Browsing by Author "Aquilina, Charles A."

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    Assessing Seasonal Changes of Spatial Complexity in Riverscapes using Drone-Based Laser Scanning
    Aquilina, Charles A.; Hession, W. Cully; Lehman, Laura; Resop, Jonathan P. (Virginia Tech, 2019-04-26)
    Light detection and ranging (lidar) is a form of remote sensing using laser pulses to measure distances. Recent advancement in lidar technology has made units small enough to mount on drones, which makes high-quality data more accessible. Recent studies have utilized drone-based photogrammetry to measure characteristics of streams and rivers, as well as their associated riparian areas. These areas have been referred to as riverscapes. The physical characteristics of riverscapes are traditionally difficult to measure due to ever-changing characteristics across space and time. Drone-based laser scanning (DLS), is uniquely positioned to measure changing physical characteristics as it allows for increased temporal (daily, monthly, seasonal flights) and spatial (more than 400 pts/m2 at 30-m flight elevation) resolutions. It has more upfront costs compared to photogrammetry, as a DLS system (large drone and lidar) is vastly more expensive than a small drone with a digital camera payload. However, lidar can penetrate through vegetation, allowing for high-quality ground data, as well as vegetation points, which is a limitation of photogrammetry. One use of this ground and vegetation data is to analyze small changes of the topography to estimate complexity (an important habitat variable), as well as obstructions to flow such as vegetation. These obstructions to flow result in increased roughness, which is an important metric in biological studies and hydraulic modeling. In previous studies, estimating roughness was limited to visual observations or back-calculating from flow measurements, which can be time consuming and does not produce continuous spatial data. Using DLS-derived ground and vegetation, we will monitor small changes in vegetation and topography over the course of the stream both longitudinally, laterally, and through time. We will test various methods of computing roughness from detailed lidar point clouds to determine roughness. Some possibilities estimating roughness and complexity include the standard deviation of the elevation change, the variation between maximum and minimum elevations in a pixel, slope variability, surface roughness factors, and others. These values can be compared to a calibrated 2D hydraulic flood modeling (HEC-RAS), DLS has the potential to change the way we map and understand spatial complexity and habitat characteristics of riverscapes.
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    Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry
    Prior, Elizabeth M.; Aquilina, Charles A.; Czuba, Jonathan A.; Pingel, Thomas J.; Hession, W. Cully (MDPI, 2021-07-03)
    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.
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    Estimating Floodplain Vegetative Roughness using Drone-Based Laser Scanning and Structure from Motion Photogrammetry
    Aquilina, Charles A. (Virginia Tech, 2020-08-20)
    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.