Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers
"> Figure 1
<p>(<b>a</b>) Overview of the study areas. Coordinates are given in CH1903+/LV95 [<a href="#B35-remotesensing-09-00186" class="html-bibr">35</a>]. The outlines of Findelengletscher (<b>b</b>), Griesgletscher (<b>c</b>) and Sankt Annafirn (<b>d</b>) are given in violet for the year 2015. The locations of the GCPs and the validation points measured with a differential Global Positioning Systems (dGPS) (continuous dGPS points) are depicted for each season. Because of the high spatial density, continuous dGPS points are not resolved individually, but appear as a line. The elevation differences between two DSMs over stable areas (<a href="#sec4dot2-remotesensing-09-00186" class="html-sec">Section 4.2</a>) are shown for Findelen- and Griesgletscher. (Background maps: Swiss Federal Office of Topography, Swisstopo.)</p> "> Figure 2
<p>Example of a GCP set on (<b>a</b>) ice and (<b>b</b>) snow. The GCP center is measured with a Leica GPS 1200 (<b>b</b>). (<b>c</b>) SenseFly eBee (<b>left</b>) and computer (<b>right</b>) linked to the transmitter sending the flight coordinates to the UAV.</p> "> Figure 3
<p>Workflow used for DSM and orthophoto generation (<b>left column</b>) and parameters selected in Agisoft Photoscan (<b>right column</b>).</p> "> Figure 4
<p>Methodology to assess the vertical accuracy for DSMs built with different numbers of GCPs. Here, an example is given for one dataset with <span class="html-italic">N</span> GCPs. Given a number of GCPs to be used for DSM generation (e.g., 3 and 4 in the top and bottom part of the figure, respectively; orange points), <span class="html-italic">n</span> different GCP-combinations (rows) are randomly selected. The remaining GCPs (check points; grey) are used for DSM validation. The standard deviation of the elevation difference between the DSM and the check points is then computed by pooling the <span class="html-italic">n</span> combinations (<math display="inline"> <semantics> <msub> <mi>σ</mi> <mi>vert</mi> </msub> </semantics> </math>).</p> "> Figure 5
<p>Vertical (<b>a</b>) and horizontal (<b>b</b>) DSM accuracy against GCP density. The accuracy is defined as the standard deviation (SD) of the elevation differences between DSMs and check points. The GCP density is calculated from the number of GCPs used to build a DSM, divided by the area investigated. Glaciers (seasons) are represented by different symbols (colors). The exponential fit (in black) follows Equation (<a href="#FD1-remotesensing-09-00186" class="html-disp-formula">1</a>).</p> "> Figure 6
<p>(<b>a</b>) Distance to the closest GCP used for DSM generation against absolute elevation differences between DSM and CPs. The data refer to all campaigns (cf. <a href="#remotesensing-09-00186-t001" class="html-table">Table 1</a>). The light blue line is a linear fit of the 50th percentiles of the elevation differences between the DSMs and the check points, calculated for 10-mdistance bins. The red lines are linear fits of the 2.5 and 97.5 percentiles. (<b>b</b>–<b>d</b>) Elevation differences between continuous dGPS points and a DSM built with (<b>b</b>) three clustered GCPs on the glacier tongue, (<b>c</b>) three spatially distributed GCPs and (<b>d</b>) GCPs located at the edge of the survey area. The example is from the Findelengletscher autumn campaign.</p> "> Figure 7
<p>(<b>a</b>) Elevation differences between summer and autumn DSMs. Orthophoto of the same area in (<b>b</b>) summer and (<b>c</b>) autumn. The glacier outline (violet line) and features changing appearance because of different illumination (red arrows) are highlighted.</p> "> Figure 8
<p>Elevation differences between a DSM generated on the tongue of the Findelengletescher (autumn campaign) and the continuous dGPS points. The DSM was built with different parameters in Agisoft Photoscan for the step “build dense cloud”: “aggressive”, “mild” or “moderate” depth filters (<b>left</b>) and “low”, “medium” or “high” cloud quality (<b>right</b>). The different DSM cell size is displayed on the left side of the plot. The boxplots show the median, interquartile range (box) and 95% confidence interval (whiskers).</p> ">
Abstract
:1. Introduction
2. Study Areas and Data Acquisition
3. Methods
3.1. DSM and Orthophoto Generation
- In the field, the GCPs are deployed on the glacier surface and their positions recorded.
- The image dataset is acquired through several (between four and fifteen; Table 1) UAV flights.
- The images are imported in Agisoft Photoscan, together with the information about acquisition location (coordinates) and the roll, pitch and yaw of the UAV platform. The information is used to preliminarily orientate the images.
- The “Align” step in Agisoft Photoscan comprises three phases. First, a feature-detection algorithm is applied to detect features (or “keypoints”) on every image [38]. The number of detected keypoints depends on the image resolution, image texture and illumination [39]. Second, matching keypoints are identified and inconsistent matches removed. Third, a bundle-adjustment algorithm is used to simultaneously solve the 3D geometry of the scene, the different camera positions and the camera parameters (focal length, coordinates of the principal point and radial lens distortions). The output of this step is a sparse point cloud. The preliminary orientation of the images (Step 3) reduces the processing time of the “align” operation, as only neighboring images (and not the entire image dataset) are searched for matching keypoints. Step 4, often referred to as SfM workflow (e.g., [40]), accounts for around 35% of the total Agisoft Photoscan processing time (Steps 3–10).
- The GCPs are manually identified on the images and their coordinates imported.
