- Zuletzt aktualisiert
- 10. Juni 2025
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Beschreibung
Countrywide vegetation height models (VHM) were generated for Switzerland based on Copernicus Sentinel-2 imagery and the digital terrain model (DTM) swissALTI3D from the Swiss Federal Office of Topography swisstopo. A Convolutional Neural Network (CNN) model was trained to estimate the maximum vegetation height at the spatial resolution of the Sentinel-2 pixel of 10 m. Vegetation heights from the spatially higher-resolved VHM Lidar NFI were used as reference data for the CNN training. Within the framework of the Swiss National Forest Inventory (NFI), the VHMs were modelled annually based on available Sentinel-2 imagery from May – September of the respective year. Further details on the creation of the VHM Sentinel NFI can be found in the paper Jiang et al. (2023, https://doi.org/10.1016/j.srs.2023.100099). Contains modified Copernicus Sentinel data.
Ressourcen
- TIFF VHM_Sentinel_NFI_2024
- TIFF VHM_Sentinel_NFI_2023
- TIFF VHM_Sentinel_NFI_2022
- TIFF VHM_Sentinel_NFI_2021
- TIFF VHM_Sentinel_NFI_2020
- TIFF VHM_Sentinel_NFI_2019
- TIFF VHM_Sentinel_NFI_2018
- TIFF VHM_Sentinel_NFI_2017
Showcases
Zusätzliche Informationen
- Identifier
- 3b1cae17-fc7a-4722-95da-92d3be869273@envidat
- Publikationsdatum
- 15. Mai 2025
- Änderungsdatum
- 10. Juni 2025
- Konform mit
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- EnviDat
- Kontaktstellen
- Sprachen
- Englisch
- Weitere Informationen
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- Landing page
- https://www.envidat.ch/#/metadata/vegetation-height-model-sentinel-nfi
- Dokumentation
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- Zeitliche Abdeckung
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- Aktualisierungsintervall
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- Metadatenzugriff
- API (JSON) XML herunterladen
