Morpho–Sedimentary Monitoring in a Coastal Area, from 1D to 2.5D, Using Airborne Drone Imagery
Abstract
:1. Introduction
1.1. Interest of Morpho-Sedimentary Monitoring in the Global Change Context
1.2. Common Data Acquisition and Monitoring Methods
1.2.1. Handborne Monitoring Methods
1.2.2. Airborne (Manned Aerial Vehicle, MAV) Monitoring Method
1.2.3. Spaceborne Monitoring Method
1.3. Interest of the Use of Drone (or Unmanned Aerial Vehicle) Imagery for Coastal Monitoring
2. Materials and Methods
2.1. Study Site Presentation
General Context of the Bay of Mont-Saint-Michel
2.2. Data Acquisition
2.2.1. Ground Control Points’ Positioning
2.2.2. Imagery Acquisition Material
2.2.3. Data Acquisition Method
2.3. Data Processing
- The first step of the raw data processing consists into image calibration and image pair matching. A data set of 63 images calibrated with a median of 21,483 key points per images. Pair matching is calibrated using an exact geolocation and orientation method. It was achieved using the original image scale, and correspondence between images was conducted using the acquisition time and triangulation of the image’s geolocation.
- Previously recorded GCPs (see Section 2.2.1) are added to the result of image pair matching to correctly georeference the data set.
- Dense point cloud is generated with a high point density (number of densified points on the study area: 72 578 307).
- 2.5D textured mesh is produced at VHR from the dense point cloud. This mesh is made using a triangulated irregular network.
3. Results
3.1. 1D Monitoring
3.2. 2D Monitoring and Derived Product
3.3. 2.5D Monitoring
4. Discussion
4.1. Shoreline Analysis
4.2. Topographic Profiles’ Analysis
4.3. Point Cloud and DEM Analyses
4.4. Possible Improvements in Drone Data Acquisition
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Data Source | Spatial Resolution/Point Distance (m) | Accuracy (m) | |
---|---|---|---|
Horizontal | Vertical | ||
Handborne | |||
DGNSS | N/A | <0.1 | <0.1 |
Tachometer | N/A | <0.1 (under 300 m distance) | <0.1 (under 300 m distance) |
Airborne (manned aerial vehicle) | |||
Optical imagery | >0.2 | N/A | N/A |
LiDAR | >0.2 | <0.1 | 0.15–0.2 |
Spaceborne | |||
Landsat 8 | 15 (panchromatic)/30 (multispectral) | 12 (CE90) | N/A |
SPOT 6–7 | 1.5 (panchromatic)/6 (multispectral) | 10 (CE90) | N/A |
WorldView-2 | 0.46 (panchromatic)/1.84 (multispectral) | 3.5 (CE90) | N/A |
WorldView-3 | 0.31 (panchromatic)/1.24 (multispectral) | 3.5 (CE90) | N/A |
Pleiades-1 | 0.5 (panchromatic)/2 (multispectral) | 3 (CE90) | N/A |
GeoEye-1 | 0.41 (panchromatic)/1.65 (multispectral) | 5 (CE90) | N/A |
UAV model | DJI Mavic Pro Platinum |
Sensor | 1/2.3” (CMOS) |
No. of pixel | Total pixels: 12.71 MP Effective pixels: 12.35 MP |
Lens | FOV 78.8°, Focus: 28 mm (35 mm format equivalent) Aperture: f/2.2 |
Flight planning and control software | DJI GS Pro |
Name | Ortho-littorale v2 | LiDAR 2018 |
Type of data | MAV orthomosaic | MAV point cloud |
Production date | 2014 | 2018 |
Data producer(s) | MEDDE-CEREMA | SHOM |
Spatial resolution | 0.5 m × 0.5 m | <0.20 m |
Horizontal accuracy | <1.2 m | 2.0 m |
Vertical accuracy | - | <0.40 m |
Flight planning software | DJI GS Pro |
Front overlap ratio | 60% |
Side overlap ratio | 60% |
Height | 50 m |
Gimbal pitch angle | −90° |
Shutter interval | 2.0 s |
Flying time | 12 mn |
Data Source | Point Density | |||
---|---|---|---|---|
Min. | Max. | Mean | Std dev. | |
UAV Drone | 786.06 | 3504.77 | 2571.86 | 805.26 |
MAV LiDAR | 11.52 | 53.69 | 29.98 | 14.02 |
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Share and Cite
Mury, A.; Collin, A.; James, D. Morpho–Sedimentary Monitoring in a Coastal Area, from 1D to 2.5D, Using Airborne Drone Imagery. Drones 2019, 3, 62. https://doi.org/10.3390/drones3030062
Mury A, Collin A, James D. Morpho–Sedimentary Monitoring in a Coastal Area, from 1D to 2.5D, Using Airborne Drone Imagery. Drones. 2019; 3(3):62. https://doi.org/10.3390/drones3030062
Chicago/Turabian StyleMury, Antoine, Antoine Collin, and Dorothée James. 2019. "Morpho–Sedimentary Monitoring in a Coastal Area, from 1D to 2.5D, Using Airborne Drone Imagery" Drones 3, no. 3: 62. https://doi.org/10.3390/drones3030062