Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman, near‐infrared and green signals

Abstract

Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non-navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is diffi cult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near-infrared (NIR), Raman] of the SHOALS-1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The fi rst method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classifi cation and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (−4·2 cm verusu −4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (−3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method.

Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry

“…The multiple returns can also benefit the classification of benthic layer as the last return of multiple returns are assigned as the seed benthic positions for the region growing classification algorithms. This algorithm is different from the method proposed by Allouis et al [24] who used NIR returns to estimate the water surface; here the mixed LiDAR signal produced by water surface and water bottom reflections was directly processed through the CWT to extract both surface and benthic locations. One of the challenges for single band bathymetric LiDAR is to recover both the water surface and bottom position from the full waveform.…”

“…In order to reduce the complexity of bathymetric LiDAR, multiple wavelengths (usually a NIR LiDAR system for water surface detection, and a green LiDAR system for water penetration) systems are normally used to facilitate benthic layer retrieval [12]. For example, Allouis et al [24] compared two new processing methods for depth extraction by using near-infrared (NIR), green and Raman LiDAR signals. By combining NIR and green waveforms, significantly more points are extracted by full waveform processing and better accuracy is achieved.…”

Canopy Height Estimation in French Guiana with LiDAR ICESat/GLAS Data Using Principal Component Analysis and Random Forest Regressions

“…Allouis et al [51] used PCA to estimate the water depth in shallow water using airborne LiDAR waveforms. Principal components were then used to perform a regression model between the principal components and water depth.…”

Quantifying River Channel Stability at the Basin Scale

“…One alternative is to develop a suite of cross-sections with width derived from a reliable DEM and channel depth interpolated from spot field measurements and available survey data where they exist. A current strand of research focuses on airborne bathymetric LiDAR (i.e., green waveform) that is able to penetrate water (for a review see [163]) and while that technology is currently limited to coarse resolutions and thus not suited to small streams [164] and is least reliable in shallow flows [165][166][167], the technology holds considerable promise for the future [168].…”