Vision for the project

to develop a machine learning model for emulating the output of Australian Flammability Monitoring System (AFMS) at higher spatial resolution (approx. 20 m).  


Funded by Natural Hazards Research Australia, this research employs machine learning modelling coupled with reflectance data from the Sentinel-2 satellite mission to estimate Fuel Moisture Content (FMC).  

This project involved training the model on the output of the AFMS and validating its results using ground fuel samples.  

The product of this research can enable the consultation of near-real-time FMC values for vegetation across Australia.  

Future research may involve gathering FMC samples tailored specifically for training models that utilise Sentinel-2 data, eliminating the necessity of emulating coarser resolution models as done in this project. 

Chief Investigators

Project Team

Mr Gianluca Scortechini