Using multi-scale and multi-model datasets for post-event assessment of wildfires

Between June and August 2022, the European Forest Fire Information System (EFFIS) reported more fires in Europe than in any other recent summer season. This is particularly true for Central Europe, where the largest forest fire in recent Czech history occurred in the German-Czech border region. With global warming and resulting longer dry periods, the length and severity of wildfire seasons in central Europe will likely increase. Therefore, easy to implement and cost-effective methods to assess wildfire damage and regeneration of the ecosystems are getting increasingly important. In this study we evaluated how different datasets obtained by uncrewed aerial system (UAS) can be incorporated with datasets obtained from the ground to describe the fire affected landscape. Thereby, multi-spectral 3D point clouds were derived from low-cost UAV laser scanning and using structure from motion (SfM) photogrammetry applied to RGB and multi-spectral imagery. The aerial datasets were combined with ground-based terrestrial and mobile laser scanning. The datasets were acquired in several surveys following the forest fire event in the German part of the National park Saxonian/Bohemian Switzerland.

Initial results show the potential of UAS-based sensing for efficient mapping of a burned area with a high resolution (600-1000 pts/m²). The combination of point clouds from UAS-based laser scanning and photogrammetry enables a detailed representation of the burned forest with different levels of fire damage (e.g., in still present canopy) when compared to the single datasets. The UAS based laser scanning data reveals higher noise compared to the SfM-based point clouds. However, the accuracy is still sufficient to improve the quality of orthomosaics in densely vegetated areas. In a next step, further investigations on data accuracy are conducted and automated point cloud fusion algorithms based on classified points are considered.