Performance Assessment of High Resolution Airborne Full Waveform LiDAR for Shallow River Bathymetry
نویسندگان
چکیده
We evaluate the performance of full waveform LiDAR decomposition algorithms with a high-resolution single band airborne LiDAR bathymetry system in shallow rivers. A continuous wavelet transformation (CWT) is proposed and applied in two fluvial environments, and the results are compared to existing echo retrieval methods. LiDAR water depths are also compared to independent field measurements. In both clear and turbid water, the CWT algorithm outperforms the other methods if only green LiDAR observations are available. However, both the definition of the water surface, and the turbidity of the water significantly influence the performance of the LiDAR bathymetry observations. The results suggest that there is no single best full waveform processing algorithm for all bathymetric situations. Overall, the optimal processing strategies resulted in a determination of water depths with a 6 cm mean at 14 cm standard deviation for clear water, and a 16 cm mean and 27 cm standard deviation in more turbid water. OPEN ACCESS Remote Sens. 2015, 7 5134
منابع مشابه
Remote Sensing of Suspended Sediment Concentrations Based on the Waveform Decomposition of Airborne LiDAR Bathymetry
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 acc...
متن کاملLe développement de LiDAR satellitaire multifonctions. Analyse exploratoire du potentiel de capteurs LiDAR pour le suivi altimétrique et bathymétrique des surfaces en eau continentales et côtières
Possessing accurate, spatial and current data on the water levels and the depths are necessary for anticipation and better management of coastal and continental waters. Among the remote sensing techniques to monitor the water bathymetry and altimetry, the LIDAR appears as an adapted and promising technique, already proven on airborne platforms, because of its potential accuracy, spatial resolut...
متن کاملA State of Art on Airborne Lidar Application in Hydrology and Oceanography: a Comprehensive Overview
Nowadays, lidar has been accepted as one of the important sensors providing accurate and dense 3D point cloud from earth surface terrain and water bathymetry. The basic idea of using lidar stems from the problem of measuring water depth without direct contacting with the water body or without any instrument mounted on the water surface in shallow regions. Bathymetric lidar that uses two differe...
متن کاملAn Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms
In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function...
متن کاملRemote Sensing of Channels and Riparian Zones with a Narrow-Beam Aquatic-Terrestrial LIDAR
The high-resolution Experimental Advanced Airborne Research LIDAR (EAARL) is a new technology for cross-environment surveys of channels and floodplains. EAARL measurements of basic channel geometry, such as wetted cross-sectional area, are within a few percent of those from control field surveys. The largest channel mapping errors are along stream banks. The LIDAR data adequately support 1D and...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Remote Sensing
دوره 7 شماره
صفحات -
تاریخ انتشار 2015