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).
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.
High temporal resolution information on burnt area is needed to improve fire behaviour and emissions models. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly and active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the timing of burnt area for 16 large wildland fires.
Evaluating the influence of observed daily weather on observed fire-related effects (e.g. smoke production, carbon emissions and burn severity) often involves knowing exactly what day any given area has burned.
In response to climate change and other threatening processes there is renewed interest in the role of refugia and refuges. In bioregions that experience drought and fire, micro-refuges can play a vital role in ensuring the persistence of species.