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The McArthur grassland and forest fire danger indices, widely used in Australia, predict six fire danger classes from 'Low-Moderate' to 'Catastrophic.' These classes were linked to the rate of fire spread and difficulty of suppression. However, the lack of rate of fire spread data, especially for elevated fire danger classes, has hindered improvement of the McArthur methodology or an alternate approach. We explored the relationship between fire danger classes and burned areas (derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument) within six climate zones during the 2000–2016 Australian fire seasons. A negative binomial linear regression model was used to explore this relationship. The fire danger classes demonstrated a corresponding increase in burned area from 'Low-Moderate' to 'Very High' classes in Australia's inland regions. The elevated fire danger classes did not contribute to this trend. In coastal regions, the satellite-derived burned area showed no relationship between fire danger classes and satellite-derived burned area. We used accumulated burned area from the daily MODIS product, which could be subjected to lagged detection as observed in the Kilmore East fire. Thus, the satellite-derived total burned area may not be a suitable metric for informing the McArthur fire danger classes across Australia. We explored the relationship of the McArthur fire danger classes with the satellite-derived daily burned area The findings suggest that satellite-derived daily burned area may not be a suitable metric for informing McArthur fire danger classes across Australia because it can be subjected to lagged detection. [ABSTRACT FROM AUTHOR]

Studies using remote sensing data for fire danger prediction have primarily relied on fire ignitions data to develop fire danger indices (FDIs). However, these data may only represent conditions suitable for ignition but may not represent fire danger conditions causing escalating fire size. The fire-related response variable's scalability is a key factor that forms a basis for an FDI to include a broader range of fire danger conditions. Remote sensing derived fire size is a scalable fire characteristic encapsulating all possible fire sizes that previously occurred in the landscape, including extreme fire events. Consequently, we propose a new FDI that uses remote sensing derived fire size as a response variable. We computed fire sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument burned area. We applied random forest (RF) and logistic regression (LR) to develop the FDI for Australia. RF models performed better than LR, and the higher predicted probabilities demonstrated higher chances for ignited fires to be escalated to larger fire sizes at a regional scale across Australia. However, the predicted probabilities cannot be related to the specific range of fire sizes due to data limitations. Further research with higher temporal and spatial resolution data of both the response and predictor variables can help establish a better relationship between a specific range of fire sizes and the predicted probabilities. [ABSTRACT FROM AUTHOR]