&lt;title&gt;Depth sounding lidar: an overview of Swedish activities and future prospects&lt;/title&gt;

Steinvall, Ove; Koppari, Kurt R.

doi:10.1117/12.258342

Cited by 12 publications

(8 citation statements)

References 1 publication

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“…Fig. 4 also demonstrates that the underestimation of the mean water depth highly depends on both the LiDAR footprint F p and the bottom slope θ but only slightly depends on the mean water depths for the four tested depthsZ = (2,5,8,12) m. We can model this underestimation with a generalized power law for given F p andZ values, i.e.,…”

Section: B Quantitative Peak Shifting Analysismentioning

confidence: 93%

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

Bouhdaoui

Bailly

Baghdadi

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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International audienceBathymetry is usually determined using the positions of the water surface and the water bottom peaks of the green LiDAR waveform. The water bottom peak characteristics are known to be sensitive to the bottom slope, which induces pulse stretching. However, the effects of a more complex bottom geometry within the footprint below semitransparent media are less understood. In this letter, the effects of the water bottom geometry on the shifting of the bottom peaks in the waveforms were modeled. For the sake of simplicity, the bottom geometry is modeled as a 1D sequence of successive contiguous segments with various slopes. The positions of the peaks in waveforms were deduced using a conventional peak detection process on simulated waveforms. The waveforms were simulated using the existing Wa-LID waveform simulator, which was extended in this study to account for a 1D complex bottom geometry. An experimental design using various water depths, bottom slopes, and LiDAR footprint sizes according to the design of satellite sensors was used for the waveform simulation. Power laws that explained the peak time shifting as a function of the footprint size and the water bottom slope were approximated. Peak shifting induces a bias in the bathymetry estimates that is based on a peak detection of up to 92% of the true water depth. This bias may also explain the frequent underestimation of the water depth from bathymetric airborne LiDAR surveys observed in various empirical studies

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Section: B Quantitative Peak Shifting Analysismentioning

confidence: 93%

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

Bouhdaoui

Bailly

Baghdadi

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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“…During the survey, we continuously obtained topographic points x, y, z inside and outside the water, in a Lambert Cartesian coordinate system. These points were obtained through green waveform processing, using the HawkEye Coastal Survey toolbox (Steinvall and Koppari, 1996).…”

Section: Hawkeye Lidar Datamentioning

confidence: 99%

Geostatistical estimations of bathymetric LiDAR errors on rivers

Bailly

Coarer²,

Languille³

et al. 2010

Earth Surf Processes Landf

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The geometry of river channels is a key descriptive element for hydromorphology, hydraulics and hydroecology. Gravel bed rivers usually have a mean water depth of ~0·5 m. For such shallow waters, the accuracy of bathymetric LiDAR data has to be precisely assessed. Alongside this accuracy investigation, methodological questions arise: How to assess the data quality of elevation LiDAR when comparing reference topographic points on river beds to laser beam footprints of several square metres at different locations? What are the consequences of uncertainties and scaling in accuracy estimations? In this study, we designed a methodology to assess the quality of LiDAR topographical data within rivers using a specifi c geostatistical method that conducts upscaling as well as interpolation of reference data that takes into account uncertainties. This method uses an anisotropic block kriging from DGPS points on LiDAR footprint areas within a channel-fi tted coordinate system. This assessment focused on a 1·5 km long reach of the Gardon gravel bed river, in the south of France. DGPS points pseudo-regularly located along the river were acquired at the same time as the LiDAR survey with the HawkEyeII system. LiDAR accuracy results for river bottom elevation show a negative bias for high depth. Added to that bias, a random error with 0·32 m standard deviation was found by considering upscaling and uncertainties in reference data, and a 0·20 m standard deviation was found if they were not considered. Consequently, if LiDAR bias can be corrected, measuring a water depth less than 32 cm, i.e. for 28% of the river area, is unrealistic.However, this experiment shows that LiDAR provides an accurate representation of the riverbed forms. It also provided a useful, continuous, topographic surface from the underwater river bed up to riparian areas.

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“…The former includes the water surface wave. The latter includes the slope and aspect which causes changes in the incident angle from different viewing orientations and stretches the bottom return (Steinvall et al 1994;Steinvall and Koppari 1996;Tulldahl and Steinvall 2004).…”

Section: Introductionmentioning

confidence: 99%

Bathymetric LiDAR Green Channel Derived Reflectance: An Experiment from the Dongsha 2010 Mission

Lin¹,

Shih²,

Chen³

et al. 2016

Terr. Atmos. Ocean. Sci.

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Bathymetric LiDAR utilizes a green laser capable of penetrating water and surveying seafloor topography. The intensity of the echo identified as the seafloor carries information about the substrate type. However, besides the reflectance characteristics of the substrate, there are also other influencing factors, including those from the LiDAR system and the environment. The data collected in the 2010 Dongsha atoll bathymetric LiDAR mission is processed and analyzed in this study. The corrections for environmental factors, mainly contributed from the water column that is presented as the inherent optical parameters of water are retrieved from the recorded green laser waveform. Those from the system, such as deviations between the signal receptors or between laser beam angles to each interface, are eliminated with data from IMU (Inertial Measurement Unit), scanner controlling mechanisms, etc. The resulting reflectance from each flight line is compared in the overlap area. The reflectance of the west side strip is subtracted from that of the east side strip. The reflectance is scaled between zero and one. While the mean of the differences is -0.0037, the standard deviation is 0.0436 for the flight line with a flight height of 400 m. The mean and standard deviation are -0.0058 and 0.0272, respectively, with flight height of 300 m. When interpolating the reflectance from the 300 m flight altitude dataset into a surface after subtracting the point measurement from the 400 m flight altitude, the mean and standard deviations are -0.0215 and 0.0382, respectively. This indicates that the consistency among flight lines of the same flight altitude is higher than those from different flight altitudes. A WorldView-2 (WV-2) image is compared with the LiDAR reflectance, and after atmospheric correction, the green band reflectance from WV-2 showed high similarities between the two image types. However, in the deep water region the one derived from LiDAR has much more information content.

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<title>Depth sounding lidar: an overview of Swedish activities and future prospects</title>

Cited by 12 publications

References 1 publication

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

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

Geostatistical estimations of bathymetric LiDAR errors on rivers

Bathymetric LiDAR Green Channel Derived Reflectance: An Experiment from the Dongsha 2010 Mission

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