Forest recovery analysis combining AI with multi-platform LiDAR and UAV-based hyperspectral imaging (KI-Recover)

Temperate forest ecosystems are increasingly pressured by climate warming and drought events that drive disturbances such as storms, wildfires and calamities. Forest dieback caused by windthrow and bark beetle infestations has increased significantly in severity and affected entire regions in Germany.

With the progression of remote sensing and unoccupied aerial vehicles (UAV, drone) as sensor platform, precise monitoring of extensive forest areas at high resolution is feasible. The project KI-Recover integrates AI-driven multi-sensor data analysis of diverse sites following significant disturbances. Our surveys were conducted during summer 2025 in the Bavarian Forest National Park and the Harz National Park. Both regions are characterized by disturbance legacies, recent dieback and minimal forest management.

Within these regions, we chose forest stands based on disturbance type and history to allow for natural regeneration, except for two sites with recent wildfire. Monitoring utilized UAV-hyperspectral scanning, multispectral and RGB mapping and UAV-LiDAR. An extensive ground campaign provided forest inventory adapted for remote sensing, vegetation species and deadwood mapping. Additionally, mobile laser scanning was employed to obtain fine-scaled 3D information of forest metrics, e.g. forest structural complexity.

We present initial results of our integrated multi-modal geospatial modelling approach. Forest inventory at image level is conducted via instance segmentation of individual living trees, as well as standing and lying deadwood at various decay stages, using convolutional neural network (CNN) architectures. A large training and validation database was created by manual annotations and labelling of RGB and multispectral data. Detailed volumetric forest structure was extracted from fused mobile and UAV LiDAR, to overcome the scale gap between. Hyperspectral transect data is used i.e. to model species richness and measure plant vitality. All methods combined will inform indicators of successional development, stand dynamics, and species establishment.

The overarching goal of this project is to couple the geospatial remote sensing surveys with a forest succession prediction and detailed AI-driven climate modelling to assess effects of extreme events, heat stress and drought, and to provide data-driven recommendations for forest management.