Depth Measurement Bias in Pulsed Airborne Laser Hydrography Induced by Chromatic Dispersion

Schwarz, Roland; Pfeifer, Norbert; Pfennigbauer, Martin; Mandlburger, Gottfried

doi:10.1109/lgrs.2020.3003088

Cited by 9 publications

(8 citation statements)

References 24 publications

(36 reference statements)

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“…Recent studies have found that the fixed refractive index of 1.33 is inappropriate [ 20 ]. Using a group velocity instead of the fixed refractive index can reduce the range-dependent bias of the water depth [ 38 ]. Therefore, the calculation of the in-water path has a significant effect on water bottom, especially in deep waters, which need to be carefully corrected.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection

Wang

Xing

et al. 2022

Sensors

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Airborne LiDAR bathymetry (ALB) has proven to be an effective technology for shallow water mapping. To collect data with a high point density, a lightweight dual-wavelength LiDAR system mounted on unmanned aerial vehicles (UAVs) was developed. This study presents and evaluates the system using the field data acquired from a flight test in Dazhou Island, China. In the precision and accuracy assessment, the local fitted planes extracted from the water surface points and the multibeam echosounder data are used as a reference for water surface and bottom measurements, respectively. For the bathymetric performance comparison, the study area is also measured with an ALB system installed on the manned aerial platform. The object detection capability of the system is examined with placed small cubes. Results show that the fitting precision of the water surface is 0.1227 m, and the absolute accuracy of the water bottom is 0.1268 m, both of which reach a decimeter level. Compared to the manned ALB system, the UAV-borne system provides higher resolution data with an average point density of 42 points/m2 and maximum detectable depth of 1.7–1.9 Secchi depths. In the point cloud of the water bottom, the existence of a 1-m target cube and the rough shape of a 2-m target cube are clearly observed at a depth of 12 m. The system shows great potential for flexible shallow water mapping and underwater object detection with promising results.

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Section: Discussionmentioning

confidence: 99%

Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection

Wang

Xing

et al. 2022

Sensors

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“…(Höhle 1971). Conventional refraction correction methods assume a constant value of n 2 = 1.33 for direction and range calculation (Schwarz et al 2020).…”

Section: Related Workmentioning

confidence: 99%

“…The numerical simulation is based on the separate observation of the beam path of each individual photon in the laser pulse and applies to the specific parameters selected as input variables. Furthermore, Schwarz et al (2020) present an approach in which the propagation time extension due to scattering effects is considered by using the group velocity instead of the phase velocity for range calculation. The slower group velocity represents the propagation speed of a laser pulse in a dispersive medium and was determined experimentally.…”

Section: Related Workmentioning

confidence: 99%

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Refined Geometric Modeling of Laser Pulse Propagation in Airborne LiDAR Bathymetry

Richter

Mader

Westfeld

et al. 2021

PFG

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To achieve a geometrically accurate representation of the water bottom, airborne LiDAR bathymetry (ALB) requires the correction of the raw 3D point coordinates due to refraction at the air–water interface, different signal velocity in air and water, and further propagation induced effects. The processing of bathymetric LiDAR data is based on a geometric model of the laser bathymetry pulse propagation describing the complex interactions of laser radiation with the water medium and the water bottom. The model comprises the geometric description of laser ray, water surface, refraction, scattering in the water column, and diffuse bottom reflection. Conventional geometric modeling approaches introduce certain simplifications concerning the water surface, the laser ray, and the bottom reflection. Usually, the local curvature of the water surface and the beam divergence are neglected and the travel path of the outgoing and the returned pulse is assumed to be identical. The deviations between the applied geometric model and the actual laser beam path cause a coordinate offset at the water bottom, which affects the accuracy potential of the measuring method. The paper presents enhanced approaches to geometric modeling which are based on a more accurate representation of water surface geometry and laser ray geometry and take into account the diffuse reflection at the water bottom. The refined geometric modeling results in an improved coordinate accuracy at the water bottom. The impact of the geometric modeling methods on the accuracy of the water bottom points is analyzed in a controlled manner using a laser bathymetry simulator. The findings will contribute to increase the accuracy potential of modern ALB systems.

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“…However, there are no studies systematically investigating the bias we introduce due to temperature measurements in a geothermal setting. Studies for the measurement bias are common in the field of remote sensing (i.e., Feng et al, 2016;Schwarz et al, 2020); however, their focus is entirely different. In remote sensing, the location of the measurements is subjected to uncertainties.…”

Section: Introductionmentioning

confidence: 99%

How biased are our models? – a case study of the alpine region

et al. 2021

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Abstract. Geophysical process simulations play a crucial role in the understanding of the subsurface. This understanding is required to provide, for instance, clean energy sources such as geothermal energy. However, the calibration and validation of the physical models heavily rely on state measurements such as temperature. In this work, we demonstrate that focusing analyses purely on measurements introduces a high bias. This is illustrated through global sensitivity studies. The extensive exploration of the parameter space becomes feasible through the construction of suitable surrogate models via the reduced basis method, where the bias is found to result from very unequal data distribution. We propose schemes to compensate for parts of this bias. However, the bias cannot be entirely compensated. Therefore, we demonstrate the consequences of this bias with the example of a model calibration.

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Depth Measurement Bias in Pulsed Airborne Laser Hydrography Induced by Chromatic Dispersion

Cited by 9 publications

References 24 publications

Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection

Evaluation of a New Lightweight UAV-Borne Topo-Bathymetric LiDAR for Shallow Water Bathymetry and Object Detection

Refined Geometric Modeling of Laser Pulse Propagation in Airborne LiDAR Bathymetry

How biased are our models? – a case study of the alpine region

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