- The GCPs coordinates are used to refine the camera calibration parameters and to optimize the geometry of the output point cloud [24].
- Multi-view stereo image matching algorithms (e.g., [40]) are applied to increase the density of the sparse point cloud. The density of the final georeferenced dense point cloud is strongly related to the number of matching keypoints. Different cloud quality parameters (“low”, “medium” and “high”) are available for the “build dense cloud” step. In this study, the parameter “low” was chosen. As shown later (Section 4.4), this reduces the DSM accuracy by 3 cm (6 cm) compared to a DSM built with the “medium” (“high”) parameter, but reduces the processing time by a factor of six (eighteen). The “build dense cloud” step with the “low” parameter accounts for around 45% of the total processing time.
- A polygon mesh is created from the dense point cloud.
- A texture map derived from all images is applied to the polygon mesh and used to create an orthophoto.
- A DSM with a cell size of 0.50 m is generated from the mesh and exported from Agisoft Photoscan. This cell size reflects a compromise between processing time and DSM accuracy (cf. Section 4.4).
3.2. Impact of the Number of GCPs on DSMs’ Accuracy
3.3. Factors Impacting Local DSM Accuracy
3.4. Impact of Surface Characteristics on DSM Accuracy
3.5. Effect of the Parameters Setting in Agisoft Photoscan on DSM Accuracy
4. Results and Discussion
4.1. Influence of GCP Number on Vertical and Horizontal DSM Accuracy
4.2. Factors Impacting Local DSM Accuracy
4.3. Impact of Surface Characteristics on DSM Accuracy
4.4. Effect of the Parameters Settings in Agisoft Photoscan on DSM Accuracy
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Glacier | Season | Date | N | N | N | N | N | Area (km) |
---|---|---|---|---|---|---|---|---|
Sankt Annafirn | Summer | 15 July 2015 | 4 | 750 | 14 | - | 224 | 1.4 |
Sankt Annafirn | Autumn | 28 September 2015 | 5 | 856 | 21 | - | 378 | 1.6 |
Sankt Annafirn | Winter | 15 April 2015 | 5 | 748 | 18 | - | 311 | 1.5 |
Findelengletscher | Summer | 22–23 July 2015 | 9 | 2044 | 20 | 10,665 | 72 | 4.0 |
Findelengletscher | Autumn | 21–22 September 2015 | 13 | 3002 | 36 | 38,813 | 90 | 5.2 |
Findelengletscher | Winter | 19–20 April 2016 | 12 | 2901 | 21 | 12,914 | 66 | 4.2 |
Griesgletscher | Summer | 3–04 August 2015 | 15 | 3307 | 25 | 15,795 | 96 | 6.2 |
Griesgletscher | Autumn | 27–28 September 2015 | 14 | 3789 | 32 | 10,790 | 84 | 6.9 |
Dataset | |||
---|---|---|---|
from Check Points (m) | from the Cont. Pts (m) | from Check Points (m) | |
Sankt Annafirn Summer | 0.12 | - | 0.06 |
Sankt Annafirn Autumn | 0.22 | - | 0.03 |
Sankt Annafirn Winter | 0.18 | - | 0.06 |
Findelengletscher Summer | 0.12 | 0.13 | 0.05 |
Findelengletscher Autumn | 0.10 | 0.13 | 0.05 |
Findelengletscher Winter | 0.04 | 0.10 | 0.03 |
Griesgletscher Summer | 0.12 | 0.20 | 0.09 |
Griesgletscher Autumn | 0.25 | 0.39 | 0.07 |
Study | Surface | Date | GSD | GCP Density | |||
---|---|---|---|---|---|---|---|
(m) | (m) | (m) | (GCP/km) | (GCP/GSD) | |||
Immerzeel et al. (2014) | Glacier | May 2013 | 0.05 | 0.30 | 0.20 | 6.3 | 1.55 × 10 |
Immerzeel et al. (2014) | Glacier | October 2013 | 0.04 | 0.15 | 0.15 | 6.0 | 9.40 × 10 |
Nolan et al. (2015) | Snow | April 2014 | 0.15 | 0.10 | 0.05 | 2.1 | 4.73 × 10 |
Whitehead et al. (2013) | Glacier | July 2010 | 0.12 | 0.42 | 0.20 | 4.8 | 7.00 × 10 |
Whitehead et al. (2013) | Glacier | July 2011 | 0.12 | 0.19 | 0.55 | 6.9 | 1.00 × 10 |
Surface Texture | Oriented Images | Matching Keypoints | Sparse Point Cloud | Dense Point Cloud |
---|---|---|---|---|
Fresh snow | 2 of 20 | 330 | 143 | 115,250 |
One-day-old snow | 19 of 20 | 34,800 | 15,400 | 1,032,700 |
Ice | 20 of 20 | 380,000 | 118,700 | 1,540,000 |
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Gindraux, S.; Boesch, R.; Farinotti, D. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers. Remote Sens. 2017, 9, 186. https://doi.org/10.3390/rs9020186
Gindraux S, Boesch R, Farinotti D. Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers. Remote Sensing. 2017; 9(2):186. https://doi.org/10.3390/rs9020186
Chicago/Turabian StyleGindraux, Saskia, Ruedi Boesch, and Daniel Farinotti. 2017. "Accuracy Assessment of Digital Surface Models from Unmanned Aerial Vehicles’ Imagery on Glaciers" Remote Sensing 9, no. 2: 186. https://doi.org/10.3390/rs9020186