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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-2
https://doi.org/10.5194/isprs-archives-XLII-2-813-2018
© Author(s) 2018. 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-XLII-2-813-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
30 May 2018
| 30 May 2018
USE OF FISHEYE PARROT BEBOP 2 IMAGES FOR 3D MODELLING USING COMMERCIAL PHOTOGRAMMETRIC SOFTWARE
D. Pagliari
Politecnico di Milano, DICA - Geodesy and Geomatics section, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
L. Pinto
Politecnico di Milano, DICA - Geodesy and Geomatics section, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
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Short summary
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Revised manuscript not accepted
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.
D. Pagliari, L. Pinto, M. Reguzzoni, and L. Rossi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 565–572, https://doi.org/10.5194/isprs-archives-XLI-B5-565-2016, https://doi.org/10.5194/isprs-archives-XLI-B5-565-2016, 2016
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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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