Lidar remote sensing has been used to survey stream channel and floodplain topography for decades. However, traditional platforms, such as aerial laser scanning (ALS) from an airplane, have limitations including flight altitude and scan angle that prevent the scanner from collecting a complete survey of the riverscape. Drone laser scanning (DLS) or unmanned aerial vehicle (UAV)-based lidar offer ways to scan riverscapes with many potential advantages over ALS. We compared point clouds and lidar data products generated with both DLS and ALS for a small gravel-bed stream, Stroubles Creek, located in Blacksburg, VA. Lidar data points were classified as ground and vegetation, and then rasterized to produce digital terrain models (DTMs) representing the topography and canopy height models (CHMs) representing the vegetation. The results highlighted that the lower-altitude, higher-resolution DLS data were more capable than ALS of providing details of the channel profile as well as detecting small vegetation on the floodplain. The greater detail gained with DLS will provide fluvial researchers with better estimates of the physical properties of riverscape topography and vegetation.

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.

Riverscapes are complex ecosystems consisting of dynamic processes influenced by spatially heterogeneous physical features. A critical component of riverscapes is vegetation in the stream channel and floodplain, which influences flooding and provides habitat. Riverscape vegetation can be highly variable in size and structure, including wetland plants, grasses, shrubs, and trees. This vegetation variability is difficult to precisely measure over large extents with traditional surveying tools. Drone laser scanning (DLS), or UAV-based lidar, has shown potential for measuring topography and vegetation over large extents at a high resolution but has yet to be used to quantify both the temporal and spatial variability of riverscape vegetation. Scans were performed on a reach of Stroubles Creek in Blacksburg, VA, USA six times between 2017 and 2019. Change was calculated both annually and seasonally over the two-year period. Metrics were derived from the lidar scans to represent different aspects of riverscape vegetation: height, roughness, and density. Vegetation was classified as scrub or tree based on the height above ground and 604 trees were manually identified in the riverscape, which grew on average by 0.74 m annually. Trees had greater annual growth and scrub had greater seasonal variability. Height and roughness were better measures of annual growth and density was a better measure of seasonal variability. The results demonstrate the advantage of repeat surveys with high-resolution DLS for detecting seasonal variability in the riverscape environment, including the growth and decay of floodplain vegetation, which is critical information for various hydraulic and ecological applications.

A floodplain in the headwaters of a tributary to the Chesapeake Bay, Ridge and Valley of the Eastern United States. This study investigated the influence of groundwater exchange in the annual wetland hydrologic budget and identified spatial and temporal variability in groundwater hydraulic gradients using an array of nested piezometers. Data showed that the created wetland met hydrologic success criteria, and that the wetland storage was fully connected with the groundwater table. Water-surface storage fluctuation was not fully explained by precipitation and evapotranspiration, suggesting that storage was highly influenced by groundwater inputs. The potentiometric surface showed that hillslope seep recharge was the dominant groundwater vector. However, during the summer and fall months, the adjacent stream channel was a losing system, and storm-driven rise in stream stage affected wetland storage. The complex hydrology of this relatively small wetland indicates that predicting the fluctuations of storage for design of unconfined floodplain wetlands is challenging, and that if the influence of groundwater seepage is negated, then fluctuations may be underestimated to the point of harming vegetation.

Accurate and precise measures of channel morphology are important when monitoring a stream post-restoration to determine changes in stability, water quality, and aquatic habitat availability. Practitioners often rely on traditional surveying methods such as a total station for measuring channel metrics (e.g., cross-sectional area, width, depth, and slope). However, these methods have limitations in terms of coarse sampling densities and time-intensive field efforts. Drone-based lidar or drone laser scanning (DLS) provides much higher resolution point clouds and has the potential to improve post-restoration monitoring efforts. For this study, a 1.3-km reach of Stroubles Creek (Blacksburg, VA, USA), which underwent a restoration in 2010, was surveyed twice with a total station (2010 and 2021) and twice with DLS (2017 and 2021). The initial restoration was divided into three treatment reaches: T1 (livestock exclusion), T2 (livestock exclusion and bank treatment), and T3 (livestock exclusion, bank treatment, and inset floodplain). Cross-sectional channel morphology metrics were extracted from the 2021 DLS scan and compared to metrics calculated from the 2021 total station survey. DLS produced 6.5 times the number of cross sections over the study reach and 8.8 times the number of points per cross section compared to the total station. There was good agreement between the metrics derived from both surveying methods, such as channel width (R² = 0.672) and cross-sectional area (R² = 0.597). As a proof of concept to demonstrate the advantage of DLS over traditional surveying, 0.1 m digital terrain models (DTMs) were generated from the DLS data. Based on the drone lidar data, from 2017 to 2021, treatment reach T3 showed the most stability, in terms of the least change and variability in cross-sectional metrics as well as the least erosion area and volume per length of reach.