributions from laboratory and field observations, as well as modelling experiments.
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.
Spatial and temporal variation in fire characteristics—termed pyrodiversity—areincreasingly recognized as important factors that structure wildlife communitiesin fire-prone ecosystems, yet there have been few attempts to incorporatepyrodiversity or post-fire habitat dynamics into predictive models of animaldistributionsandabundancetosupportpost-firemanagement.Weusetheblack-backed woodpecker—a s