Spatiotemporal observations on the distribution of snow in forests for two study sites and three winter seasons
Abstract: The spatial distribution of snow is key for understanding melting dynamics, particularly in forests where snow amounts vary on small spatial scales (<3 m) compared to open areas. Uncrewed Aerial Vehicle (UAV)-based Light Detection and Ranging (LiDAR) measurements quantify the snow distribution in forests at this high spatial resolution. Such datasets showed that snow distribution can be aggregated into spatial patterns featuring similar snow dynamics. The identification of snow distribution patterns from LiDAR data can be achieved using the ClustSnow workflow, first presented by Geissler et al. (2023). In this workflow, identified patterns are used for spatially extrapolating observed time series of snow depth and snow water equivalent from a few locations. We present a novel dataset that comprises UAV-based LiDAR snow depth maps, timeseries derived using a dense automatic snow depth sensor network and additional ground measurements. The data was collected during three winter seasons (October 2020 - May 2023) at two forested sites. The study sites are located in the sub-alpine Alptal, Switzerland and on the Schauinsland mountain, Germany. The dataset moreover includes the final spatiotemporally continuous snow depth and snow water equivalent maps derived using the ClustSnow workflow. This comprehensive dataset provides valuable insights into snow distribution and dynamics in forested eco-hydrological systems
Umfang und Inhalt: - repeated UAV-based LiDAR snow depth (HS) maps
- LiDAR-derived vegetation and topography data
- HS time series from automatic snow measurement stations (SnoMoS)
- manual transect measurements
- Snow products derived using the ClustSnow workflow available at https://github.com/jgenvironment/ClustSnow
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Keyword
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Lidar
Flugkörper
Fernerkundung
Nadelgehölze
Maschinelles Lernen
- Event
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Veröffentlichung
- (where)
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Freiburg
- (who)
-
Universität
- (when)
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2024
- Creator
- DOI
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10.6094/UNIFR/255332
- URN
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urn:nbn:de:bsz:25-freidok-2553326
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.2025, 1:45 PM CET
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Geissler, Joschka
- Rathmann, Lars
- Weiler, Markus
- Universität
Time of origin
- 2024
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