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.

Small unoccupied aerial systems (sUAS) are increasingly used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors, low cost, precise manoeuvrability and navigation makes them versatile tools. The goal of this study is to investigate if sUAS multispectral imagery can be utilized to measure turbidity and total suspended solids (TSS) of small streams. sUAS multispectral imagery and water samples at varying depths were collected before and after rain events on three sampling dates in 2019 from Moores Creek in Lanett, Alabama (AL), United States of America (USA), which was restored in 2017. The water samples were processed for TSS and turbidity and related to pixel values from the multispectral imagery. Linear regression was used to develop models for TSS and turbidity. The models were then tested on Moores Mill Creek in Chewacla State Park, AL, USA. For Lanett, TSS and turbidity regression models for low flows had coefficients of determination (R 2) values of 0.77 and 0.78, respectively. During high flows, different single bands and band ratios were required for comparable R 2 values, suggesting separate models may be needed for high and low flow events. When the Lanett models were applied to Chewacla State Park, predicted TSS and turbidity were not comparable to measured values indicating that location-specific models may be required. Future research should incorporate depth as a variable since streambed visibility likely impacts results, along with other modelling and data analysis methods, such as machine learning.

Due to land development, high concentrations of suspended sediment are produced from erosion after rain events. Sediment basins are commonly used for the settlement of suspended sediments before discharge. Stormwater regulations may require frequent sampling and monitoring of these basins, both of which are time and labor intensive. Potential remedies are small, unoccupied aerial systems (sUAS). The goal of this study was to demonstrate whether sUAS multispectral imagery could measure high levels of total suspended solids (TSS) and turbidity in a sediment basin. The sediment basin at the Auburn University Erosion and Sediment Control Testing Facility was used to simulate a local 2-year, 24-h storm event with a 30-min flow rate. Water samples were collected at three depths in two locations every 15 min for six hours with corresponding sUAS multispectral imagery. Multispectral pixel values were related to TSS and turbidity in separate models using multiple linear regressions. TSS and turbidity regression models had coefficients of determination (r2) values of 0.926 and 0.851, respectively. When water column measurements were averaged, the r2 values increased to 0.965 and 0.929, respectively. The results indicated that sUAS multispectral imagery is a viable option for monitoring and assessing sediment basins during high-concentration events.

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Small unoccupied aerial systems (sUASs) are increasingly being used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors, low cost, and precise maneuverability and navigation make them a versatile tool for a field researcher. Procedures and instrumentation for sUASs are largely undefined, especially for atmospheric and hydrologic applications. The sUAS’s ability to collect atmospheric data for characterizing land–atmosphere interactions was examined at three distinct locations: Costa Rican rainforest, mountainous terrain in Georgia, USA, and land surfaces surrounding a lake in Florida, USA. This study aims to give further insight on rapid, sub-hourly changes in the planetary boundary layer and how land development alters land–atmosphere interactions. The methodology of using an sUAS for land–atmospheric remote sensing and data collection was developed and refined by considering sUAS wind downdraft influence and executing systematic flight patterns throughout the day. The sUAS was successful in gathering temperature and dew point data, including rapid variations due to changing weather conditions, at high spatial and temporal resolution over various land types, including water, forest, mountainous terrain, agriculture, and impermeable human-made surfaces. The procedure produced reliably consistent vertical profiles over small domains in space and time, validating the general approach. These findings suggest a healthy ability to diagnose land surface atmospheric interactions that influence the dynamic nature of the near-surface boundary layer.