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

Bouhdaoui, A.; Bailly, Jean-Stéphane; Baghdadi, Nicolas; Abady, Lydia

doi:10.1109/lgrs.2013.2292814

Cited by 27 publications

(24 citation statements)

References 15 publications

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“…Given the speed of light in water ( c w =2.232 × 10 8 ms −1 ), this amount of time corresponds to a specific distance traveled through the water column. Based on this logic and earlier work by Allouis et al (, Equation 3) and Bouhdaoui et al (, Equation 9), we calculated minimum detectable depth as

d_{\min} = \frac{1}{2} c_{w} normalΔ t

…”

Section: Methodsmentioning

confidence: 99%

“…; Bouhdaoui et al. ). For a number of reasons, then, directly evaluating the accuracy of remotely sensed bed elevations via comparison to field measurements remains necessary.…”

Section: Introductionmentioning

confidence: 99%

“…incorporated these concepts into a forward image model for assessing the utility of data acquired by a specific sensor from a particular river of interest. An analogous forward model does not exist for bathymetric LiDAR, although waveform simulation algorithms could serve as a starting point (Abdallah et al, 2012;Bouhdaoui et al, 2014). For a number of reasons, then, directly evaluating the accuracy of remotely sensed bed elevations via comparison to field measurements remains necessary.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Legleiter

Overstreet

Glennie

et al. 2015

Earth Surf Processes Landf

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Remote sensing is a powerful tool for examining river morphology. This study used detailed field surveys to assess the capability of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR to measure bed elevations in rivers with disparate optical characteristics. Field measurements of water column optical properties in the clear Snake River, the more complex Blue and Colorado, and highly turbid Muddy Creek were used to calculate depth retrieval precision and dynamic range. Differences in depth of a few centimeters were detectable via passive optical techniques in the clearest stream, but precision was greatly reduced under turbid conditions. The bathymetric LiDAR evaluated in this study could not detect shallow depths or differences in depth smaller than 11 cm owing to the difficulty of distinguishing water surface and bottom returns in laser waveforms. In clear water and with high radiometric resolution, hyperspectral systems such as CASI could detect depths approaching 10 m, but semi-empirical analysis of the Aquarius LiDAR indicated that maximum detectable depths were of the order of 2-3 m in the clear-flowing Snake River, and closer to 1 m in the more turbid streams. Turbidity also constrained spectrally based depth retrieval, and depth estimates from the Blue/Colorado were far less reliable than on the Snake. Both sensors yielded positively biased (0.03 m for CASI, 0.08 m for Aquarius) bed elevations on the Snake, with precisions of 0.16-0.17 m. For the Blue/Colorado, mean errors were of the order of 0.2 m, biased shallow for optical data and biased deep for LiDAR, although no Aquarius laser returns were recorded from the deepest parts of these channels; precisions were reduced to 0.29-0.32 m. Both approaches have advantages and limitations, and prospective users must understand the capabilities and constraints associated with various types of remote sensing to ensure efficient use of these evolving technologies.

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d_{\min} = \frac{1}{2} c_{w} normalΔ t

…”

Section: Methodsmentioning

confidence: 99%

“…; Bouhdaoui et al. ). For a number of reasons, then, directly evaluating the accuracy of remotely sensed bed elevations via comparison to field measurements remains necessary.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Legleiter

Overstreet

Glennie

et al. 2015

Earth Surf Processes Landf

View full text Add to dashboard Cite

show abstract

“…Extracting the signals of the water surface and the bottom part from an echo waveform is the key to retrieving bathymetry based on the LiDAR waveform. The retrieving methods and parameters of the sounding system were not disclosed, but they seem to apply the commonly used peak detection method [9,18] and mathematical fitting method [12,38,39]. A burr phenomenon always exists in the echo waveform signals arising from noise.…”

Section: Depth Extraction From Waveform Datamentioning

confidence: 99%

Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image

Zhang

et al. 2019

Applied Sciences

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Optical remote sensing is an effective means of water depth measurement, but the current approach of mainstream bathymetric retrieval requires a large amount of onsite measurement data. Such data are hard to obtain from places where underwater terrains are complicated and unsteady, and from sea areas affected by issues with rights and conflicts of interest. In recent years, the emergence of airborne light detection and ranging (LiDAR) provided a new technical means for field bathymetric survey. In this study, water depth inversion was carried out around an island far from the mainland by using remote sensing images and real LiDAR waveform data. Multi-Gaussian function fitting was proposed to extract water depth data from waveform data, and bathymetric values were used as control and validation data of the active and passive combination of water depth inversion. Results show that the relative error was 5.6% for the bathymetric retrieval from LiDAR waveform data, and the accuracy meets the requirements of ocean bathymetry. The average relative error of water depth inversion based on active and passive remote sensing was less than 9%. The method used in this study can also reduce the use of LiDAR data and the cost, thus providing a new idea for future coastal engineering application and construction.

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“…However, these coefficients can be regarded as constants in the same region and in a short period [20,22,[24][25][26]. F can be calculated using the sensor height H and LiDAR divergence angle γ [27] using the following equation:…”

Section: Introductionmentioning

confidence: 99%

Shallow Water Measurements Using a Single Green Laser Corrected by Building a Near Water Surface Penetration Model

Zhao

Zhang

et al. 2017

Remote Sensing

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Abstract:To reduce the size and cost of an integrated infrared (IR) and green airborne LiDAR bathymetry (ALB) system, and improve the accuracy of the green ALB system, this study proposes a method to accurately determine water surface and water bottom heights using a single green laser corrected by the near water surface penetration (NWSP) model. The factors that influence the NWSP of green laser are likewise analyzed. In addition, an NWSP modeling method is proposed to determine the relationship between NWSP and the suspended sediment concentration (SSC) of the surface layer, scanning angle of a laser beam and sensor height. The water surface and water bottom height models are deduced by considering NWSP and using only green laser based on the measurement principle of the IR laser and green laser, as well as employing the relationship between NWSP and the time delay of the surface return of the green laser. Lastly, these methods and models are applied to a practical ALB measurement. Standard deviations of 3.0, 5.3, and 1.3 cm are obtained by the NWSP, water-surface height, and water-bottom height models, respectively. Several beneficial conclusions and recommendations are drawn through the experiments and discussions.Keywords: airborne LiDAR bathymetry; near water surface penetration; water surface height; water bottom height; suspended sediment concentration of the surface layer

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

Cited by 27 publications

References 15 publications

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Retrieval of Nearshore Bathymetry around Ganquan Island from LiDAR Waveform and QuickBird Image

Shallow Water Measurements Using a Single Green Laser Corrected by Building a Near Water Surface Penetration Model

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