A simulator (Wa-LiD) was developed to simulate the reflection of LiDAR waveforms from water across visible wavelengths. The specific features of the simulator include (i) a geometrical representation of the water surface properties, (ii) the use of laws of radiative transfer in water adjusted for wavelength and the water's physical properties, and (iii) modelling of detection noise and signal level due to solar radiation. A set of simulated waveforms was compared with observed LiDAR waveforms acquired by the HawkEye airborne and GLAS satellite systems in the near-infra red or green wavelengths and across inland or coastal waters. Signalto-noise ratio (SNR) distributions for the water surface and bottom waveform peaks are compared with simulated and observed waveforms. For both systems (GLAS and HawkEye), Wa-LiD simulated SNR conform to the observed SNR distributions.

International audienceThis work aimed to prospect future space-borne LiDAR sensor capacities for global bathymetry over inland and coastal waters. The sensor performances were assessed using a methodology based on waveform simulation. A global representative simulated waveform database is first built from the Wa-LiD (Water LiDAR) waveform simulator and from distributions of water parameters assumed to be representative at the global scale. A bathymetry detection and estimation process is thus applied to each waveform to determine the bathymetric measurement probabilities in coastal waters, shallow lakes, deep lakes and rivers for a range of water depths. Finally, with a sensitivity analysis of waveforms, the accuracy and some limiting factors of the bathymetry are identified for the dominant water parameters. Two future space-borne LiDAR sensors were explored: an ultraviolet (UV) LiDAR and a green LiDAR. The results show that the bathymetric measurement probabilities at a 1 m depth is 63%, 54%, 24% and 19% with the green LiDAR for deep lakes, coastal waters, rivers and shallow lakes, respectively, and 10%, 22%, 1% and 0% with the UV LiDAR, respectively. The threshold values of dominant water parameters (sediment, yellow substance and phytoplankton concentrations) above which bathymetry detection fails were identified and mapped. The accuracy on the bathymetry estimates for both LiDAR sensors is 2.8 cm for one standard deviation with negligible bias (approximately 0.5 cm). However, these accuracy statistics only include the errors coming from the digitizing resolution and the inversion algorithm

Abstract:The Ice, Cloud and Land Elevation Satellite (ICESat) laser altimetry mission from 2003 to 2008 provided an important dataset for elevation measurements. The quality of GLAS/ICESat (Geoscience Laser Altimeter System) data was investigated for Lake Leman in Switzerland and France by comparing laser data to hydrological gauge water levels. The correction of GLAS/ICESat waveform saturation successfully improved the quality of water elevation data. First, the ICESat elevations and waveforms corresponding to water footprints across the transition from the land to water were analyzed. Water elevations (2 to 10 measurements) following the land-water transition are often of lesser quality. The computed accuracy for the ICESat elevation measurements is approximately 5 cm, excluding transitions footprints, and 15 cm, including these footprints. Second, the accuracy of ICESat elevation was studied using data acquired on French rivers with a width greater than the size of the ICESat footprint. The obtained root mean square error (RMSE) for ICESat elevations in regard to French rivers was 1.14 m (bias = 0.07 m; standard deviation = 1.15 m), which indicates that small rivers could not be monitored using ICESat with acceptable accuracy due to land-water transition sensor inertia.

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International audienceA new approach based on a mixture of Gaussian and quadrilateral functions was developed to process bathymetric lidar waveforms. The approach was tested on two simulated data sets obtained from the existing Water-LIDAR (Wa-LID) waveform simulator. The first simulated data set corresponds to a sensor configuration modeled after a possible future satellite bathymetric lidar sensor that was previously studied. The second simulated data set corresponds to a lidar airborne configuration modeled using the HawkEye airborne lidar parameters. In the proposed approach, the lidar waveform is fitted into a combination of three functions, two Gaussians for both the water surface and water bottom contributions and a quadrilateral function to fit the water column contribution. The results show more accurate bathymetry estimates compared with the use of a triangular function to fit the column contribution or a simple peak detection method. For the satellite configuration, the bias is improved by 16.8 and 0.8 cm compared with the peak detection method and the use of a triangular function, respectively. For the airborne configuration, the bias is improved by 10.0 and 2.4 cm compared with the peak detection method and the use of a triangular function, respectively. The proposed waveform fitting using the quadrilateral function underestimates the bathymetry by −5.0 and −6.1 cm for the simulated satellite and airborne data sets, respectively. The standard deviations of the bathymetry estimates are 6.0 and 8.2 cm, respectively. The obtained biases are inherent to overlaps between functions fitting the water surface, column, and bottom contributions