# Estimating Directional Wave Spectra Properties in Non-Breaking Waves from a UAS-Mounted Multi-beam Lidar

Explore cutting-edge lidar wave measurement techniques with our study on UAS-mounted lidar for inner shelf wave spectra and directional moments.

**Oct 9, 2023**

This paper was written by F. Feddersen, O. B. Marques, J. H. MacMahan, and R. L. Grenzeback.

### Abstract

Wave spectra and directional moment measurements are of scientific and engineering interest and are routinely measured with wave buoys. Recently both fixed and UAS-mounted lidar remote sensing have measured surf zone wave spectra. However, wave statistics seaward of the surf zone have not been measured with a lidar due to a lower number of returns and directional moments have not been measured at all. We use a multi-beam scanning lidar mounted on a gasoline-powered UAS to estimate wave spectra, slope spectra, and directional moments on the inner shelf in ≈ 10 m water depth from an 11-min hover and compare to a co-located wave buoy. Lidar returns within circular sampling regions with varying radius 𝑅 are fit to a plane and a 2D parabola, providing sea-surface and slope time series. Wave spectra across the sea-swell (0.04–0.4 Hz) are robustly estimated for 𝑅 ≥ 0.8 m. Estimating slope spectra is more challenging. Large 𝑅 works well in the swell band and smaller 𝑅 work well at higher frequencies, comparing well with a wave buoy inferred slope spectra. Directional Fourier coefficients are estimated from wave and slope spectra and cross-spectra and are compared to a wave buoy in the sea-swell band. Larger 𝑅 and the 2D parabola-fit yield better comparison to the wave buoy. Mean wave angles and directional spreads, functions of the directional Fourier coefficients, are well reproduced at 𝑅 = 2.4 m and the 2D-parabola fit, within the uncertainties of the wave buoy. This UAS-mounted multi-beam scanning lidar and this methodology can be used in regions where wave buoys are not easily deployable, e.g., near rocky coasts or cliffs.

[View Full Paper](http://falk.ucsd.edu/pdf/Feddersen2023JTECH_submit.pdf)