Modeling surface fuel loads in temperate oak and pine forests using 3D structural metrics from terrestrial laser scanning

Background Surface fuels strongly influence forest fire behavior, yet their distribution and composition are often not known in detail. As surface fuels result from forest litter and vegetation dynamics, we hypothesize that fuel loads vary systematically with forest structure and composition. If so, forest structural characteristics could be used to predict surface fuels and improve fire modeling efforts. Here, we assess the relationship between forest structure and fuel loads of various surface fuel classes (leaves, needles, cones, acorns, woody debris, moss, grass, and herb) in Central European forests (Germany, Czechia) and along a gradient from pine ( Pinus sylvestris ) to oak ( Quercus spp.) dominated plots. Across 53 plots, we derive highly detailed, observer-independent structural and compositional metrics using terrestrial laser scanning (TLS) and field-measured surface fuel loads using destructive sampling in replicated microplots. Results Random forest models show the strongest predictive performance for fuel classes composed of smaller, evenly distributed particles (leaves, needles, and the complete fuel load) with R 2 values up to 0.72. When modeling all plots along the species gradient together, simple forest inventory metrics that distinguish between coniferous and deciduous trees are most important for predicting surface fuel loads. When modeling pure-species plots separately, more complex descriptors of vegetation density and height become essential. Conclusion Relatively simple, inventory-based metrics, many of which are available in existing forest databases, can sufficiently predict key surface fuel components across the surveyed species gradient. This enables more accessible and scalable fuel mapping for broad analysis, spanning a range of species compositions. Integrating this into fire behavior models could significantly enhance fire risk assessment and support proactive forest management strategies. More detailed models focusing on single species and pure stands, on the other hand, benefit from the additional information provided by 3D structural metrics. Here, vegetation density in the canopy is an important predictor of surface fuel load.