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 varie..