Range Difference Between Shallow and Deep Channels of Airborne Bathymetry LiDAR With Segmented Field-of-View Receivers

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 16
• Main Authors: Li, Jizhe, Tao, Bangyi, He, Yan, Li, Youzhi, Huang, Haiqing, Mao, Zhihua, Yu, Jiayong
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive optics; Airborne LiDAR bathymetry (ALB); Bathymeters; Bathymetry; Bias; Channels; Coefficients; correction method; Depth; Divergence; Field of view; Laser beams; Laser radar; Lasers; Lidar; Monte Carlo simulation; Oceanography; Optical properties; Optical pulses; Optical receivers; Optical scattering; Polynomials; range difference; Receivers; Scattering coefficient; segmented field-of-view (FOV); semianalytical Monte Carlo method (SALMOM); Sensitivity analysis; Statistical methods; Water depth
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3172351

Significant range differences were identified between shallow and deep channels of the Mapper5000 bathymetry light detection and ranging (LiDAR) system with segmented field-of-view (FOV) receivers. Range difference varied with depth and water optical properties. The main feature was the maximum value in range difference curves, which ranged from 0.3 to 0.6 m and usually exceeded the International Hydrographic Organization (IHO) accuracy standards. Sensitivity analyses based on a semianalytical Monte Carlo simulation model revealed that the scattering phase function and laser beam divergence angle played more important roles in causing pulse dispersion and determining the amplitude and position of maximum range difference than absorption and scattering coefficients. A range difference correction method by fitting existing shallow and deep channel data in the overlapping range with a cubic polynomial was proposed to correct the deep channel data in the entire depth range that LiDAR can detect. Depth discontinuity at the junction of the shallow channel and deep channel measurements was successfully removed, and the mean and standard deviation of corrected range differences were within 0.01 and 0.1 m, respectively. A combination of range difference correction and mean bias corrector can be an alternative method for depth bias correction of segmented-FOV LiDAR when referenced sonar data is not available.