Accuracy and Consistency of Space-based Vegetation Height Maps for
  Forest Dynamics in Alpine Terrain
Yuchang Jiang Marius Rüetschi Vivien Sainte Fare Garnot Mauro Marty Konrad Schindler Christian Ginzler Jan D. Wegner
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.