Welcome to this short description of lidar-toolbox: a MATLAB set of libraries to handle nearshore wave data collected with lidar scanners. The two main features are the libraries for gridding multibeam lidar (lidar-gridding) and performing spectral, cross-spectral and bispectral analysis (spectral-analysis). In essence, the spectral-analysis library is an adaptation of my bispectral-analysis library to gappy data collected by multibeam lidar scanners (https://github.com/ke-martins/bispectral-analysis). This adaptation was motivated while developing the nonlinear, lidar-based, nearshore depth inversion algorithm and working on the manuscript submitted to CENG (Martins et al., submitted).
At the moment, there are 4 inter-dependent sub-libraries, each dedicated to specific applications described below. This is expected to grow in the future, and include libraries for data acquisition/pre-processing for instance. In the examples folder, there are several example scripts to grid multibeam lidar data and perform spectral analysis on such gridded data. Additionally, a complete workflow example of the implementation of the Boussinesq-based neashore depth inversion method of Martins et al. (2023) to field lidar datasets is presented. This example is released as a accompanying code for the manuscript submitted to CENG (Martins et al., see references list).
Latest updates:
Contact me:
Kévin Martins
CNRS researcher at UMR 7266 LIENSs, CNRS - La Rochelle University, France
[email protected]
📂 lidar-gridding
Description:
Library containing functions that grid (spatial and temporal interpolations) data collected by single- and multibeam lidar scanners. It is based on the built-in function scatteredInterpolant (Delaunay triangulation) and makes the most of 4D (t,x,y,z) point clouds to interpolate raw data on grids and respect some quality criteria to prevent gaps over-filling.
List of functions:
fun_gridded_lidar_diagnostics.m
fun_singlebeam_lidar_gridding.m
fun_multibeam_lidar_gridding.m
📂 gappy-series-preprocessing
Description:
Functions for pre-processing gappy data series so that spectral, cross-spectral and bispectral analyses can be applied to them. Series are essentially reorganised by blocks, and quality is controlled through the number of NaNs allowed per block.
List of functions:
fun_count_pNaNs.m
fun_interp_series.m
fun_prep_gappy_series_by_block.m
fun_prep_gappy_series_by_block_xs.m
📂 spectral-analysis
Description:
Functions needed to perform spectral, cross-spectral and bispectral analyses on gappy free surface elevation timeseries of ocean waves measured with lidars. Timeseries should be pre-organised in matrices with the library gappy-series-preprocessing. For users interested in bispectral products, it also directly contains relevant functions for a range of nearshore applications (wave dispersive properties, non-linear energy transfers between triads etc).
List of functions:
fun_compute_spectrum_mat.m
fun_compute_cross_spectrum_mat.m
fun_compute_bispectrum_mat.m
fun_compute_krms.m
fun_compute_krms_terms.m
fun_compute_Snl.m
fun_compute_edof.m
📂 bulk-wave-speed
Description:
Function to compute sub-resolution lag between two timeseries and corresponding bulk celerity.
List of functions:
fun_compute_c_from_xcorr.m
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Martins, K., Bonneton, P., de Viron, O., Turner, I. L., Harley, M. D., & Splinter, K. (2023). New Perspectives for Nonlinear Depth‐Inversion of the Nearshore Using Boussinesq Theory. Geophysical Research Letters 50(2), e2022GL100498. https://doi.org/10.1029/2022GL100498
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Martins, K., Brodie, K. L., Fiedler, J. W., O'Dea, A. M., Spore, N. J., Grenzeback, R. L., Dickhudt, P. J., Bak, S. de Viron, O., Bonneton, P. Seamless nearshore topo-bathymetry reconstruction from lidar scanners: a Proof-of-Concept based on a dedicated field experiment at Duck, NC. submitted to Coastal Engineering.