Background Wildland fires are fundamentally landscape phenomena, making it imperative
to evaluate wildland fire strategic goals and fuel treatment effectiveness at large spatial and …

Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire
modeling uncertainties remain largely unquantified in the literature, mainly due to computing …

Geospatial fire behaviour and fire hazard simulators, fire effects models and smoke emission
software commonly use standard fuel models in order to simplify data collection and the …

Understanding the role of fire in the water and carbon cycles is crucial for understanding the
Earth's system. Remote sensing is a valuable tool for this purpose as it covers large areas …

Computational models for natural hazards usually require a large number of input
parameters that affect the model outcome in a complex manner. The sensitivity of the input …

A growing number of wildfires around the world have been resulting in severe post-fire
effects. Understanding the accurate distribution of the wildfire burn severity is particularly …

Environmental models involve inherent uncertainties, the understanding of which is required
for use by practitioners. One method of uncertainty quantification is global sensitivity …

An understanding of variation in dead fuel moisture content (FMC) is essential for accurate
predictions of wildfire risk, particularly in productive wet forests where FMC is a primary …

Numerical simulations of wildfire spread can provide support in deciding firefighting actions
but their predictive performance is challenged by the uncertainty of model inputs stemming …

Simulation is used to predict the spread of a wildland fire across land in real-time.
Nevertheless, the large uncertainties in these simulations must be quantified in order to …