@inbook {bnh-4957, title = {Australian cities and the governance of climate change}, booktitle = {Australia{\textquoteright}s Metropolitan Imperative - An Agenda for Governance Reform}, year = {2018}, publisher = {CSIRO Publishing}, organization = {CSIRO Publishing}, chapter = {Australian cities and the governance of climate change}, address = {Clayton, Victoria, Australia}, issn = {9781486307968}, url = {https://www.publish.csiro.au/book/7726/}, author = {Newton, Peter and Bertram, Nigel and John Handmer and Nigel Tapper and RP Thornton and Whetton, Penny} } @article {bnh-5264, title = {Determining the minimum sampling frequency for ground measurements of burn severity}, journal = {International Journal of Wildland Fire}, year = {2018}, month = {06/2018}, abstract = {

Understanding burn severity is essential to provide an overview of the precursory conditions leading to fires as well as understanding the constraints placed on fire management services when mitigating their effects. Determining the minimum sampling frequency for ground measurements is not only essential for accurately assessing burn severity, but also for fire managers to better allocate resources and reduce the time and costs associated with sampling. In this study, field sampling methods for assessing burn severity are analysed statistically for 10 burn sites across Victoria, Australia, with varying spatial extents, topography and vegetation. Random and transect sampling methods are compared against each other using a Monte Carlo simulation to determine the minimum sample size needed for a difference of 0.02 (2\%) in the severity classes proportions relative to the population proportions. We show that, on average, transect sampling requires a sampling rate of 3.16\% compared with 0.59\% for random sampling. We also find that sites smaller than 400 ha require a sampling rate of between 1.4 and 2.8 times that of sites larger than 400 ha to achieve the same error. The information obtained from this study will assist fire managers to better allocate resources for assessing burn severity.

}, doi = {10.1071/WF17055}, url = {http://www.publish.csiro.au/WF/WF17055}, author = {Alexander Holmes and Sarah Harris and Nigel Tapper and Christoph R{\"u}diger} } @article {bnh-3919, title = {Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data}, journal = {Environmental Modelling \& Software}, volume = {97}, year = {2017}, month = {11/2017}, pages = {61-71}, chapter = {61}, abstract = {

Accurate description of forest surface fuel load is important for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, assessing potential fire hazards and assisting in fuel hazard-reduction burns to reduce fire risks to the community and the environment. Bushfire related studies and current operational activities have a common challenge in quantifying fuels, since the fuel load varies across the landscape. This paper developed a predictive model that efficiently and accurately estimates quantities of surface fuel in Australian southeast Eucalypt forests. Model coefficients were determined through a three-step process that attempts to evaluate how the spatial variation in surface fuel load relates to litter-bed depth, fuel characteristics, topography and previous fire disturbance. First, the forest surface fuel depth-to-load relationship was established; second, key quantitative variables of environmental factors were added; and third, important qualitative variables of fuel characteristics were included. The verification of model prediction was conducted through leave-one-out cross-validation (CV). Light Detection and Ranging was used to quantify forest structural characteristics and terrain features. The calibrated model had a\ R2\ of 0.89 (RMSE\ =\ 20.7\ g) and performed better than the currently used surface fuel load models, including McArthur{\textquoteright}s (R2\ =\ 0.61 and\ RMSE\ =\ 39.6\ g) and Gilroy and Tran{\textquoteright}s (R2\ =\ 0.69 and\ RMSE\ =\ 36.5\ g) models. This study describes a novel approach to forest surface fuel load modelling using forest characteristics and environmental factors derived from LiDAR data through statistical analysis. The model established in this study can be used as an efficient approach to assist in forest fuel management and fire related operational activities.

}, doi = {10.1016/j.envsoft.2017.07.007}, url = {http://www.sciencedirect.com/science/article/pii/S1364815216304418}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} } @conference {bnh-3232, title = {Estimation of forest surface fuel load using airborne LiDAR data}, booktitle = {SPIE Remote Sensing}, year = {2017}, month = {09/2017}, publisher = {SPIE}, organization = {SPIE}, address = {Warsaw, Poland}, abstract = {

Accurately describing forest surface fuel load is significant for understanding bushfire behaviour and suppression difficulties, predicting ongoing fires for operational activities, as well as assessing potential fire hazards. In this study, the Light Detection and Ranging (LiDAR) data was used to estimate surface fuel load, due to its ability to provide threedimensional
information to quantify forest structural characteristics with high spatial accuracies. Firstly, the multilayered eucalypt forest vegetation was stratified by identifying the cut point of the mixture distribution of LiDAR point density through a non-parametric fitting strategy as well as derivative functions. Secondly, the LiDAR indices of heights, intensity, topography, and canopy density were extracted. Thirdly, these LiDAR indices, forest type and previous fire disturbances were then used to develop two predictive models to estimate surface fuel load through multiple regression analysis. Model 1 was developed based on LiDAR indices, which produced a R2 value of 0.63. Model 2 (R2 = 0.8) wasderived from LiDAR indices, forest type and previous fire disturbances. The accurate and consistent spatial variation in surface fuel load derived from both models could be used to assist fire authorities in guiding fire hazard-reduction burns and fire suppressions in the Upper Yarra Reservoir area, Victoria, Australia.

}, author = {Chen, Yang and Xuan Zhou and Marta Yebra and Sarah Harris and Nigel Tapper} }