An assessment of pre- and post fire near surface fuel hazard in an Australian dry sclerophyll forest using point cloud data captured using a terrestrial laser scanner

Assessment of ecological and structrual changes induced by fire events is important for understanding the effects of fire, and planning future ecological and risk mitigation strategies. This study employs Terrestrial Laser Scanning (TLS) data captured at multiple points in time to monitor the changes in a dry sclerophyll forest induced by a prescribed burn. Point cloud data was collected for two plots; one plot undergoing a fire treatment, and the second plot remaining untreated, thereby acting as the control. Data was collected at three epochs (pre-fire, two weeks post fire and two years post fire). Coregistration of these multitemporal point clouds to within an acceptable tolerance was achieved through a two step process utilising permanent infield markers and manually extracted stem objects as reference targets. Metrics describing fuel height and fuel fragmentation were extracted from the point clouds for direct comparison with industry standard visual assessments. Measurements describing the change (or lack thereof) in the control plot indicate that the method of data capture and coregistration were achieved with the required accuracy to monitor fire induced change. Results from the fire affected plot show that immediately post fire 67% of area had been burnt with the average fuel height decreasing from 0.33 to 0.13 m. At two years post-fire the fuel remained signicantly lower (0.11 m) and more fragmented in comparison to pre-fire levels. Results in both the control and fire altered plot were comparable to synchronus onground visual assessment. The advantage of TLS over the visual assessment method is, however, demonstrated through the use of two physical and spatially quantifiable metrics to describe fuel change. These results highlight the capabilities of multitemporal TLS data for measuring and mapping changes in the three dimensional structure of vegetation. Metrics from point clouds can be derived to provide quantified estimates of surface and near-surface fuel loss and accumulation, and inform prescribed burn efficacy and burn severity reporting.