Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry
Suspended sediment concentrations (SSCs) have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB). However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-04-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/5/681 |
| _version_ | 1819294143889801216 |
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| author | Xinglei Zhao Jianhu Zhao Hongmei Zhang Fengnian Zhou |
| author_facet | Xinglei Zhao Jianhu Zhao Hongmei Zhang Fengnian Zhou |
| author_sort | Xinglei Zhao |
| collection | DOAJ |
| description | Suspended sediment concentrations (SSCs) have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB). However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remote sensing of SSCs in calm waters by using the range biases of green surface points of ALB. The near water surface penetrations (NWSPs) of green lasers are calculated on the basis of the green and reference surface points. The range biases (ΔS) are calculated by using the corresponding NWSPs and beam-scanning angles. In situ measured SSCs (C) and range biases (ΔS) are used to establish an empirical C-ΔS model at SSC sampling stations. The SSCs in calm waters are retrieved by using the established C-ΔS model. The proposed method is applied to a practical ALB measurement performed by Optech Coastal Zone Mapping and Imaging LiDAR. The standard deviations of the SSCs retrieved by the 3D point-cloud method are less than 20 mg/L. |
| first_indexed | 2024-12-24T04:21:38Z |
| format | Article |
| id | doaj.art-19b07d9136eb470db62b76c665bac49c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T04:21:38Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-19b07d9136eb470db62b76c665bac49c2022-12-21T17:15:46ZengMDPI AGRemote Sensing2072-42922018-04-0110568110.3390/rs10050681rs10050681Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR BathymetryXinglei Zhao0Jianhu Zhao1Hongmei Zhang2Fengnian Zhou3School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaAutomation Department, School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, ChinaThe Survey Bureau of Hydrology and Water Resources of Yangtze Estuary, Shanghai 200136, ChinaSuspended sediment concentrations (SSCs) have been retrieved accurately and effectively through waveform methods by using green-pulse waveforms of airborne LiDAR bathymetry (ALB). However, the waveform data are commonly difficult to analyze. Thus, this paper proposes a 3D point-cloud method for remote sensing of SSCs in calm waters by using the range biases of green surface points of ALB. The near water surface penetrations (NWSPs) of green lasers are calculated on the basis of the green and reference surface points. The range biases (ΔS) are calculated by using the corresponding NWSPs and beam-scanning angles. In situ measured SSCs (C) and range biases (ΔS) are used to establish an empirical C-ΔS model at SSC sampling stations. The SSCs in calm waters are retrieved by using the established C-ΔS model. The proposed method is applied to a practical ALB measurement performed by Optech Coastal Zone Mapping and Imaging LiDAR. The standard deviations of the SSCs retrieved by the 3D point-cloud method are less than 20 mg/L.http://www.mdpi.com/2072-4292/10/5/681airborne LiDAR bathymetryrange bias of green surface pointnear water surface penetrationsuspended sediment concentration |
| spellingShingle | Xinglei Zhao Jianhu Zhao Hongmei Zhang Fengnian Zhou Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry Remote Sensing airborne LiDAR bathymetry range bias of green surface point near water surface penetration suspended sediment concentration |
| title | Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry |
| title_full | Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry |
| title_fullStr | Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry |
| title_full_unstemmed | Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry |
| title_short | Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry |
| title_sort | remote sensing of sub surface suspended sediment concentration by using the range bias of green surface point of airborne lidar bathymetry |
| topic | airborne LiDAR bathymetry range bias of green surface point near water surface penetration suspended sediment concentration |
| url | http://www.mdpi.com/2072-4292/10/5/681 |
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