Monitoring snow depth change across a range of landscapes with ephemeral snow packs using Structure from Motion applied to lightweight unmanned aerial vehicle videos

Abstract. Snow depth (SD) can vary by more than an order of magnitude over length scales of metres due to topography, vegetation and microclimate. Differencing of digital surface models derived from Structure from Motion (SfM) processing of airborne imagery has been used to produce SD maps with between ∼2 cm to ∼15 cm horizontal resolution and accuracies on the order of ±10 cm over both relatively flat surfaces with little or no vegetation and over alpine regions. Studies indicate that accuracy is lower in the presence of vegetation above or below the snowpack and in rough topography; suggesting that some biases may be temporally persistent. Moreover, flight and image parameters vary across studies but they are typically not related a priori to an expected uncertainty in SD. This study tests two hypotheses: i) that SD change can be more accurately estimated when differencing snow covered elevation surfaces rather than the absolute snow depth based on differencing a snow covered and snow free surface and ii) the vertical accuracy of SfM processing of imagery acquired by commercial light weight unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory. Moreover, these hypotheses are tested over areas with ephemeral snow pack conditions and across a range of micro-topography and vegetation cover. Weekly SD maps with

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Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

The Cryosphere ◽

10.5194/tc-12-3535-2018 ◽

2018 ◽

Vol 12 (11) ◽

pp. 3535-3550 ◽

Cited By ~ 4

Author(s):

Richard Fernandes ◽

Christian Prevost ◽

Francis Canisius ◽

Sylvain G. Leblanc ◽

Matt Maloley ◽

...

Keyword(s):

Free Surface ◽

Unmanned Aerial Vehicle ◽

Structure From Motion ◽

Snow Depth ◽

Low Cost ◽

Horizontal Resolution ◽

Absolute Difference ◽

Flat Surfaces ◽

Aerial Vehicle

Abstract. Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between ∼2 and ∼15 cm horizontal resolution and accuracies of ±10 cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along ∼50 m transects, ranged from 1.58 to 10.56 cm for weekly SD and from 2.54 to 8.68 cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71 cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.

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Supplementary material to "Monitoring snow depth change across a range of landscapes with ephemeral snow packs using Structure from Motion applied to lightweight unmanned aerial vehicle videos"

10.5194/tc-2018-82-supplement ◽

2018 ◽

Cited By ~ 1

Author(s):

Richard Fernandes ◽

Christian Prevost ◽

Francis Canisius ◽

Sylvain G. Leblanc ◽

Matt Maloley ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Structure From Motion ◽

Snow Depth ◽

Aerial Vehicle ◽

Supplementary Material

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Accuracy Assessment of Unmanned Aerial Vehicle (UAV) Structure from Motion Photogrammetry Compared with Total Station for a Deformed Slope

Understanding and Reducing Landslide Disaster Risk - ICL Contribution to Landslide Disaster Risk Reduction ◽

10.1007/978-3-030-60319-9_32 ◽

2020 ◽

pp. 291-299

Author(s):

Vera Hui Loo ◽

Chou Khong Wong

Keyword(s):

Unmanned Aerial Vehicle ◽

Structure From Motion ◽

Accuracy Assessment ◽

Total Station ◽

Aerial Vehicle

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Experimental Study on 3D Measurement Accuracy Detection of Low Altitude UAV for Repeated Observation of an Invariant Surface

Processes ◽

10.3390/pr10010004 ◽

2021 ◽

Vol 10 (1) ◽

pp. 4

Author(s):

Sha Gao ◽

Shu Gan ◽

Xiping Yuan ◽

Rui Bi ◽

Raobo Li ◽

...

Keyword(s):

Experimental Study ◽

Unmanned Aerial Vehicle ◽

Structure From Motion ◽

Measurement Accuracy ◽

Test Plot ◽

3D Measurement ◽

Difference Model ◽

Repeated Observations ◽

Aerial Vehicle ◽

Low Altitude

Low-altitude unmanned aerial vehicle (UAV) photogrammetry combined with structure-from-motion (SFM) algorithms is the latest technological approach to imaging 3D stereo constructions. At present, derivative products have been widely used in landslide monitoring, landscape evolution, glacier movement, volume measurement, and landscape change detection. However, there is still a lack of research into the accuracy of 3D data positioning based on the structure-from-motion of unmanned aerial vehicle (UAV-SFM) technology, itself, which can affect the measurable effectiveness of the results in further applications of this technological approach. In this paper, validation work was carried out for the DJI Phantom 4 RTK UAV, for earth observation data related to 3D positioning accuracy. First, a test plot with a relatively stable surface was selected for repeated flight imaging observations. Specifically, three repeated flights were performed on the test plot to obtain three sorties of images; the structure from motion and multi-view stereo (SFM-MVS) key technology was used to process and construct a 3D scene model, and based on this model the digital surface model (DSM) and digital orthophoto map (DOM) data of the same plot with repeated observations were obtained. In order to check the level of 3D measurement accuracy of the UAV technology itself, a window selection-based method was used to sample the point cloud set data from the three-sortie repeat observation 3D model. The DSM and DOM data obtained from three repeated flights over the surface invariant test plots were used to calculate the repeat observation 3D point errors, taking into account the general methodology of redundant observation error analysis for topographic surveys. At the same time, to further analyze the limits of the UAV measurement technique, possible under equivalent observation conditions with the same processing environment, a difference model (DOD) was constructed for the DSM data from three sorties, to deepen the overall characterization of the differences between the DSMs obtained from repeated observations. The results of the experimental study concluded that both the analysis of the 3D point set measurements based on window sampling and the accuracy evaluation using the difference model were generally able to achieve a centimeter level of planimetric accuracy and vertical accuracy. In addition, the accuracy of the surface-stabilized hardened ground was better, overall, than the accuracy of the non-hardened ground. The results of this paper not only probe the measurement limits of this type of UAV, but also provide a quantitative reference for the accurate control and setting of an acquisition scheme of the UAV-based SfM-MVS method for geomorphological data acquisition and 3D reconstruction.

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