Analyzing near water surface penetration in laser bathymetry – A case study at the River Pielach

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1. BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images G. Mandlburger et al. https://doi.org/10.1007/s41064-021-00142-3
2. A Review on Methods for Measurement of Free Water Surface G. Rak et al. https://doi.org/10.3390/s23041842
3. Analysis and correction of ocean wave pattern induced systematic coordinate errors in airborne LiDAR bathymetry P. Westfeld et al. https://doi.org/10.1016/j.isprsjprs.2017.04.008
4. Development of an Automated Riverbed Construction Using Airborne Bathymetric LiDAR Reflecting Depth-Dependent Characteristics in Large-Scale River Basins H. An & J. Lee https://doi.org/10.7780/kjrs.2025.41.4.3
5. DEVELOPMENT OF A METHOD FOR MAPPING THE BOTTOM OF SHALLOW RESERVOIRS BY LASER BATHYMETRY R. Eminov et al. https://doi.org/10.14489/td.2022.02.pp.050-055
6. Retrieval of Suspended Sediment Concentration from Bathymetric Bias of Airborne LiDAR X. Zhao et al. https://doi.org/10.3390/s222410005
7. Refraction Correction of Airborne LiDAR Bathymetry Based on Sea Surface Profile and Ray Tracing F. Yang et al. https://doi.org/10.1109/TGRS.2017.2721442
8. Inventory of Forest Attributes to Support the Integration of Non-provisioning Ecosystem Services and Biodiversity into Forest Planning—from Collecting Data to Providing Information T. Knoke et al. https://doi.org/10.1007/s40725-021-00138-7
9. Airborne lidar bathymetry: assessing quality assurance and quality control methods with Leica Chiroptera examples K. Saylam et al. https://doi.org/10.1080/01431161.2018.1430916
10. Identification and Assessment of Groundwater and Soil Contamination from an Informal Landfill Site X. Liu & Y. Wang https://doi.org/10.3390/su142416948
11. Gaussian Half-Wavelength Progressive Decomposition Method for Waveform Processing of Airborne Laser Bathymetry K. Guo et al. https://doi.org/10.3390/rs10010035
12. Laser bathymetry on rough riverbed channels: State‐of‐the‐art and future prospects T. Himmelsbach et al. https://doi.org/10.1002/esp.70319
13. Integrated Full-Waveform Analysis and Classification Approaches for Topo-Bathymetric Data Processing and Visualization in HydroVISH F. Steinbacher et al. https://doi.org/10.1007/s41064-021-00150-3
14. Strip Adjustment of Multi-Temporal LiDAR Data—A Case Study at the Pielach River M. Wimmer et al. https://doi.org/10.3390/rs16152838
15. NWSP: A Novel Indicator for Ocean–Land Interface Extraction Using Bathymetric LiDAR X. Zhao et al. https://doi.org/10.1109/JOE.2024.3436907
16. Algorithm for Detection of Water Surface Height in UAV-Borne Photon-Counting LiDAR Y. Li et al. https://doi.org/10.1109/LGRS.2023.3262136
17. A Simplified End-User Approach to Lidar Very Shallow Water Bathymetric Correction M. Okhrimenko & C. Hopkinson https://doi.org/10.1109/LGRS.2019.2915267
18. Topo-Bathymetric LiDAR for Monitoring River Morphodynamics and Instream Habitats—A Case Study at the Pielach River G. Mandlburger et al. https://doi.org/10.3390/rs70506160
19. Evaluation of Green-LiDAR Data for Mapping Extent, Density and Height of Aquatic Reed Beds at Lake Chiemsee, Bavaria—Germany N. Corti Meneses et al. https://doi.org/10.3390/rs9121308
20. Improvement of Full Waveform Airborne Laser Bathymetry Data Processing based on Waves of Neighborhood Points T. Kogut & K. Bakuła https://doi.org/10.3390/rs11101255
21. Underwater optical imaging and sensing in turbidity using three-dimensional integral imaging: a review A. Maric et al. https://doi.org/10.3788/AI.2025.20002
