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Open AccessArticle

Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems

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School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China
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Automation Department, School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
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The Survey Bureau of Hydrology and Water Resources of Yangtze Estuary, Shanghai 200136, China
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Author to whom correspondence should be addressed.
Academic Editors: Weimin Huang and Xiaofeng Li
Remote Sens. 2017, 9(7), 710; https://doi.org/10.3390/rs9070710
Received: 24 May 2017 / Revised: 5 July 2017 / Accepted: 6 July 2017 / Published: 10 July 2017
(This article belongs to the Section Ocean Remote Sensing)
Airborne LiDAR bathymetry (ALB) is efficient and cost effective in obtaining shallow water topography, but often produces a low-accuracy sounding solution due to the effects of ALB measurements and ocean hydrological parameters. In bathymetry estimates, peak shifting of the green bottom return caused by pulse stretching induces depth bias, which is the largest error source in ALB depth measurements. The traditional depth bias model is often applied to reduce the depth bias, but it is insufficient when used with various ALB system parameters and ocean environments. Therefore, an accurate model that considers all of the influencing factors must be established. In this study, an improved depth bias model is developed through stepwise regression in consideration of the water depth, laser beam scanning angle, sensor height, and suspended sediment concentration. The proposed improved model and a traditional one are used in an experiment. The results show that the systematic deviation of depth bias corrected by the traditional and improved models is reduced significantly. Standard deviations of 0.086 and 0.055 m are obtained with the traditional and improved models, respectively. The accuracy of the ALB-derived depth corrected by the improved model is better than that corrected by the traditional model. View Full-Text
Keywords: airborne LiDAR bathymetry; depth bias correction; improved depth bias model; measurement and hydrological parameters airborne LiDAR bathymetry; depth bias correction; improved depth bias model; measurement and hydrological parameters
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MDPI and ACS Style

Zhao, J.; Zhao, X.; Zhang, H.; Zhou, F. Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems. Remote Sens. 2017, 9, 710.

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