Potential of Space-Borne LiDAR Sensors for Global Bathymetry in Coastal and Inland Waters

Abstract

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

Remote Sensing of Suspended Sediment Concentrations Based on the Waveform Decomposition of Airborne LiDAR Bathymetry

“…Ceccaldi [31] tested the combinations of several functions to efficiently distinguish the waveform contributions. Of all the functions considered, Gaussian, triangle, and Weibull functions have been verified to yield a high performance in fitting the air-water interface, the volume backscatter and the bottom returns, respectively [31].…”

“…Of all the functions considered, Gaussian, triangle, and Weibull functions have been verified to yield a high performance in fitting the air-water interface, the volume backscatter and the bottom returns, respectively [31]. Abady et al [32] replaced the triangle function with a quadrilateral function to fit volume backscatter returns and assessed the method by using waveforms simulated by a Water LiDAR (Wa-LID) waveform simulator.…”

“…The quadrilateral function and the exponential decomposition need to be verified further by a lot of ALB data and water turbidity information. To conduct a simple and efficient decomposition, the method in reference [31] is adopted in this study.…”

Modeling the Water Bottom Geometry Effect on Peak Time Shifting in LiDAR Bathymetric Waveforms

“…The sensor parameters were fixed and chosen to agree with the configurations studied by the EADS-Astrium company (European Aeronautic Defence and Space Company) [13], particularly regarding laser energy. Because the limit of exposure to laser radiation (LLR) is 5.10 −3 J/m 2 for wavelengths between 400 and 700 nm (including the green laser with a 532-nm wavelength) [14], [15], the maximum allowed energy E 0 (J) by the LiDAR that meets the standards of ocular safety is defined as…”

“…Based on a previous review [13], the water parameter values were fixed and chosen to be representative of clear coastal waters with reflective bottoms [13], [16], [17]. The chosen sensor and water parameters are all listed in Tables I and II. Waveform simulations were performed for the following mean water depthsZ at the footprint scale: 2, 5, 8, and 12 m. Five different footprint sizes F p were also used: 5, 10, 20, 30, and 40 m. The slope segment angle was varied between 0 • and 50 • , with a step size of 1 • .…”