Synopsis of the Research Project:

Bushfires have been ranked as the fourth Australian disaster after heat waves, tropical cyclones and floods (Coates, 1999). Bushfire is a natural and periodic event in Australia and almost 75% of the Australian continent is affected by scrub fires due a long dry spell (Hennessy & Wales, 2005). Large scale biomass burning modifies global carbon cycle, changes landscape patterns and diversity, thus, influencing the energy balance of forest ecosystems (Tanase et al., 2011). The impact of bushfire is multifarious through removing the existing vegetation cover, affecting the post-fire vegetation regrowth and its composition (Röder et al., 2008). Variations in fire severity across the landscapes after the forest fires leave complex spatial patterns of vegetation damage which is directly linked to fire intensity (White et al., 1996). Australia needs sophisticated fire modelling techniques to prevent and mitigate bushfires. Several fire spread models [FireDST, CAFE] developed and have been using for bush fire spread modelling (Tolhurst et al., 2008). The integration of ground-based sampling and geo-spatial information will be a holistic approach to predict the prolonged impacts of bushfires on vegetation.

 The aim of this research is to develop a stochastic fire effect model to predict the impacts of fire severity on the vegetation of selected national parks around Sydney. The main aim of the research will be achieved through the following research objectives:

• Understanding the spatio-temporal patterns of bush fires considering important environmental variables
• Assessment and  of burn severity using field sampling and geo-spatial fire indices
• Parameterizing, calibrating, verification and validation of a stochastic fire effect model with the derived from geo-sampling (Geo-CBI) and geospatial information

 The outcome of this research will be a geo-spatial fire effect model integrating environmental parameters, geo-spatial fire information and climatic data as the model inputs, which will help to develop statistically reliable future environmental scenarios for the post-fire impacts on vegetation. The developed fire effect model will be developed with the spatio-temporal satellite image analysis and robust geo-processing techniques, so, a minimal data upgradation will be required to simulate the short and long-term impacts of bushfires. This model will be validated on the selected National Parks around Sydney, but, there will be a chance to improve and validate the developed algorithm for the whole Australia, which will minimize the time and cost of fire impact prediction.

References:

Coates, L. (1999). Flood Fatalities in Australia, 1788-1996. Australian Geographer, 30(3), 391-408. doi: 10.1080/00049189993657

Hennessy, K. J., & Wales, N. S. (2005). Climate change impacts on fire-weather in south-east Australia.

Röder, A., Hill, J., Duguy, B., Alloza, J. A., & Vallejo, R. (2008). Using long time series of Landsat data to monitor fire events and post-fire dynamics and identify driving factors. A case study in the Ayora region (eastern Spain). Remote Sensing of Environment, 112(1), 259-273. doi: http://dx.doi.org/10.1016/j.rse.2007.05.001

Tolhurst, K., Shields, B., & Chong, D. (2008). Phoenix: development and application of a bushfire risk management tool. Australian Journal of Emergency Management, The, 23(4), 47.

White, J. D., Ryan, K. C., Key, C. C., & Running, S. W. (1996). Remote sensing of forest fire severity and vegetation recovery. International Journal of Wildland Fire, 6(3), 125-136.