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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B2-2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-61-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-61-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
28 Jun 2021
| 28 Jun 2021
IMPROVING UAV TELEMETRY POSITIONING FOR DIRECT PHOTOGRAMMETRY
Dept. of Civil and Environmental Engineering (DICA), Politecnico di Milano, Milan, Italy
Dept. of Civil and Environmental Engineering (DICA), Politecnico di Milano, Milan, Italy
E. Capizzi
Dept. of Civil and Environmental Engineering (DICA), Politecnico di Milano, Milan, Italy
L. Pinto
Dept. of Civil and Environmental Engineering (DICA), Politecnico di Milano, Milan, Italy
M. Reguzzoni
Dept. of Civil and Environmental Engineering (DICA), Politecnico di Milano, Milan, Italy
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Short summary
Short summary
We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. 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. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
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The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, https://doi.org/10.5194/tc-10-511-2016, 2016
Short summary
Short summary
We investigate snow depth distribution at peak accumulation over a small Alpine area using photogrammetry-based surveys with a fixed wing unmanned aerial system. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to -0.073 m. Moreover, in this case study snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m.
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