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

Zhao, Jianhu; Zhao, Xinglei; Zhang, Hongmei; Zhou, Fengnian

doi:10.3390/rs9050426

Cited by 35 publications

(37 citation statements)

References 32 publications

(42 reference statements)

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“…Consequently, the distance measurement accuracy could be improved. To evaluate the degree of accuracy improvement quantitatively, parameter δ could be calculated for each point as shown by Equation (13). The mean values of δ were 64.87%, 58.33%, 62.46%, and 36.42% for the points in Figure 7a-d, respectively.…”

Section: Discussion and Validationmentioning

confidence: 99%

“…However, these errors can significantly increase to centimeter and even decimeter levels [8,10] for smooth surfaces because diffuse and specular reflections exist in all-natural surfaces and the type of reflection affects the direction and strength of backscattered light. For targets with a relatively smooth or glossy surface (e.g., water [11][12][13], fresh ice [14][15][16], foliage/leaves [17,18], metal [19], porcelain, and plastic [20,21]), specular reflections are dominant over diffuse reflections. For a generally smooth surface of multiple facets with different orientations, specular reflections are distributed in the surroundings of the reflection angle direction [22].…”

Section: Introductionmentioning

confidence: 99%

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Investigation of TLS Intensity Data and Distance Measurement Errors from Target Specular Reflections

et al. 2018

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Terrestrial laser scanners (TLSs) can provide accurate and high-resolution data by measuring the distances (ranges) between the scanned points and the scanner center using time-of-flight or phase-shift-based methods. Distance measurement accuracy is of vital importance in TLSs and mainly influenced by instrument mechanism, atmospheric conditions, scanning geometry, and target surface properties. In general, existing commercial TLSs can achieve millimeter precision. However, significant errors (centimeter and even decimeter levels) beyond the instruments' nominal accuracy exist in distance observations for targets with highly reflective surfaces whose specular reflections are dominant because these reflections can increase the backscattered laser signal power considerably and cause further disorder in the echo detection and recognition by TLS photodetectors. Apart from distance, the intensity value derived from the backscattered signal and influenced by the same factors as that of the distance measurement errors is recorded by TLSs. A certain link exists between the two instrumental observations. In this study, the anomalous distance measurement errors caused by target specular reflections are explored. The different planar reflective targets scanned by a Faro Focus 3D 120 terrestrial scanner are used to experimentally investigate the relationship between the original intensity values and the distance measurement errors. Results imply that the distance measurement errors caused by specular reflections are not as erratic as they ostensibly seem. On the contrary, distance measurement errors are strongly related to the original intensity values. A polynomial can be established to empirically model the relationship between the original intensity data and the distance measurement errors. With use of the original intensity to compensate for the measured distance observations, the point cloud data accuracy can be improved by approximately 55.52%.

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Section: Discussion and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of TLS Intensity Data and Distance Measurement Errors from Target Specular Reflections

et al. 2018

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“…Therefore, the specific research questions are (i) if the single photon sensitivity results in sufficiently many echoes to enable high-resolution water surface mapping, and (ii) if water surface level underestimation is less compared to conventional LiDAR. The underestimation effect is well known from literature for green laser radiation [1,3,23]. In addition, we investigate (iii) whether the flight mission parameters generally optimized for topographic mapping are equally suitable for water surface detection, or otherwise, which settings need to be adapted to enable high-resolution water level mapping.…”

Section: Introductionmentioning

confidence: 95%

On the Feasibility of Water Surface Mapping with Single Photon LiDAR

Mandlburger

Jutzi

2019

IJGI

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Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable of penetrating clear shallow water. The technology is designed for large-area topographic mapping, which also includes the water surface. While the penetration capabilities of green lasers generally lead to underestimation of the water level heights, we specifically focus on the questions of whether Single Photon LiDAR (i) is less affected in this respect due to the high receiver sensitivity, and (ii) consequently delivers sufficient water surface echoes for precise high-resolution water surface reconstruction. After a review of the underlying sensor technology and the interaction of green laser light with water, we address the topic by comparing the surface responses of actual Single Photon LiDAR and Multi-Photon Topo-Bathymetric LiDAR datasets for selected horizontal water surfaces. The anticipated superiority of Single Photon LiDAR could not be verified in this study. While the mean deviations from a reference water level are less than 5 cm for surface models with a cell size of 10 m, systematic water level underestimation of 5–20 cm was observed for high-resolution Single Photon LiDAR based water surface models with cell sizes of 1–5 m. Theoretical photon counts obtained from simulations based on the laser-radar equation support the experimental data evaluation results and furthermore confirm the feasibility of Single Photon LiDAR based high-resolution water surface mapping when adopting specifically tailored flight mission parameters.

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“…To obtain the accurate slopes and amplitudes of volume backscatter returns, the waveform decomposition method depicted in Section 2.2 was adopted. The volume backscatter returns are separated from the superposed surface returns by establishing the green waveform model (see Equation (12)) using the raw waveforms. The green waveform model enables formation of the waveforms of the air-water interface returns (see Figure 5a,c,e,g) and corresponding volume backscatter returns (see Figure 5b,d,f,h).…”

Section: Data Acquisitionmentioning

confidence: 99%

“…ALB is an accurate, cost-effective, and rapid technique for shallow water measurements [11][12][13] and can also be used for non-bathymetric purposes, such as the measurement of seawater turbidity [14]. Although water turbidity depends on SSCs and particle composition, many experiments have shown a good linear relationship between water turbidity and SSCs [12].…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Zhang

et al. 2018

Remote Sensing

Self Cite

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

Cited by 35 publications

References 32 publications

Investigation of TLS Intensity Data and Distance Measurement Errors from Target Specular Reflections

Investigation of TLS Intensity Data and Distance Measurement Errors from Target Specular Reflections

On the Feasibility of Water Surface Mapping with Single Photon LiDAR

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

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