Refraction Correction of Airborne LiDAR Bathymetry Based on Sea Surface Profile and Ray Tracing

Yang, Fanlin; Su, Dianpeng; Ma, Yi; Feng, Chengkai; Yang, Anxiu; Wang, Mingwei

doi:10.1109/tgrs.2017.2721442

Cited by 48 publications

(26 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically speaking, foams covered on the sea surface can significantly enhance surface scattering with the increase of wind speed, which has a great influence on the transmittance [9]. The tides and random fluctuations can increase the geometry uncertainty of the sea surface [10,20], which may greatly affect the transmission path of the laser to influence the transmittance and refraction angles. Therefore, there is abundant room for further progress in establishing a more unified and comprehensive model for more complete and complex environmental conditions by introducing other environmental factors.…”

Section: Discussionmentioning

confidence: 99%

“…From the results, we can see the dynamic range of refraction angle was relatively small. However, the planimetric effects and depth coordinate errors caused by the refraction angle were considerable to detect the underwater targets at tens or even hundreds of meters [20]. Therefore, it is necessary to quickly calculate the refraction angle under different wind conditions to correct the detection results.…”

Section: Statistics Of Refraction Anglesmentioning

confidence: 99%

See 1 more Smart Citation

A Fast Analysis Method for Blue-Green Laser Transmission through the Sea Surface

Dong

Xie

et al. 2020

Sensors

View full text Add to dashboard Cite

The fast estimation of blue-green laser transmission characteristics through the fluctuating sea surface, such as refraction angles and transmittance, is very important to correct operating parameters, detection depth and anti-detection warning in airborne Light Detection and Ranging (LiDAR) applications. However, the geometry of the sea surface is changed by complex environment factors, such as wind and wave, which significantly affect the rapid acquisition of the blue-green laser transmission characteristics. To address this problem, a fast analysis method is provided to rapidly compute the blue-green laser transmittance and refraction angles through the fluctuating sea surface driven by different wind directions and speeds. In the method, a three-dimensional wave model driven by the wind was built to describe the wave spatial distribution varying with time. Using the wave model, the propagation path of the scanning laser footprint was analyzed using the proposed meshing method, thus the transmittance and refraction angles of the optical path can be fast obtained by using parallel computing. The simulation results imply that the proposed method can reduce the time consumption by 70% compared with the traditional analytical method with sequential computing. This paper provides some statistical laws of refraction angles and transmittance through the fluctuating sea surface under different wind conditions, which may serve as a basic for fast computation of airborne LiDAR transmission characteristics in complex environments.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Statistics Of Refraction Anglesmentioning

confidence: 99%

A Fast Analysis Method for Blue-Green Laser Transmission through the Sea Surface

Dong

Xie

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Unfortunately, in situ data of water levels/volumes of lakes across the globe are very limited, especially for developing countries and some remote areas. Previous studies also reported that the lake water level and volume can be obtained by the bathymetric data derived from various sensors [6], e.g., ship-borne singlebeam or multibeam echo sounders, and airborne bathymetric lidars (Light detection and ranging) [7][8][9]. However, the high cost of those accurate bathymetric data limits the use of this approach at a global scale [10].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

Zhang

et al. 2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

With new Ice, Cloud, and land Elevation Satellite (ICESat)-2 lidar (Light detection and ranging) datasets and classical Landsat imagery, a method was proposed to monitor annual changes of lake water levels and volumes for 35 years dated back to 1980s. Based on the proposed method, the annual water levels and volumes of Lake Mead in the USA over 1984–2018 were obtained using only two-year measurements of the ICESat-2 altimetry datasets and all available Landsat observations from 1984 to 2018. During the study period, the estimated annual water levels of Lake Mead agreed well with the in situ measurements, i.e., the R2 and RMSE (Root-mean-square error) were 1.00 and 1.06 m, respectively, and the change rates of lake water levels calculated by our method and the in situ data were −1.36 km3/year and −1.29 km3/year, respectively. The annual water volumes of Lake Mead also agreed well with in situ measurements, i.e., the R2 and RMSE were 1.00 and 0.36 km3, respectively, and the change rates of lake water volumes calculated by our method and in situ data were −0.57 km3/year and −0.58 km3/year, respectively. We found that the ICESat-2 exhibits a great potential to accurately characterize the Earth’s surface topography and can capture signal photons reflected from underwater bottoms up to approximately 10 m in Lake Mead. Using the ICESat-2 datasets with a global coverage and our method, accurately monitoring changes of annual water levels/volumes of lakes—which have good water qualities and experienced significant water level changes—is no longer limited by the time span of the available satellite altimetry datasets, and is potentially achievable over a long-term period.

show abstract

“…However, sub-dm water level height accuracy is not only required for estimating global water level rise [14] and for bathymetric Light Detection And Ranging (LiDAR) as the precondition for proper refraction correction of the raw signal [10,15,16], but also, as mentioned above, for calibration and validation of multidimensional CFD models. Such models allow simulation of realistic and complex flow situations including, for example, a potential surface tilt for bended rivers.…”

Section: Introductionmentioning

confidence: 99%

On the Feasibility of Water Surface Mapping with Single Photon LiDAR

Mandlburger

Jutzi

2019

IJGI

View full text Add to dashboard Cite

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.

show abstract

Refraction Correction of Airborne LiDAR Bathymetry Based on Sea Surface Profile and Ray Tracing

Cited by 48 publications

References 31 publications

A Fast Analysis Method for Blue-Green Laser Transmission through the Sea Surface

A Fast Analysis Method for Blue-Green Laser Transmission through the Sea Surface

Monitoring Annual Changes of Lake Water Levels and Volumes over 1984–2018 Using Landsat Imagery and ICESat-2 Data

On the Feasibility of Water Surface Mapping with Single Photon LiDAR

Contact Info

Product

Resources

About