Wildfire as an interplay between water deficiency, manipulated tree species composition and bark beetle. A remote sensing approach

During 2022, Bohemian Switzerland NP was affected by the largest wildfire in the Czech Republic throughout its modern history. The NP is formed by sandstone towers, deep narrow valleys and dense forests. From the 19th century onwards, Norway spruce and non-native Weymouth pine were massively planted here. A series of weather extremes in the last years caused an exceptional drought and consequent massive bark beetle outbreak and spruce die off, followed by the catastrophic event. Wildfires of such a dimension are rather uncommon in Central Europe, and this event therefore serves as a perfect model situation to study the role of species composition, bark beetle and water availability on the fire dynamics as well as the changes in biodiversity and natural succession after the disaster. Before the fire, the area was dominated by conifers, mostly standing dry after the bark beetle attack except along the water courses, and further formed of clear cuts, healthy deciduous beech forests and rocky outcrops. 

Pre-fire vegetation state, fire severity and post-fire regeneration were assessed using a combination of remote sensing sources. In particular, we used pre- and post-fire series of Sentinel-2 satellite MSS imagery, and acquisition of multispectral (MSS) and LIDAR data. The whole area was sampled from small aircraft TL232 Condor by three sensors - photogrammetric camera Hasselblad A6D-100c (ground sampling distance - GSD - 5 cm), MSS sensor MicaSense Altum (GSD 32 cm) and LIDAR RIEGL VUX 1-LR (13 points/m), and detailed sites were sampled using drone mounted sensors - MSS (MicaSense Altum, GSD 5 cm) and LIDAR (DJI L1). Forest composition and changes in health status were derived using a range of spectral indices and supervised classification. Fire severity and forest structure were derived using a combination of Lidar and optical point cloud, fisheye camera, ground sampling, and analysis of optical data (supervised classification, vegetation indices). 

Our research revealed that fire disturbance was low or none at native deciduous tree stands and waterlogged sites. On the opposite, it was more severe at dry bark-beetle clearings covered by a thick layer of litter as compared to standing dead spruce. We can infer that in places where many stems were only partly burned or the trees postponed the die-off, the fire went faster and the severity of disturbance was lower. In some cases, we could see patterns formed by ground fire, such as burned circles around trees or tree stools surrounded by unburned areas. Post-fire regeneration is very fast, and even after one year, vegetation growth can be detected using LIDAR and photogrammetric point clouds. Derived information on fire severity, detailed 3D stand structure and health status are to be used as a proxy of the fire disturbance impact on biodiversity and patterns of regeneration.