Predicting continuous variation in forest fuel load using biophysical models: a case study in south-eastern Australia

Abstract

The increasing potential for wildfires in Mediterranean-type landscapes has resulted in pressure to mitigate fire threats. This is typically achieved by strategic reduction of fuel. To prioritise fuel management, it is necessary to understand vegetation dynamics and the relationships between plants and fuel. As the direct measurement of fuel in the field is labour intensive, mapped vegetation classes are typically used as to estimate fuel load. As vegetation properties vary continuously, the error in such estimates can be high. Remotely sensed and biophysical data are commonly used for vegetation classification, but rarely for estimating fuel load. This study investigated how fuel load varied with vegetation composition in an Australian woodland and assessed the potential for using biophysical models to create continuous estimates. Fuel was found to be influenced by species abundance, with some species having a greater contribution to load than others. Fuel was found to be somewhat predictable, with quantities related to fire history and several other biophysical variables. Models were applied to create continuous maps of fuel load; these provided a more precise representation of fuel variation than using discrete classes. Improved maps have the potential to facilitate improved prediction of fire behaviour and assist targeted fuel management.