Vision for the project
To compile aggregated and harmonised fuel data on a national level using remote sensing technologies, to support a publicly available database benefiting bushfire planning and response.
Research
Funded by the Australian Research Data Commons (ARDC) under the Translational Research Data Challenges Initiative, this research contributes to the compilation and sharing of existing field and remote sensing observations of fuel attributes, such as fuel load/biomass, in a national database. Machine learning models are applied to estimate biomass using various remote sensing products, including optical and Synthetic Aperture Radar (SAR) sensors (Sentinel-2 and Sentinel-1) and full-waveform lidar-based measurements from Global Ecosystem Dynamics Investigation (GEDI) data.
This national database is being used to improve remote sensing products and is publicly available to support other research programs. The national fuel attributes databases directly benefit bushfire planning and response, as the fuel data is readily available for assessing bushfire risk, predicting fire behaviour, informing suppression efforts and planning prescribed burns.
This research is a collaborative effort involving partners such as ANU, TERN, DCCEEW, AFAC, and CSIRO.