Risk Assessment and Analysis

Microbes inhabiting the above- and belowground tissues of forest trees and soils play a critical role in the response of forest ecosystems to global climate change. However, generalizations about the vulnerability of the forest microbiome to climate change have been challenging due to responses that are often context dependent.

Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires.

Public agencies and organizations often deliver financial assistance through cost sharing, in which recipients contribute some portion toward total costs. However, cost sharing might raise equity concerns if it reduces participation among populations with lower incomes.

Extreme heat and wildfire smoke events are increasingly co-occurring in the context of climate change, especially in California. Extreme heat and wildfire smoke may have synergistic effects on population health that vary over space.

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.

The effects of wildfire on the power grid are a recurring concern for utility companies who need reliable information about where to prioritize infrastructure hardening. Though there are existing data layers that provide measures of burn probability, these models predominately consider long-term climate variables, which are not helpful when analyzing current season trends.

Aim

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Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data.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.