10ARSPC Template for Proceedings on CD ROM

Fuel load and fuel moisture content are crucial parameters in estimating bushfire risk. However, current measures of these parameters are not spatially distributed within fire districts; the district-level indices are derived from pointbased estimates at sparsely located stations. In addition, they are based on meteorological inputs alone, rather than observations, and are the major sources of error in assessing bushfire risk. Remotely-sensed spectral indices can provide a direct, spatially-distributed estimate of fuel load and moisture content. In this study, we investigate timeseries of three remotely-sensed spectral indices land-surface-temperature-to-air-temperature ratio (LST/Ta), crop water stress index (CWSI) and normalized difference vegetation index (NDVI) in the period preceding the Black Saturday bushfires, and assess their ability to predict bushfire risk. We also consider timeseries of LST/Ta with LST derived from microwave observations (LSTTB/Ta). We find that, while timeseries of the existing method of measuring fuel load and moisture content are noisy and regularly overpredict bushfire risk, the remotely-sensed indices all display clear seasonal behaviour, with distinct changes immediately prior to Black Saturday suggesting fuel load increased and fuel moisture content decreased. These changes were not evident in a control timeseries for a low-risk bushfire season. LSTTB/Ta, while noisier than the LST/Ta timeseries, replicates the general behaviour of the LST/Ta series and is found to be a useful tool for estimating fuel moisture content in cloudy or smoky conditions.