Quantifying surface fuels for fire modelling in temperate forests using airborne lidar and Sentinel-2: potential and limitations

Surface fuel information is an essential input for models of fire behaviour and fire effects. However, spatially explicit, continuous information on surface fuel loads and fuelbed depth is scarce because the collection of field data is laborious, while suitable methods for deriving estimates from remote sensing data are still at an early stage of development. Fine-scale surface fuel mapping using both passive and active remote sensing has not yet been carried out in Central European forest types, and it remains unexplored how prediction uncertainties of different fuel components affect modelled fire behaviour. This study combines very detailed airborne lidar and multispectral satellite data to extract metrics describing forest structure and composition in two forested areas in southwestern Germany. These metrics were used to predict field-sampled surface fuel components using random forest regression. Accuracies of continuous fuel load predictions were compared to accuracies that could be achieved if only forest type-specific average fuels were assigned. Results revealed that models based on remotely sensed metrics explain part of the variance in litter and fine dead woody fuels (R2=0.27-0.41), but not in coarser dead woody fuels. Estimates for herb and shrub fuels were fairly accurate (R2=0.55-0.64) but limited for the more fire-relevant fine fraction of shrub fuels (R2=0.39). Fuelbed depth was moderately well predicted based on remote sensing data (R2=0.44). Lidar-derived metrics were particularly useful for predicting understory fuels and fuelbed depth. Litter and fine woody fuel predictions were linked to canopy characteristics captured with both lidar and multispectral data and similarly accurate estimates could be obtained using average values based on forest type. We used the fine-scale surface fuel maps derived from remote sensing to predict potential surface fire behaviour in the study area and analysed the sensitivity of modelled fire behaviour to errors in the predicted loads of different surface fuel components: fire behaviour was most sensitive to errors in litter and especially shrub fuel loads, hence estimates of these components need to be improved. Overall, this study showed that statistical relationships between remotely sensed metrics describing forest composition and structure and surface fuels have some potential for estimating fuel loads in Central European forest types and should be further developed to provide starting points for realistic fire behaviour models.