We explore the potential for using a small unmanned aerial vehicle (UAV) quadcopter to collect long-dwell imagery of the nearshore from which important measurements can be made at low cost and with flexibility. This paper extends the existing topographic imaging approaches that rely on having plentiful ground control spread across the image, to the nearshore case where the bulk of the image is water with no control point and vehicle metadata must be used. The UAV autopilot was found to be capable of excellent station-keeping with the positional errors of 0.20 and 0.53 m (horizontal and vertical) and the viewing angle errors of 0.25° (tilt and roll) and 0.38° (azimuth). The ground position of imaged objects could be found with 0.21-m accuracy. Metadata returned by the UAV on camera position was accurate at 5 m, and the camera roll could be assumed to be 0°, reducing the ground control requirements to two, or even one location. Even under this extreme simplification, ground position errors averaged only 10 m but were worse for cases when only control points near to the UAV were used. A model for the visual contrast of waves when viewed from different angles found that large tilts are important but, in contrast to theory, that there was little dependence on the viewing azimuthal angle. Derived Argus products agreed well with the same products collected using a traditional fixed Argus station. UAVs appear to be a very promising alternate to fixed camera systems if limited duration sampling is adequate.

This paper details the collection, geo-referencing, and data processing algorithms for a fully-automated, permanently deployed terrestrial lidar system for coastal monitoring. The lidar is fixed on a 4-m structure located on a shore-backing dune in Duck, North Carolina. Each hour, the lidar collects a three-dimensional framescan of the nearshore region along with a 30-min two-dimensional linescan time series oriented directly offshore, with a linescan repetition rate of approximately 7 Hz. The data are geo-referenced each hour using a rigorous co-registration process that fits 11 fixed planes to a baseline scan to account for small platform movements, and the residual errors from the fit are used to assess the accuracy of the rectification. This process decreased the mean error (defined as the magnitude of the offset in three planes) over a two-year period by 24.41 cm relative to using a fixed rectification matrix. The automated data processing algorithm then filters and grids the data to generate a dry-beach digital elevation model (DEM) from the framescan along with hourly wave runup, hydrodynamic, and morphologic statistics from the linescan time series. The lidar has collected data semi-continuously since January 2015 (with gaps occurring while the lidar was malfunctioning or being serviced), resulting in an hourly data set spanning four years as of January 2019. Examples of data products and potential applications spanning a range of spatial and temporal scales relevant to coastal processes are discussed.

A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from-motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with roor-mean-square error (RMSE)/bias of 0.26/-0.05 and 0.34/-0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System-Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS-INS input and no GCPs. The resultant DSM products are comparable [root-mean-squared difference (RMSD)/bias of 0.62/-0.09 m, and processing time is significantly reduced.

The transformation of surface gravity waves from 11 m depth to runup was observed on the low‐sloped (1/80) Agate Beach, Oregon, with a cross‐shore transect of current meters, pressure sensors, and a scanning lidar. Offshore wave heights H₀ ranged from calm (0.5 m) to energetic (>7 m). Runup, measured with pressure sensors and a scanning lidar, increases linearly with (H₀L₀)^1/2, with L₀ the deep‐water wavelength of the spectral peak. Runup saturation, in which runup oscillations plateau despite further increases in (H₀L₀)^1/2, is not observed. Infragravity wave shoaling and nonlinear energy exchanges with short waves are included in an infragravity wave energy balance. This balance closes for high‐infragravity frequencies (0.025–0.04 Hz) but not lower frequencies (0.003–0.025 Hz), possibly owing to unmodeled infragravity energy losses of wave breaking and/or bottom friction. Dissipative processes limit, but do not entirely damp, increases in runup excursions in response to increased incident wave forcing.

Observations of waves and setup on a steep, sandy beach are used to identify and assess potential applications of spatially dense lidar measurements for studying inner-surf and swash-zone hydrodynamics. There is good agreement between lidar- and pressure-based estimates of water levels (r² = 0.98, rmse = 0.05 m), setup (r² = 0.92, rmse = 0.03 m), infragravity wave heights (r² = 0.91, rmse = 0.03 m), swell–sea wave heights (r² = 0.87, rmse = 0.07 m), and energy density spectra. Lidar observations did not degrade with range (up to 65 m offshore of the lidar) when there was sufficient foam present on the water surface to generate returns, suggesting that for narrow-beam 1550-nm light, spatially varying spot size, grazing angle affects, and linear interpolation (to estimate the water surface over areas without returns) are not large sources of error. Consistent with prior studies, the lidar and pressure observations indicate that standing infragravity waves dominate inner-surf and swash energy at low frequencies and progressive swell–sea waves dominate at higher frequencies. The spatially dense lidar measurements enable estimates of reflection coefficients from pairs of locations at a range of spatial lags (thus spanning a wide range of frequencies or wavelengths). Reflection is high at low frequencies, increases with beach slope, and decreases with increasing offshore wave height, consistent with prior studies. Lidar data also indicate that wave asymmetry increases rapidly across the inner surf and swash. The comparisons with pressure measurements and with theory demonstrate that lidar measures inner-surf waves and setup accurately, and can be used for studies of inner-surf and swash-zone hydrodynamics.