During August 2021, a wildfire outbreak in Evia, Greece’s second largest island, resulted in a major environmental and economic crisis. Apart from biodiversity and habitat loss, the disaster triggered a financial crisis because it wiped out wood-productive forests and outdoor areas that attract visitors.
Background Maximizing the effectiveness of fuel treatments at landscape scales is a key research and management need given the inability to treat all areas at risk from wildfire. We synthesized information from case studies that documented the influence of fuel treatments on wildfire events.
As contemporary wildfire activity intensifies across the western United States, there is increasing recognition that a variety of forest management activities are necessary to restore ecosystem function and reduce wildfire hazard in dry forests. However, the pace and scale of current, active forest management is insufficient to address restoration needs.
Background: The risk of destructive wildfire on fire-prone landscapes with excessive fuel buildup has prompted the use of fuel reduction treatments to protect valued resources from wildfire damage. The question of how to maximize the effectiveness of fuel reduction treatments at landscape scales is important because treating an entire landscape may be undesirable or unfeasible.
Wildfires pose a large and growing threat to communities across California, and understanding fire vulnerability and impacts can enable more effective risk management. Government hazard maps are often used to identify at-risk areas, but hazard zones and fire experience may have different implications for communities.
Since the 1930s, California’s Sierra Nevada has warmed by an average of 1.2∘∘C. Warming directly primes forests for easier wildfire ignition, but the change in climate also affects vegetation species composition.
Recent increases in Douglas-fir (Psuedotsuga menziesii var. menziesii) mortality in the Klamath Mountains ecoregion raise concerns about the long-term resilience of Douglas-fir in the ecoregion and increased potential for uncharacteristic wildfire.
Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data.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.
Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades.
Soil moisture conditions are represented in fire danger rating systems mainly through simple drought indices based on meteorological variables, even though better sources of soil moisture information are increasingly available.