They constitute the so-called fire triangle (Barrowman, 1951). If a single element ends, the triangle breaks and the fire goes out. We are interested here in three other elements influencing the spread rate of a wildland fire and acting as input variables in wildfire prediction models: fuel characteristics, topography, and weather (Countryman, 1972). Large bushfires have burnt across the ACT in the summer almost every decade since the establishment of the ACT in 1911. (Bushfire planning of ACT-Nicola Main-ACT RFS) Of these, 2003 Canberra bushfires are one of the catastrophic bushfire disasters in the Australian Capital Territory and the region resulting in significant loss of life and property. Within the aftermath of the 2003 bushfires, ACT was closely criticised over the fact that it didn't have adequate resources at hand to control the fires, or systems in position to receive these services. Previous inquiries recommended that the ACT Emergency Services should coordinate the development of emergency management mapping products for the bushfire management team and other services. Additionally, they recommended that an appropriate geographic information systems capability be maintained to enable the production of fire-specific maps as the need arises. (Review of fire management act bushfire council, December …show more content…
In recent years, remote sensing techniques have been applied to estimate fuel models (Pyne et al., 1996)(Bååth et al., 2002) developed an approach to obtain practical information about recent and future forest fuel potentials. LiDAR systems have been used to estimate critical parameters for fire behavior and may, potentially, be used to compute and distinguish a range of fuel attributes including understory fuel height (Means et al., 2000, Mutlu et al., 2008a). LiDAR is a proven active remote sensing technology used to measure distances with high accuracy of 0.5-2 m in all directions(Wagner et al., 2008). This technology provides horizontal and vertical information at high spatial resolution and vertical accuracies, offering opportunities for enhanced forest monitoring, management and planning (Dubayah and Drake, 2000, Andersen et al., 2005). Over the last decade, LiDAR has been increasingly used by researchers to map forests and the structures. LiDAR has been used successfully to capture forest structure, to map individual trees in forests and critical wildlife habitat characteristics, to predict forest volume and biomass, to develop inputs for forest fire behavior modeling and to map forest topography and infrastructure. LiDAR derived data provide accurate estimates of surface fuel parameters efficiently and accurately over extensive areas of