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Geoscience Australia is conducting a series of national risk assessments for a range of natural hazards such as severe winds. The impact of severe wind varies considerably between equivalent structures located at different sites due to local roughness of the upwind terrain, shielding provided by upwind structures and topographic factors.Terrain surface roughness information is a critical spatial input to generate wind multipliers. It is generally the first spatial field to be evaluated, as it is utilised in both the generation of the terrain and topographic wind multiplier. Landsat imagery was employed to generate a terrain surface roughness product for six major metropolitan areas across Australia. It was necessary to investigate the applicability of multi-sensor approaches to generate a regional/national terrain surface roughness map based on the Australian/New Zealand wind loading standard (AS/NZS 1170.2). This paper discusses the methodology that developed a procedure to derive terrain surface roughness from various multi-source satellite images. MODIS, Landsat, and IKONOS imagery were acquired (from 12 September -26 November 2002) covering a significant portion of the New South Wales, Australia. An object-based image segmentation and classification technique was tested for seven bands of MODIS, six bands of Landsat Thematic Mapper, and four bands of IKONOS. Eleven terrain categories were identified using this technique which achieved classification accuracies of 79% and 93% over metropolitan (Sydney) and rural/urban areas respectively. It was revealed that the object-based image classification enhances the quality of the terrain product compared to traditional spectralbased maximum likelihood classification methods. To further improve the derivation of terrain roughness classification results, an integrated textural-spectral analysis merged Synthetic Aperture Radar and optical datasets provided in a study by [1]. A comparison with results derived from textural-spectral classification showed considerable improvement over the results from earlier classification techniques.

There is an increasing application for bushfire spread models in planning for prescribed burning and for the generation of fire risk assessment maps in fire prone communities. An evaluation of FARSITE and FlamMap bushfire spread models developed by the Fire Sciences Laboratory at Missoula involved a comparison of fire simulator models over two Californian landscapes representing different terrain and vegetation regimes. The paper includes a discussion on the models, the assumptions and limitations resulting from their application, and also the assembly of data to build landscape files and model outputs over the two test areas. The spatial datasets used in this study are sourced from the USGS EROS Data Center's LANDFIRE database at 30-metre pixel resolution. Information about fuel which has been derived from satellite imagery, terrain modelling and biophysical and local field knowledge was used to build Anderson's 13 Fire Behavior Fuel Models (FBFM13) and Scott and Burgan's 40 Fire Behavior Fuel Models (FBFM40). These were ingested into the FARSITE fire growth simulation model and FlamMap fire potential simulator. The FBFM40 provides a better representation of fuel across the landscape, leading to an improvement in surface fire intensity prediction and increased precision in determining crown fire behaviour. The FARSITE/FlamMap were used to model fire behaviour, and WindWizard simulated wind speed and direction scenarios across the Woodacre and Glen Ellen regions near San Francisco, California. FARSITE and FlamMap are two separate fire simulation models that use the same input datasets (vegetation/ground cover type, crown stand height, crown base height, crown bulk density, temperature, humidity, precipitation, slope, aspect, elevation, wind speed and direction). In this study, the actual fire perimeters were not available to compare the overestimated and underestimated fire growth perimeters/areas after and before using gridded wind data into the fire simulation. However, previous studies [1] demonstrate that incorporation of gridded wind data clearly improves prediction of fire growth perimeters. The preliminary evaluation of FARSITE/FlamMap simulations appropriately predicts fire growth process and assesses resources at risk, suggesting the need for further experiments in areas of different terrain and vegetation, and with varied weather conditions. In future research, it is proposed to evaluate how these models compare with existing Australian fire models by using the example of recent Australian wildfire events.

Tropical cyclone Tracy (Tracy) remains one of the most destructive natural hazard events in Australia's history. Growth in the population and size of Darwin since 1974 makes it desirable to know what impact an event similar to Tracy would have on the present day built environment. To assess the impacts in 1974 and the present day, we apply the Tropical Cyclone Risk Model (TCRM) developed at Geoscience Australia.A parametric wind field generated by TCRM is applied to building damage models in an attempt to reproduce the widespread damage to residential structures associated with Tracy in 1974. Employing these models yields a mean damage estimate of 36 per cent of replacement cost across all residential building stock in Darwin -a figure lower than that determined by post-event damage assessments. The unaccounted impact of large windborne debris is one possible explanation for the discrepancy between the observed and simulated damage.Based on the satisfactory replication of the damage associated with the historical impacts of Tracy, the wind field is then applied to the current day residential building database in order to assess the impact of Tracy were it to strike Darwin in 2008. We find that the mean damage to Darwin for the same urban footprint as the 1974 analysis in the present day would be around five per cent. This represents an approximately 90 per cent reduction in the modelled damage, and a significant portion of this reduction can be attributed to building code improvements.

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