Fire spread models are used by Australian fire agencies to forecast the speed, direction and intensity of vegetation fires. Whilst there are a number of variables in grassland models, biomass (fuel weight) and moisture content in the fuel are important factors.

The Country Fire Authority of Victoria (CFA) has led research into assessing the annual drying of grasslands, a process known as curing. CFA use both satellite and ground observers to estimate curing, which are used to support decision making for Fire Restrictions, Total Fire Bans and fire operations. Current issues include the estimation of curing in mixed landscapes, the validation of satellite derived data where there is no ground observer network, pasture green up after summer rain and the creation of high resolution data sets at experimental fires.

At an October 2014 meeting at the University of Melbourne, interested parties from CFA, CSIRO and RISER[1] discussed opportunities for research into grassland fire model inputs using a range of sensing devices including existing static ground based sensors and un-manned aerial vehicles (UAV). A project to capture a multi-date colour, infra-red and Normalised Difference Vegetation Index (NDVI) image collection from a UAV platform in conjunction with ground observations was agreed. The project acquired multiple images at Moorooduc and Scoresby near Melbourne during the 2014-15 summer.

In generating the UAV and field observation data sets, a number of operational challenges were encountered. They included establishment of survey quality ground control points, sensor types and in-field limitations such as power back up and environmental conditions. This paper documents these challenges and provides a checklist for anyone interested in capturing similar collections in the future.

[1] RISER: Resilient Information System for Emergency Response is multi-sponsored research project at the University of Melbourne, Department of Infrastructure Engineering