Shoreline extraction based on LiDAR data obtained using an USV

• Autorzy: Halicki A. , Specht M. , Stateczny A. , Specht C. , Lewicka O.

Identyfikatory

DOI

10.12716/1001.17.02.22

• Języki publikacji: EN

Abstrakty

EN

This article explores the use of Light Detection And Ranging (LiDAR) derived point clouds to extract the shoreline of the Lake Kłodno (Poland), based on their geometry properties. The data collection was performed using the Velodyne VLP-16 laser scanner, which was mounted on the HydroDron Unmanned Surface Vehicle (USV). A modified version of the shoreline extraction method proposed by Xu et al. was employed, comprising of the following steps: (1) classifying the point cloud using the Euclidean cluster extraction with a tolerance parameter of 1 m and min. cluster size of 10,000 points, (2) further filtration of boundary points by removing those with height above 1 m from the measured elevation of water surface, (3) manual determination of a curve consisting of 5 points located along the entire shoreline extraction region at a relatively constant distant from the coast, (4) removal of points that are further from the curve than the average distance, repeated twice. The method was tested on the scanned section of the lake shoreline for which Ground Control Points (GCP) were measured using a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver. Then, the results were compared to the ground truth data, obtaining an average position error of 2.12 m with a standard deviation of 1.11 m. The max error was 5.54 m, while the min. error was 0.41 m, all calculated on 281 extracted shoreline points. Despite the limitations of this parametric, semi-supervised approach, those preliminary results demonstrate the potential for accurate shoreline extraction based on LiDAR data obtained using an USV. Further testing and optimisation of this method for larger scale and better generalisation for different waterbodies are necessary to fully assess its effectiveness and feasibility. In this context, it is essential to develop computationally efficient methods for approximating shorelines that can accurately determine their course based on a set of points.

Słowa kluczowe

EN

shoreline; LiDAR; laser scanning; Unmanned Surface Vessel; unmanned vehicles; hydrography; geodesy

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2023
• Tom: Vol. 17 No. 2
• Strony: 445--453
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Halicki A.

• Marine Technology Ltd., Gdynia, Poland
• University of Porto (FEUP), Porto, Portugal

autor

Specht M.

• Marine Technology Ltd., Gdynia, Poland
• Gdynia Maritime University, Gdynia, Poland

autor

Stateczny A.

• Gdańsk University of Technology, Gdańsk, Poland

autor

Specht C.

• Gdynia Maritime University, Gdynia, Poland

autor

Lewicka O.