22. Improved Model for Depth Bias Correction in Airborne LiDAR Bathymetry Systems J. Zhao et al. https://doi.org/10.3390/rs9070710
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24. Stage Monitoring in Turbid Reservoirs with an Inclined Terrestrial Near-Infrared Lidar S. Tamari et al. https://doi.org/10.3390/rs8120999
25. Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser G. Liang et al. https://doi.org/10.3390/rs13183628
26. The Use of Green Laser in LiDAR Bathymetry: State of the Art and Recent Advancements A. Szafarczyk & C. Toś https://doi.org/10.3390/s23010292
27. Evaluating gradient descent variations for artificial neural network bathymetry modeling and sensitivity analysis C. Lee et al. https://doi.org/10.1117/1.JRS.18.022204
28. Seafloor Point Refinement for Airborne LiDAR Bathymetry Based on Neighborhood Adaptive Moving Least Squares Y. Guo et al. https://doi.org/10.1109/JSTARS.2025.3634744
29. Shallow Water Measurements Using a Single Green Laser Corrected by Building a Near Water Surface Penetration Model J. Zhao et al. https://doi.org/10.3390/rs9050426
30. Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry X. Zhao et al. https://doi.org/10.3390/rs10050681
31. Precise detection of water surface through the analysis of a single green waveform from bathymetry LiDAR B. Tao et al. https://doi.org/10.1364/OE.468404
32. Analysis of Turbidity Induced Water Surface Uncertainty in Airborne Photon-Counting LiDAR Bathymetry Y. Li et al. https://doi.org/10.1109/TGRS.2024.3381165
33. Design and evaluation of a full-wave surface and bottom-detection algorithm for LiDAR bathymetry of very shallow waters R. Schwarz et al. https://doi.org/10.1016/j.isprsjprs.2019.02.002
34. A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry S. Xing et al. https://doi.org/10.3390/s19235065
35. Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor G. Mandlburger et al. https://doi.org/10.3390/rs12060986
36. An Improved Water-Land Discriminator Using Laser Waveform Amplitudes and Point Cloud Elevations of Airborne LIDAR X. Zhao et al. https://doi.org/10.2112/JCOASTRES-D-20-00185.1
37. Background noise reduction for airborne bathymetric full waveforms by creating trend models using Optech CZMIL in the Yellow Sea of China X. Zhao et al. https://doi.org/10.1364/AO.402973
38. MVCNN: A Deep Learning-Based Ocean–Land Waveform Classification Network for Single-Wavelength LiDAR Bathymetry G. Liang et al. https://doi.org/10.1109/JSTARS.2022.3229062
39. 利用机载激光雷达归一化差异水指数的水体提取 赵. Zhao Xinglei et al. https://doi.org/10.3788/AOS241688
40. Quantifying Airborne Lidar Bathymetry Quality-Control Measures: A Case Study in Frio River, Texas K. Saylam et al. https://doi.org/10.3390/s18124153
41. Errors of Airborne Bathymetry LiDAR Detection Caused by Ocean Waves and Dimension-Based Laser Incidence Correction K. Guo et al. https://doi.org/10.3390/rs13091750
42. An Integrated Approach to Riverbed Morphodynamic Modeling Using Remote Sensing Data M. Bozzano et al. https://doi.org/10.3390/jmse12112055
43. Classification of Airborne Laser Bathymetry Data Using Artificial Neural Networks T. Kogut & A. Slowik https://doi.org/10.1109/JSTARS.2021.3050799
44. Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment M. Andersen et al. https://doi.org/10.5194/hess-21-43-2017
45. Refraction error correction of Airborne LiDAR Bathymetry data considering sea surface waves W. Xu et al. https://doi.org/10.1016/j.jag.2021.102402
46. Light detection and ranging of natural systems L. Irwin et al. https://doi.org/10.1038/s43586-025-00446-3