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

We compared high-resolution drone laser scanning (DLS) and structure from motion (SfM) photogrammetry-derived vegetation heights at the Virginia Tech StREAM Lab to determine Manning's roughness coefficient. We utilized two calibrated approaches and a calculated approach to estimate roughness from the two data sets (DLS and SfM), then utilized them in a two-dimensional (2D) hydrodynamic model (HEC-RAS). The calculated approach used plant characteristics to determine vegetative roughness, while the calibrated approaches involved adjusting roughness values until model outputs approached values of field data (e.g., velocity probe and visual observations). We compared the model simulations to seven actual high-flow events during the fall of 2018 and 2019 using measured field data (velocity sensors, groundwater well height, marked flood extents). We used a t-test to find that all models were not significantly different to water surface elevations from our 18 wells in the floodplain (p > 0.05). There was a decrease in RMSE (-0.02 m) using the calculated compared to the calibrated models. Another decrease in RMSE was found for DLS compared to SfM (-0.01 m). This increase might not justify the increased cost of a DLS setup over SfM (~$150,000 versus ~$2,000), though future studies are needed. Our results inform hydrodynamic modeling efforts, which are becoming increasingly important for management and planning as we experience increasing high-flow events in the eastern United States due to climate change.