Mapping the Shallow Water Seabed Habitat With the SHOALS

Collin, Antoine; Archambault, Philippe; Long, Bernard

doi:10.1109/tgrs.2008.920020

Cited by 81 publications

(47 citation statements)

References 24 publications

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“…The LiDAR equation in the water column can be expressed as follows based on the description of Allocca et al (2002) [15] and Collin et al (2008) [20]:…”

Section: Parameters Of the Volume Backscatter Returnmentioning

confidence: 99%

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

Zhao

Zhang

et al. 2018

Remote Sensing

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Airborne LiDAR bathymetry (ALB) has been shown to have the ability to retrieve water turbidity using the waveform parameters (i.e., slopes and amplitudes) of volume backscatter returns. However, directly and accurately extracting the parameters of volume backscatter returns from raw green-pulse waveforms in shallow waters is difficult because of the short waveform. This study proposes a new accurate and efficient method for the remote sensing of suspended sediment concentrations (SSCs) in shallow waters based on the waveform decomposition of ALB. The proposed method approaches raw ALB green-pulse waveforms through a synthetic waveform model that comprises a Gaussian function (for fitting the air-water interface returns), triangle function (for fitting the volume backscatter returns), and Weibull function (for fitting the bottom returns). Moreover, the volume backscatter returns are separated from the raw green-pulse waveforms by the triangle function. The separated volume backscatter returns are used as bases to calculate the waveform parameters (i.e., slopes and amplitudes). These waveform parameters and the measured SSCs are used to build two power SSC models (i.e., SSC (C)-Slope (K) and SSC (C)-Amplitude (A) models) at the measured SSC stations. Thereafter, the combined model is formed by the two established C-K and C-A models to retrieve SSCs. SSCs in the modeling water area are retrieved using the combined model. A complete process for retrieving SSCs using the proposed method is provided. The proposed method was applied to retrieve SSCs from an actual ALB measurement performed using the Optech Coastal Zone Mapping and Imaging LiDAR in a shallow and turbid water area. A mean bias of 0.05 mg/L and standard deviation of 3.8 mg/L were obtained in the experimental area using the combined model.

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“…The LiDAR equation in the water column can be expressed as follows based on the description of Allocca et al (2002) [15] and Collin et al (2008) [20]:…”

Section: Parameters Of the Volume Backscatter Returnmentioning

confidence: 99%

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

Zhao

Zhang

et al. 2018

Remote Sensing

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“…These highly accurate mapping systems ensure full coverage of the coastal zone, from the backshore to depths of 2 or 3 Secchi depths (with hydrographic systems), and detailed studies of coastal and submarine environments (Riley 1995, Pope et al 1997, Irish et al 2000, Collin et al 2008, Collin et al 2010, Cottin et al 2010.…”

Section: Alb Surveymentioning

confidence: 99%

“…Assuming the celerity of light in physical elements, each bin, i.e. one nanosecond, corresponds to a vertical resolution of 0.15 m and 0.1125 m in air and water respectively (Collin et al 2008). In this study, only the green "deep" receiver signal (out of the 4 channels) was analyzed.…”

Section: Alb Surveymentioning

confidence: 99%

“…Several regions of interest (ROIs) were chosen to reflect the diversity of bedforms and sedimentological facies at Paspebiac, and ALB data were extracted and converted into a DEM with a spatial resolution of 1x1 m². An IDL-ENVI protocol, based on Collin et al (2008) protocol, was then used to identify the laser waveforms located within each ROIs, to isolate the benthic returns and to compute basic statistical parameters (mean, variance, skewness coefficient, kurtosis coefficient…). Erroneous data were corrected by using a nearest neighbor interpolation and depth attenuation was corrected by a non-linear bathymetric regression.…”

Section: Alb Data Processingmentioning

confidence: 99%

“…Airborne LiDAR bathymetry (ALB) has proven to be an efficient laser-based tool to monitor coastal environments and to map biological habitats (Collin et al 2008(Collin et al , 2010 or geomorphology (Cottin et al 2009, Xhardé et al, 2010. In this study, the possibility to get sedimentological (porosity, compaction) and hydrodynamical (suspended sediment concentration, turbulence) information from ALB surveys was investigated.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Airborne Lidar Bathymetry Applied to Coastal Hydrodynamic Processes

Long¹,

Aucoin²,

Montreuil³

et al. 2011

Int. Conf. Coastal. Eng.

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In this study, the possibility to get sedimentological (density, compaction) and hydrodynamical (suspended sediment concentration, turbulence) information from ALB surveys is demonstrated. ALB laser reflectance was found to be higher on the lee-side and on the crest of the ripple bedform than in the trough and on the stoss-side. Moreover, laser reflectance was also found to be higher on "active" subtidal dunes, located within the depth of action of the waves, than on deeper "passive" bedforms. Based on lab experiments conducted in a hydraulic flume under X-ray CT-Scanner, these observations were related to sediment compaction and suspended sediment.

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2011

Remote Sensing and Global Environmental Change

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Mapping the Shallow Water Seabed Habitat With the SHOALS

Abstract: International audienc

Cited by 81 publications

References 24 publications

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

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

Airborne Lidar Bathymetry Applied to Coastal Hydrodynamic Processes

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