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

• URL: http://arxiv.org/abs/2309.01797v1
• Date: Mon, 4 Sep 2023 20:23:57 GMT
• Title: Accuracy and Consistency of Space-based Vegetation Height Maps for Forest Dynamics in Alpine Terrain
• Authors: Yuchang Jiang, Marius R\"uetschi, Vivien Sainte Fare Garnot, Mauro Marty, Konrad Schindler, Christian Ginzler, Jan D. Wegner
• Abstract summary: The Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 m. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps. 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.
• Score: 18.23260742076316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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