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Monitoring and understanding forest dynamics is essential for environmentalconservation and management. This is why the Swiss National Forest Inventory(NFI) provides countrywide vegetation height maps at a spatial resolution of0.5 m. Its long update time of 6 years, however, limits the temporal analysisof forest dynamics. This can be improved by using spaceborne remote sensing anddeep learning to generate large-scale vegetation height maps in acost-effective way. In this paper, we present an in-depth analysis of thesemethods for operational application in Switzerland. We generate annual,countrywide vegetation height maps at a 10-meter ground sampling distance forthe years 2017 to 2020 based on Sentinel-2 satellite imagery. In comparison toprevious works, we conduct a large-scale and detailed stratified analysisagainst a precise Airborne Laser Scanning reference dataset. This stratifiedanalysis reveals a close relationship between the model accuracy and thetopology, especially slope and aspect. We assess the potential of deeplearning-derived height maps for change detection and find that these maps canindicate changes as small as 250 $m^2$. Larger-scale changes caused by a winterstorm are detected with an F1-score of 0.77. Our results demonstrate thatvegetation height maps computed from satellite imagery with deep learning are avaluable, complementary, cost-effective source of evidence to increase thetemporal resolution for national forest assessments.

Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain