We monitor how vegetation in the high latitudes is changing under climate warming – from individual trees to entire landscapes. To do this, we use state-of-the-art remote sensing across all spatial scales: ground-based measurements, drones, research aircraft, and satellite missions such as the multi-spectral Landsat and Sentinel-2, the Sentinel-1 and TDX radar missions and the hyperspectral EnMAP. Our data and methods range from 3D LiDAR point clouds and drone and airborne multispectral or hyperspectral cameras to optical and radar satellites, allowing us to precisely map forest structure, tundra vegetation.

A key focus is on the transition zones between taiga and tundra and on boreal forests, which are particularly sensitive to warming and play an important role in the global carbon cycle. In these regions, we develop standardized training datasets and reference products tailored to machine learning and the validation of satellite products – for example on forest types, tree height, biomass, or permafrost indicators.

Our open access datasets and methods support research, public authorities, and practitioners in detecting changes at an early stage and making better-informed decisions in climate and nature conservation.