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Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

1
Department of Biological Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA
2
Kleinschmidt Associates, Strasburg, PA 17579, USA
3
Department of Geography, Virginia Tech, Blacksburg, VA 24061, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Mateo Gašparović
Remote Sens. 2021, 13(13), 2616; https://doi.org/10.3390/rs13132616
Received: 31 May 2021 / Revised: 28 June 2021 / Accepted: 30 June 2021 / Published: 3 July 2021
Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance of the SfM-derived vegetation heights/roughness values if we were using a SfM DEM, although further work is needed. These results will help improve hydrodynamic modeling efforts, which are becoming increasingly important for management and planning in response to climate change, specifically in regions were high flow events are increasing. View Full-Text
Keywords: lidar; structure from motion; vegetative roughness; drones; unoccupied aerial system; floodplains; flooding; hydrodynamic modeling; HEC-RAS; flooding lidar; structure from motion; vegetative roughness; drones; unoccupied aerial system; floodplains; flooding; hydrodynamic modeling; HEC-RAS; flooding
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MDPI and ACS Style

Prior, E.M.; Aquilina, C.A.; Czuba, J.A.; Pingel, T.J.; Hession, W.C. Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry. Remote Sens. 2021, 13, 2616. https://doi.org/10.3390/rs13132616

AMA Style

Prior EM, Aquilina CA, Czuba JA, Pingel TJ, Hession WC. Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry. Remote Sensing. 2021; 13(13):2616. https://doi.org/10.3390/rs13132616

Chicago/Turabian Style

Prior, Elizabeth M., Charles A. Aquilina, Jonathan A. Czuba, Thomas J. Pingel, and W. C. Hession. 2021. "Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry" Remote Sensing 13, no. 13: 2616. https://doi.org/10.3390/rs13132616

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