Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data
|Title||Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Chen, Y, Zhu, X, Yebra, M, Harris, S, Tapper, N|
|Journal||Environmental Modelling & Software|
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's (R2 = 0.61 and RMSE = 39.6 g) and Gilroy and Tran'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.