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