Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-43-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-43-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
03 Jan 2017
Research article |
| 03 Jan 2017
Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment
Mikkel Skovgaard Andersen
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Áron Gergely
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
Zyad Al-Hamdani
Geological Survey of Denmark and Greenland, Copenhagen, Denmark
Frank Steinbacher
Airborne Hydro Mapping GmbH, Innsbruck, Austria
Laurids Rolighed Larsen
NIRAS, Allerød, Denmark
Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
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Cited
21 citations as recorded by crossref.
- Emerging trends in topobathymetric LiDAR technology and mapping N. Pricope & M. Bashit 10.1080/01431161.2023.2287564
- Tidal Creek Extraction from Airborne LiDAR Data Using Ground Filtering Techniques H. Kim et al. 10.1007/s12205-020-2336-8
- Assessing Storm Response of Multiple Intertidal Bars Using an Open-Source Automatic Processing Toolbox A. Montreuil et al. 10.3390/rs14041005
- A method for estimating sediment budgets of washover deposits using digital terrain models L. Hansen et al. 10.1002/esp.5066
- Evaluation of Boulder Characteristics for Improved Boulder Detection Based on Machine Learning Techniques S. Hansen et al. 10.3390/geosciences12110421
- Mapping Topobathymetry in a Shallow Tidal Environment Using Low-Cost Technology S. Genchi et al. 10.3390/rs12091394
- Ecohydraulic Modelling to Support Fish Habitat Restoration Measures A. Adeva-Bustos et al. 10.3390/su11051500
- Extracting Shallow-Water Bathymetry from Lidar Point Clouds Using Pulse Attribute Data: Merging Density-Based and Machine Learning Approaches K. Lowell & B. Calder 10.1080/01490419.2021.1925790
- Assessing Marginal Shallow-Water Bathymetric Information Content of Lidar Sounding Attribute Data and Derived Seafloor Geomorphometry K. Lowell & B. Calder 10.3390/rs13091604
- Characterizing beach intertidal bar systems using multi‐annual LiDAR data A. Miles et al. 10.1002/esp.4594
- The application of data innovations to geomorphological impact analyses in coastal areas: An East Anglia, UK, case study A. Rumson et al. 10.1016/j.ocecoaman.2019.104875
- The importance of the number of points, transect location and interpolation techniques in the analysis of bathymetric measurements D. Diaconu et al. 10.1016/j.jhydrol.2018.12.070
- Classifying airborne bathymetry data using the Random Forest algorithm T. Kogut & M. Weistock 10.1080/2150704X.2019.1629710
- The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements A. Szafarczyk & C. Toś 10.3390/s23010292
- Analyzing inundation extent in small reservoirs: A combined use of topography, bathymetry and a 3D dam model W. Chen et al. 10.1016/j.measurement.2018.01.042
- A multi-scale geomorphometric approach to semi-automated classification of seabed morphology of a dynamic and complex marine meander bend L. Hansen et al. 10.1016/j.geomorph.2022.108371
- ACCURACY VERIFICATION OF AIRBORNE GREEN LASER BATHYMETRY IN THE SURF ZONE S. NAKAMURA 10.2208/kaigan.74.I_1447
- Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: Comparison of radar altimetry, LIDAR and photogrammetry techniques F. Bandini et al. 10.1016/j.rse.2019.111487
- Methodology for Creating a Digital Bathymetric Model Using Neural Networks for Combined Hydroacoustic and Photogrammetric Data in Shallow Water Areas M. Łącka & J. Łubczonek 10.3390/s24010175
- Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data S. Hansen et al. 10.3390/rs13204101
- Automatic classification and mapping of the seabed using airborne LiDAR bathymetry L. Janowski et al. 10.1016/j.enggeo.2022.106615
21 citations as recorded by crossref.
- Emerging trends in topobathymetric LiDAR technology and mapping N. Pricope & M. Bashit 10.1080/01431161.2023.2287564
- Tidal Creek Extraction from Airborne LiDAR Data Using Ground Filtering Techniques H. Kim et al. 10.1007/s12205-020-2336-8
- Assessing Storm Response of Multiple Intertidal Bars Using an Open-Source Automatic Processing Toolbox A. Montreuil et al. 10.3390/rs14041005
- A method for estimating sediment budgets of washover deposits using digital terrain models L. Hansen et al. 10.1002/esp.5066
- Evaluation of Boulder Characteristics for Improved Boulder Detection Based on Machine Learning Techniques S. Hansen et al. 10.3390/geosciences12110421
- Mapping Topobathymetry in a Shallow Tidal Environment Using Low-Cost Technology S. Genchi et al. 10.3390/rs12091394
- Ecohydraulic Modelling to Support Fish Habitat Restoration Measures A. Adeva-Bustos et al. 10.3390/su11051500
- Extracting Shallow-Water Bathymetry from Lidar Point Clouds Using Pulse Attribute Data: Merging Density-Based and Machine Learning Approaches K. Lowell & B. Calder 10.1080/01490419.2021.1925790
- Assessing Marginal Shallow-Water Bathymetric Information Content of Lidar Sounding Attribute Data and Derived Seafloor Geomorphometry K. Lowell & B. Calder 10.3390/rs13091604
- Characterizing beach intertidal bar systems using multi‐annual LiDAR data A. Miles et al. 10.1002/esp.4594
- The application of data innovations to geomorphological impact analyses in coastal areas: An East Anglia, UK, case study A. Rumson et al. 10.1016/j.ocecoaman.2019.104875
- The importance of the number of points, transect location and interpolation techniques in the analysis of bathymetric measurements D. Diaconu et al. 10.1016/j.jhydrol.2018.12.070
- Classifying airborne bathymetry data using the Random Forest algorithm T. Kogut & M. Weistock 10.1080/2150704X.2019.1629710
- The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements A. Szafarczyk & C. Toś 10.3390/s23010292
- Analyzing inundation extent in small reservoirs: A combined use of topography, bathymetry and a 3D dam model W. Chen et al. 10.1016/j.measurement.2018.01.042
- A multi-scale geomorphometric approach to semi-automated classification of seabed morphology of a dynamic and complex marine meander bend L. Hansen et al. 10.1016/j.geomorph.2022.108371
- ACCURACY VERIFICATION OF AIRBORNE GREEN LASER BATHYMETRY IN THE SURF ZONE S. NAKAMURA 10.2208/kaigan.74.I_1447
- Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: Comparison of radar altimetry, LIDAR and photogrammetry techniques F. Bandini et al. 10.1016/j.rse.2019.111487
- Methodology for Creating a Digital Bathymetric Model Using Neural Networks for Combined Hydroacoustic and Photogrammetric Data in Shallow Water Areas M. Łącka & J. Łubczonek 10.3390/s24010175
- Classification of Boulders in Coastal Environments Using Random Forest Machine Learning on Topo-Bathymetric LiDAR Data S. Hansen et al. 10.3390/rs13204101
- Automatic classification and mapping of the seabed using airborne LiDAR bathymetry L. Janowski et al. 10.1016/j.enggeo.2022.106615
Saved (preprint)
Latest update: 18 Apr 2024
Short summary
The transition zone between land and water is difficult to map due to shallow water depth and often challenging environmental conditions. Airborne topobathymetric lidar is capable of providing both topographic and bathymetric elevation information, resulting in a seamless coverage of the land–water transition zone. We present the processing and performance of topobathymetric lidar data along with a geomorphometric and morphological classification of landforms in a high-energy tidal environment.
The transition zone between land and water is difficult to map due to shallow water depth and...
