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remote sensing

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Remote sensing applications for prescribed burn research

Year of Publication
2024
Publication Type

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking.

Global variation in ecoregion flammability thresholds

Year of Publication
2024
Publication Type

Anthropogenic climate change is altering the state of worldwide fire regimes, including by increasing the number of days per year when vegetation is dry enough to burn. Indices representing the percent moisture content of dead fine fuels as derived from meteorological data have been used to assess geographic patterns and temporal trends in vegetation flammability.

The Power Grid/Wildfire Nexus: Using GIS and Satellite Remote Sensing to Identify Vulnerabilities

Year of Publication
2023
Publication Type

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.

Deterioration of air quality associated with the 2020 US wildfires

Year of Publication
2023
Publication Type
The wildfires of August and September 2020 in the western part of the United States were characterized by an unparalleled duration and wide geographical coverage. A particular consequence of massive wildfires includes serious health effects due to short and long-term exposure to poor air quality.

Projecting live fuel moisture content via deep learning

Year of Publication
2023
Publication Type

Background: Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data; however, almost all these estimate current LFMC (nowcasting models).