This project uses cutting-edge technology to produce spatial information on fuel condition, fire hazard and impact. Such information can support a wide range of fire risk management and response activities such as hazard reduction burning and pre-positioning firefighting resources and, in the longer term, the new National Fire Danger Rating System (NFDRS). The project is part of the Bushfire & Natural Hazards CRC.
The first phase of this project (2014-2017) involved the parallel investigation of a number of promising data sources and methods that can be categorised as either ‘in-field’ or ‘national-scale’ methods. In-field methods provide detailed information at the plot scale of metres to hectares. They provide more accurate and spatially concentrated measurements but can also be relatively costly – examples investigated previously in this project include on-ground networks of field sensors measuring grass curing or fuel moisture content (FMC), and automated ground-based LiDAR laser scanning for fuel characterisation. National-scale methods are generally derived from already available satellite imagery and other spatial data. Two such methods were successfully developed in this project: the Australian Flammability Monitoring System (AFMS), and the High-resolution Fire Risk and Impact (HiFRI) model-data fusion framework. The former was implemented at national-scale, whereas the latter was tested for a smaller region but can be applied anywhere in Australia.
Generally, information derived from the national-scale methods appear to represent better return on investment and generated greater interest among end users (Yebra et al 2016c). They therefore appear to have greater utilization potential than in-field methods, which require careful consideration of the cost and the representativeness of the sample locations. However, end users did recognise the importance of in-field methods as part of the verification, acceptance and tuning of large-scale methods. Moreover, adoption of some in-field technologies was considered more likely to occur once data acquisition and analysis technologies become cheaper.
Over the next three years (2017-2020), this research project will focus on increasing the understanding, reliability and long- term continuity of the AFMS, and through this, its acceptance and adoption. In addition, a small number of promising, low-cost in-field techniques will continue to be investigated to improve their cost/benefit ratio and utility.
1. AFMS understanding and reliability. The algorithm we have developed to map FMC for Australia is physically-based using reflectance data from MODIS satellite and radiative transfer models (RTM) Look-up Table inversion techniques. The evaluation of the algorithm for different vegetation types in Australia (Yebra et al. 2016a) has shown that better description of the links between vegetation biophysical and structural properties and leaf reflectance is a critical need, especially for sclerophyll forests. This is because existing RTMs that describe vegetation chemical, structural and optical properties are mainly derived for European vegetation types. Further advancement towards physically-based satellite FMC monitoring methods can be realised through the development of RTMs suited for Australian temperate sclerophyll forest. Field measurement of leaf spectra and corresponding leaf biochemical traits of key species will be essential to that end and will be undertaken as part of the project.
2. AFMS long-term continuity. The current AFMS relies on MODIS instruments on board the Terra and Aqua satellites. However, the expected lifetime of the Terra and Aqua satellites has already been exceeded, and at some point in the not-too-distant future they will become inoperative. To support a AFMS continuity strategy we will evaluate the feasibility and relative benefits of using alternative satellites, in collaboration with Geoscience Australia and Bureau of Meteorology. The most promising candidate data sources are the geostationary Japanese Himawari-8 satellite, the European Sentinel-2 and the Landsat and VIIRS satellites. Apart from ensuring data continuity and redundancy, the use of these satellites may also create the opportunity to increase the temporal and/or spatial resolution of the AFMS. The benefits of this will also be investigated.
3. Towards comprehensive characterization of flammability. The AFMS provides the first Australia-wide product of flammability from satellite estimates of live FMC (Yebra et al. 2016b). The flammability index was adjusted using a continuous logistic probability model between fire occurrence and live FMC. However, live FMC is only one of the variables that influences fire occurrence, and therefore the importance of other factors (e.g. fire weather, dead FMC, total fine live and dead fuel load, and ignition) should also be considered for a comprehensive characterization of flammability, where possible. For example, weather observations and forecasts are available from Bureau of Meteorology, method of Matthews et al. (2006) can be used to predict dead FMC and Quan et al. (2016) to estimate grassland aboveground biomass. We will quantitatively integrate these additional factors by including them in probabilistic prediction framework. Such an approach will provide a more observation-based and comprehensive assessment of flammability, where current national approaches (e.g. the MacArthur-type methods) are conceptual and focused on meteorological variables.
4. Low-cost technology to monitor fuel condition on Defence Lands. As this project has already demonstrated, remote sensing techniques for fire risk assessment have progressed rapidly in recent years. These offer the potential for land managers, like the Department of Defence, to access broad-area information that underpins key decisions for fuel management and conduct of training activities with potential to start bushfires. This approach is particularly useful for remote areas and restricted access areas used by Defence. However, satellite data may not provide Defence managers with ready access to all necessary up to date data or the necessary spatial or temporal resolution, and field assessment is still needed to build understanding and confidence in satellite-derived information. To that end, low-cost ground-based techniques such as fuel depth gauges or automatic cameras may provide a more immediate method for managers to assess fuel condition (e.g. FMC, fuel structure and fuel load). Consequently, there is a need to assess the real and ongoing cost of providing fuel-related fire risk information using both satellite and field techniques against the suitability of the data for Defence and other lands, and its potential common good value. It is anticipated that the most powerful and robust flammability assessment system will include a combination of both (spatial) satellite and (in situ) field monitoring methods.
|Project team||Dr. Marta Yebra
Prof. Albert van Dijk
A/Prof. Geoff Cary
|End users||(in no particular order):
|Project start date||1 July 2017|
|Project end date||30 June 2020|
- Quan, X., He, B., Yebra, M., Yin, C., Liao, Z., Xueting, Z. 2017. A radiative transfer model-based method for the estimation of grassland aboveground biomass. IEEE International Journal of Applied Earth Observation and Geoinformation, 54, 159-168.
- Matthews S, McCaw WL (2006) A next-generation fuel moisture model for fire behaviour prediction. Forest Ecology and Management. 234, S91–S91.
- Yebra, M., Quan, X., van Dijk, A., Cary, G. 2016a Monitoring and forecasting fuel moisture content for Australia using a combination of remote sensing and modelling. Proceedings for the 5th International Fire Behaviour and Fuels Conference. April 11-15, 2016, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA.
- Yebra, M., Quan, X., van Dijk, A., Cary, G. 2016b. The Australian flammability system. AFAC/BFNHCRC Conference, Brisbane, September, 2016.
- Yebra, M., van Dijk, A., Cary, G. J. 2016c. Assessment of the utilization potential of new technologies to map bushfire hazard and impacts. BNHCRC Milestone report. 3.4.1.