See Letter to the Editor on this article by Adam et al., 26, 3756–3758. See also Response to the Letter by Bradstock et al., 26, e8–e9.

Extreme fire seasons characterised by very large ‘mega-fires’ have demonstrably increased area burnt across forested regions globally. However, the effect of extreme fire seasons on fire severity, a measure of fire impacts on ecosystems, remains unclear. Very large wildfires burnt an unprecedented area of temperate forest, woodland and shrubland across south-eastern Australia in 2019/2020, providing an opportunity to examine the impact of extreme fires on fire severity patterns. We developed an atlas of wildfire severity across south-eastern Australia between 1988 and 2020 to test (a) whether the 2019/2020 fire season was more severe than previous fire seasons, and (b) if the proportion of high-severity fire within the burn extent (HSp) increases with wildfire size and annual area burnt. We demonstrate that the 2019/2020 wildfires in south-eastern Australia were generally greater in extent but not proportionally more severe than previous fires, owing to constant scaling between HSp and annual fire extent across the dominant dry-forest communities. However, HSp did increase with increasing annual fire extent across wet-forests and the less-common rainforest and woodland communities. The absolute area of high-severity fire in 2019/2020 (∼1.8 M ha) was larger than previously seen, accounting for ∼44% of the area burnt by high-severity fire over the past 33 years. Our results demonstrate that extreme fire seasons are a rare but defining feature of fire regimes across forested regions, owing to the disproportionate influence of mega-fires on area burnt.

Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R² of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.