Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory
"> Figure 1
<p>Photos of the electric powered Styrofoam delta shaped fixed wing Unmanned Aerial Vehicle (UAV) used in this study.</p> "> Figure 2
<p>(<b>A</b>) Map of study site located near Marriottsville (Howard County, MD). A zoom-in of the floodplain area is also shown in the bottom right (<b>B</b>). Traces of the river banks and river cross-sections perpendicular to flow direction are overlain (refer to main body of text for more details). The image in the bottom left (<b>C</b>) is the digital surface model (DSM) from the drone and shows the USGS NHDPlus [<a href="#B16-remotesensing-11-00982" class="html-bibr">16</a>] stream centerline (flowline) as a dashed black line. Note the deviation of the rather straight-line geometry of the NHDPlus flowline from the actual, highly complex, stream topology as depicted in the drone DSM.</p> "> Figure 3
<p>LiDAR (<b>A</b>) and drone (<b>B</b>) bare earth elevation models for the area depicted in <a href="#remotesensing-11-00982-f002" class="html-fig">Figure 2</a>. Elevation contour lines are overlain. Note that topography is only marginally different between the two datasets.</p> "> Figure 4
<p>Distribution of the residual difference between the LiDAR and the drone elevation data. There is a slight negative skew of the distribution which could indicate a small systematic error in the drone elevation data.</p> "> Figure 5
<p>Heights along GPS traces of right (top panel) and left (bottom panel) river bank are shown. For accuracy assessment, see main body of text.</p> "> Figure 6
<p>Sample river cross-sections (drawn perpendicular to flow direction). Cross-sectional elevations reveal in-channel depth topology. For locations of cross-sections, see <a href="#remotesensing-11-00982-f002" class="html-fig">Figure 2</a>. We attribute the non-trivial horizontal shift of the section in the middle graph to changes in cross-section geomorphology in-between acquisition dates of the LiDAR and drone datasets; however, there is no clear evidence for this. Note that in both cases, the river was in very low flow conditions, so we expect a fairly accurate channel depth estimation from both datasets.</p> "> Figure 7
<p>Floodplain fill operations for LiDAR (<b>C</b>) and drone (<b>D</b>) DTMs as well as the cumulative distribution function (CDF) of floodplain heights (<b>B</b>). Contour lines at 2 m height intervals are shown in blue on the images. Note that the “probability of inundation” from the CDF (y-axis) in B indicates the “probability” of the entire floodplain area (shown in black in (<b>A</b>)) being inundated at a given floodplain height (x-axis).</p> ">
Abstract
:1. Introduction
- Cost-effective capture of fine-scale spatial data describing the current hydrological condition and water resource status of catchments at user-defined time-steps;
- Data capture at fine temporal resolution for describing water system dynamics in soil moisture, vegetation, and topography in catchments where there are important downstream effects on water resources (e.g., floods, erosion events or vegetation removal).
2. Methodology
2.1. Physical Description of the UAV and Equipment
2.2. Description of the Study Area and Conditions during Data Collection
2.3. Using a Structure from Motion Algorithm to Create a Digital Surface Model
2.4. Comparison between LiDAR and Drone DEM
2.4.1. LiDAR Data Description
2.4.2. Preparation of UAV Point Cloud to Compare with LiDAR
2.4.3. DEM Analysis
2.4.4. Characterizing First-Order Floodplain and Channel Hydraulics
3. Results and Discussion
3.1. Accuracy Assessment
3.2. River and Floodplain Characteristics
3.3. Implications for Flood Mapping and Prediction
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Property | LiDAR | UAV Scene |
---|---|---|
Spheroid | Global Reference System 1980 | World Geodetic System 1984 |
Horizontal Datum | North American Datum 83 | World Geodetic System 84 (1674) |
Vertical Datum | North American Datum 83 (NSRS 2007) | International Terrestrial Reference Frame 2008 |
Coordinate system | State Plane 1900 | Universal Transverse Mercator 18N |
Point spacing | 1.4 m | 0.068 m |
Scene/Relative accuracy | 18.5 cm RMSEz | 0.98 pixel = 3.32 cm (Agisoft scene error) |
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Schumann, G.J.-P.; Muhlhausen, J.; Andreadis, K.M. Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory. Remote Sens. 2019, 11, 982. https://doi.org/10.3390/rs11080982
Schumann GJ-P, Muhlhausen J, Andreadis KM. Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory. Remote Sensing. 2019; 11(8):982. https://doi.org/10.3390/rs11080982
Chicago/Turabian StyleSchumann, Guy J.-P., Joseph Muhlhausen, and Konstantinos M. Andreadis. 2019. "Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory" Remote Sensing 11, no. 8: 982. https://doi.org/10.3390/rs11080982