• Gdynia Maritime University, Gdynia, Poland

Bibliografia

• 1. Li, Z.; Zhai, J.; Wu, F. Shape Similarity Assessment Method for Coastline Generalization. ISPRS Int. J. Geo-Inf. 2018, 7, 283.
• 2. Sui, L.; Wang, J.; Yang, X.; Wang, Z. Spatial‐temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018. Sustainability 2020, 12, 3242.
• 3. Kanwal, S.; Ding, X.; Sajjad, M.; Abbas, S. Three Decades of Coastal Changes in Sindh, Pakistan (1989–2018): A Geospatial Assessment. Remote Sens. 2020, 12, 8.
• 4. Nikolakopoulos, K.; Kyriou, A.; Koukouvelas, I.; Zygouri, V.; Apostolopoulos, D. Combination of Aerial, Satellite, and UAV Photogrammetry for Mapping the Diachronic Coastline Evolution: The Case of Lefkada Island. ISPRS Int. J. Geo‐Inf. 2019, 8, 489.
• 5. Zhang, Y.; Hou, X. Characteristics of Coastline Changes on Southeast Asia Islands from 2000 to 2015. Remote Sens. 2020, 12, 519.
• 6. Mury, A.; Jeanson, M.; Collin, A.; James, D.; Etienne, S. High Resolution Shoreline and Shelly Ridge Monitoring over Stormy Winter Events: A Case Study in the Megatidal Bay of Mont‐Saint‐Michel (France). J. Mar. Sci. Eng. 2019, 7, 97.
• 7. Fu, Y.; Guo, Q.; Wu, X.; Fang, H.; Pan, Y. Analysis and Prediction of Changes in Coastline Morphology in the Bohai Sea, China, Using Remote Sensing. Sustainability 2017, 9, 900.
• 8. Mahamud, U.; Takewaka, S. Shoreline Change around a River Delta on the Cox’s Bazar Coast of Bangladesh. J. Mar. Sci. Eng. 2018, 6, 80.
• 9. Martínez, C.; Quezada, M.; Rubio, P. Historical Changes in the Shoreline and Littoral Processes on a Headland Bay Beach in Central Chile. Geomorphology 2011, 135, 80–96. 453
• 10. Chu, Z.X.; Yang, X.H.; Feng, X.L.; Fan, D.J.; Li, Y.K.; Shen, X.; Miao, A.Y. Temporal and Spatial Changes in Coastline Movement of the Yangtze Delta during 1974– 2010. J. Asian Earth Sci. 2013, 66, 166–174.
• 11. Cowart, L.; Corbett, D.R.; Walsh, J.P. Shoreline Change along Sheltered Coastlines: Insights from the Neuse River Estuary, NC, USA. Remote Sens. 2011, 3, 1516–1534.
• 12. Kuleli, T.; Guneroglu, A.; Karsli, F.; Dihkan, M. Automatic Detection of Shoreline Change on Coastal Ramsar Wetlands of Turkey. Ocean Eng. 2011, 38, 1141– 1149.
• 13. Specht, M.; Specht, C.; Lewicka, O.; Makar, A.; Burdziakowski, P.; Dąbrowski, P. Study on the Coastline Evolution in Sopot (2008–2018) Based on Landsat Satellite Imagery. J. Mar. Sci. Eng. 2020, 8, 464.
• 14. Zhang, X.; Pan, D.; Chen, J.; Zhao, J.; Zhu, Q.; Huang, H. Evaluation of Coastline Changes under Human Intervention Using Multi‐temporal High‐resolution Images: A Case Study of the Zhoushan Islands, China. Remote Sens. 2014, 6, 9930–9950.
• 15. Specht, C.; Weintrit, A.; Specht, M.; Dąbrowski, P. Determination of the Territorial Sea Baseline—Measurement Aspect. IOP Conf. Ser. Earth Environ. Sci. 2017, 95, 1–10.
• 16. Specht, M.; Specht, C.; Wąż, M.; Dąbrowski, P.; Skóra, M.; Marchel, Ł. Determining the Variability of the Territorial Sea Baseline on the Example of Waterbody Adjacent to the Municipal Beach in Gdynia. Appl. Sci. 2019, 9, 3867.
• 17. Basterretxea, G.; Orfila, A.; Jordi, A.; Fornós, J.; Tintoré, J. Evaluation of a Small Volume Renourishment Strategy on a Narrow Mediterranean Beach. Geomorphology 2007, 88, 139–151.
• 18. Specht, M.; Specht, C.; Mindykowski, J.; Dąbrowski, P.; Maśnicki, R.; Makar, A. Geospatial Modeling of the Tombolo Phenomenon in Sopot Using Integrated Geodetic and Hydrographic Measurement Methods. Remote Sens. 2020, 12, 737.
• 19. Viaña‐Borja, S.P.; Ortega‐Sánchez, M. Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas. Remote Sens. 2019, 11, 2186.
• 20. Boak, E.H.; Turner, I.L. Shoreline Definition and Detection: A Review. J. Coast. Res. 2005, 214, 688–703.
• 21. Stockdonf, H.F.; Sallenger Jr.; A.H.; List, J.H.; Holman, R.A. Estimation of Shoreline Position and Change Using Airborne Topographic Lidar Data. J. Coast. Res. 2002, 18, 502–513.
• 22. Farris, A.S.; Weber, K.M.; Doran, K.S.; List, J.H. Comparing Methods Used by the U.S. Geological Survey Coastal and Marine Geology Program for Deriving Shoreline Position from Lidar Data. Available online: https://pubs.usgs.gov/of/2018/1121/ofr20181121.pdf (accessed on 26 April 2023).
• 23. Fernández Luque, I.; Aguilar Torres, F.J.; Aguilar Torres, M.A.; Pérez García, J.L.; López Arenas, A. A New, Robust, and Accurate Method to Extract Tidecoordinated Shorelines from Coastal Elevation Models. J. Coast. Res. 2012, 28, 683–699.
• 24. Hua, L.W.; Bi, Y.L.; Hao, L. The Research of Artificial Shoreline Extraction Based on Airborne LIDAR Data. J. Phys.: Conf. Ser. 2021, 2006, 012048.
• 25. Liu, H.; Wang, L.; Sherman, D.J.; Wu, Q.; Su, H. Algorithmic Foundation and Software Tools for Extracting Shoreline Features from Remote Sensing Imagery and LiDAR Data. J. Geogr. Inf. Syst. 2011, 3, 99– 119.
• 26. Xu, S.; Ye, N.; Xu, S. A New Method for Shoreline Extraction from Airborne LiDAR Point Clouds. Remote Sens. Lett. 2019, 10, 496–505.
• 27. Rusu, R.B. Semantic 3d Object Maps for Everyday Manipulation in Human Living Environments. KI ‐Künstliche Intelligenz 2010, 24, 345–348.
• 28. Rusu, R.B. Semantic 3d Object Maps for Everyday Manipulation in Human Living Environments. PhD Thesis, