the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Measuring the snowpack depth with Unmanned Aerial System photogrammetry: comparison with manual probing and a 3D laser scanning over a sample plot
Abstract. Photogrammetric surveys using Unmanned Aerial Systems (UAS) may represent an alternative to existing methods for measuring the distribution of snow, but additional efforts are still needed to establish this technique as a low-cost, yet precise tool. Importantly, existing works have mainly used sparse evaluation datasets that limit the insight into UAS performance at high spatial resolutions. Here, we compare a UAS-based photogrammetric map of snow depth with data acquired with a MultiStation and with manual probing over a sample plot. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. The use of a MultiStation, which exploits a scanning principle, also enables to compare UAS data on snow with a frequently used instrument in high-resolution applications. Results show that the Root Mean Square Error (RMSE) between UAS and MultiStation data on snow is equal to 0.036 m when comparing the two point clouds. A large fraction of this difference may be, however, due to spurious differences between datasets due to simultaneous snowmelt, as the RMSE on bare soil is equal to 0.02 m. When comparing UAS data with manual probing, the RMSE is equal to 0.31 m, whereas the median difference is equal to 0.12 m. The statistics significantly decrease up to RMSE = 0.17 m when excluding areas of likely water accumulation in snow and ice layers. These results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
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- RC1: 'Review for tc-2017-57', Anonymous Referee #1, 24 May 2017
- AC1: 'Reply to Reviewer #1 comments', Carlo De Michele, 06 Jun 2017
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RC2: 'Comments on manuscript tc-2017-57', Anonymous Referee #2, 04 Aug 2017
- AC2: 'Reply to Referee #2', Carlo De Michele, 06 Sep 2017
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RC3: 'Review', Anonymous Referee #3, 21 Aug 2017
- AC3: 'Reply to Referee #3', Carlo De Michele, 06 Sep 2017
- EC1: 'Editor's decision', Florent Dominé, 23 Aug 2017
- RC1: 'Review for tc-2017-57', Anonymous Referee #1, 24 May 2017
- AC1: 'Reply to Reviewer #1 comments', Carlo De Michele, 06 Jun 2017
-
RC2: 'Comments on manuscript tc-2017-57', Anonymous Referee #2, 04 Aug 2017
- AC2: 'Reply to Referee #2', Carlo De Michele, 06 Sep 2017
-
RC3: 'Review', Anonymous Referee #3, 21 Aug 2017
- AC3: 'Reply to Referee #3', Carlo De Michele, 06 Sep 2017
- EC1: 'Editor's decision', Florent Dominé, 23 Aug 2017
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Cited
5 citations as recorded by crossref.
- Application of Low-Cost UASs and Digital Photogrammetry for High-Resolution Snow Depth Mapping in the Arctic E. Cimoli et al. 10.3390/rs9111144
- Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos R. Fernandes et al. 10.5194/tc-12-3535-2018
- Machine learning model for snow depth estimation using a multisensory ubiquitous platform S. Nasim et al. 10.1007/s11629-021-7186-4
- Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain M. Adams et al. 10.1007/s00024-017-1748-y
- Image acquisition effects on Unmanned Air Vehicle snow depth retrievals A. Tekeli & S. Dönmez 10.5194/piahs-380-81-2018