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

Abstract

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.

Quantifying the Spatial Variability of Annual and Seasonal Changes in Riverscape Vegetation Using Drone Laser Scanning

“…In this study, we quantified and classified riverscape vegetation at 0.1-m resolution using an active system (i.e., DLS). Other studies have classified vegetation using passive systems, such as drone-based imagery [7,8,31]. Woodget et al [7] used drone-based imagery and SfM to create 0.02-m DEMs and classified images with a mean elevation error of 0.05 m, similar to the elevation errors observed in our study.…”

“…Resop et al [9] scanned multiple streambanks and the floodplain using DLS in 2017, demonstrating the potential of DLS to perform change detection over the entire riverscape. Prior et al [31] used DLS and SfM data for this reach from 2018 to estimate hydraulic roughness based on vegetation height and velocity data.…”

“…Both methods have been effective at estimating roughness, but have only recently been explored with DLS. Prior et al [31] estimated Manning's n from both DLSand SfM-derived CHMs through empirical methods as well as by classifying vegetation (i.e., grass, scrub, small trees, and large trees) and calibrating roughness with observed stream velocity data and 2D HEC-RAS.…”

The Manning’s Roughness Coefficient Calibration Method to Improve Flood Hazard Analysis in the Absence of River Bathymetric Data: Application to the Urban Historical Zamora City Centre in Spain

“…There have been many studies on friction parameter estimation, especially on a relationship between estimated Manning's coefficients and river bed conditions. These range from the classical tables and lists [57,58], to present-day estimations using fractals and connectivity [59,60] from remote sensing information [61], as well as including visual guides [45] and technical determination procedures [62,63]; all of these methods can be grouped in two kinds of approaches: (i) grain size-roughness relationships for different river bottom patches or polygons and (ii) micro-topographical analyses of bathymetrical data.…”

“…However, in any case, a fully spatially distributed Manning's coefficient based upon the physical characteristics of terrain (mainly the riverbed characteristics) was not achieved due to the complex distribution and the high degree of spatial variability in the physical characteristics of the terrain (grain-size and micro-topography distributions) and vegetation. This objective is already feasible for floodplains [61] by using UAVs, but not for the submerged areas of river channels.…”