Modelling multi-layer fine fuel loads in temperate eucalypt forests using airborne LiDAR and inventory data

Wildfires are increasing in intensity and frequency due to climate change and land-use changes, posing critical threats to ecosystems, economies, and human safety. Fine fuels (<6 mm, such as leaves and twigs) are known key drivers of wildfire ignition and spread, particularly in temperate forests where high flammability increases wildfire hazard. Accurately quantifying fine fuel loads (FFL) across vertical forest layers is essential for understanding and predicting wildfire behaviour, yet past studies using Airborne Laser Scanning (ALS) have been limited to canopy fuels, overlooking surface and understorey layers that play a key role in wildfire propagation. This study addresses this gap by developing an ALS-based modelling approach to estimate FFL across four vertical layers: canopy, elevated (or ladder), near-surface, and surface. The study was conducted in eucalypt-dominated forests in Victoria, southeastern Australia. We stratified ALS point clouds into distinct layers (overstorey, intermediate, shrub, and herb), computed layer-specific structural metrics, and trained Random Forest models to predict multi-layer FFL. The models performed well, with the highest accuracy for canopy FFL (R 2 = 0.74, relative RMSE = 49.42 %) and moderate accuracy for elevated, near-surface, and surface FFL (R 2 = 0.42–0.56, relative RMSE = 59.77–77.57 %). Model interpretation revealed that integrating ALS metrics from multiple forest layers maximised accuracy and highlighted the complex role of vertical forest structure in predicting FFL. Prediction maps captured horizontal and vertical FFL variations across landscapes, reflecting differences in forest structure. Furthermore, pre-fire FFL, especially in surface and canopy layers, showed statistically significant associations with wildfire-induced forest loss. This study advances multi-layer FFL estimation using ALS data, offering a more comprehensive fuel information for wildfire hazard assessment and forest management. Future research should explore the scalability of this method by integrating satellite-derived data to extend FFL mapping at broader spatial scales. • Novel data-driven approach for stratifying ALS data into vertical forest layers. • A new modelling framework for quantifying multi-layer forest fine fuel loads. • Integrating ALS metrics from multiple layers improved understorey modeling accuracy. • Pre-fire fuel loads showed statistically significant correlations with forest loss.