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 influence of forest treatments on wildfire effects is challenging to interpret. This is, in part, because the impact forest treatments have on wildfire can be slight and variable across many factors. Effectiveness of a treatment also depends on the metric considered.
Background: Fire research and management applications, such as fire behaviour analysis and emissions modelling, require consistent, highly resolved spatiotemporal information on wildfire growth progression.
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).
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.
The Composite Burn Index (CBI) is commonly linked to remotely sensed data to understand spatial and temporal patterns of burn severity. However, a comprehensive understanding of the tradeoffs between different methods used to model CBI with remotely sensed data is lacking.
Fire location and burning area are essential parameters for estimating fire emissions. However, ground-based fire data (such as fire perimeters from incident reports) are often not available with the timeliness required for real-time forecasting.
Background: Remotely sensed burned area products are critical to support fire modelling, policy, and management but often require further processing before use. Aim: We calculated fire history metrics from the Landsat Burned Area Product (1984–2020) across the conterminous U.S.
This study examines the post-fire biogeophysical and biochemical dynamics after several high-severity wildfires that occurred in mixed conifer and ponderosa pine forest types in the Sierra Nevada and Klamath Mountains regions between 1986 and 2017.
As the frequency and size of wildfires increase, accurate assessment of burn severityis essential for understanding fire effects and evaluating post-fire vegetation impacts.