We calculated fire leverage (the unit reduction in unplanned fire area resulting from one unit of previous fire) for six global case studies. Two showed leverage and four did not, confirming that prescribed fire treatment only reduces unplanned fire area in certain regions.

Operational fire behaviour models are based on several uncertain input parameters that describe the fire environment. We propose using global sensitivity analysis to reduce model complexity, and use optimised sensitivity derivative enhanced sampling together with random-start Halton sequences to produce an ensemble of model runs. Our proposed method improves standard Monte Carlo simulation errors with a lower computational overhead.

For improved assessment of grassland curing (senescence) in Victoria, south-eastern Australia, a new satellite curing model was developed, based on historical satellite and ground-based observations. With use of the new satellite model, an integrated model was developed, combining near-real-time satellite data with weekly curing observations from the ground.

Live fuel moisture content (LFMC), leaf ignition, and their relationships with environmental variables were assessed for six Patagonian species of Argentina. LFMC was inversely correlated with leaf ignition and was positively correlated with environmental variables in species showing low LFMC. Results help understand fire behaviour of Patagonian forests, and are useful for comparison with other fire-prone ecosystems.

Rainforests were once thought to be highly sensitive to fire, but recent research suggests otherwise. We found that juveniles of three rainforest species, including the critically endangered Wollemi pine, resprouted after fire. Resprouting may have helped these species to persist in fire-prone landscapes.

We quantified fuel treatment effects on surface fuel load, vegetation cover and forest structure before and after fire-only and mechanical treatments in California. We suggest a second-entry treatment 8 years after the initial treatment to reduce accumulating fuel loads, understorey vegetation and small-diameter trees in order to maintain and extend the intended effectiveness of treatments.

Using a spatial point process model, we predicted that lightning-caused wildfires may increase faster than human-caused fires in response to climate change in the Lake Tahoe Basin, USA. However, such prediction was highly uncertain, highlighting the importance of multi-model inference and uncertainty analysis in fire occurrence studies.

Regression models were used to predict average burnt areas and assess the relative influence of weather, vegetation and topography in the distribution of wildfires in central Quebec. Interactions between those factors proved important and allowed accurate predictions of burnt areas at a resolution of 350 km² and 11 years.

Fire regimes are changing worldwide. Here, we introduce a novel approach for assessing changes in fire regimes in a Mediterranean area from the modelling of different fire typologies according to their dominant spread pattern. This can help us to better understand fire impacts and it can lead to new ways of predicting global change effects on fire regimes.

The study proposed a framework for reconstructing time series of burned areas in the taiga–steppe transition zone of northern Mongolia using MODIS composites. The synergistic use of spring MODIS composite (Julian dates, JD 97–177), logistic regression and MODIS active fire product successfully reconstructed long-term burned areas in the taiga–steppe transition zone.

We examined effects of wildfire and topography on soil N availability in a Chinese boreal forest. Topography greatly influenced the distribution of soil N availability. However, wildfire altered this pattern by increasing soil available N, suggesting that wildfire exerts a fertilisation effect shortly after fire and decreases topographic influences.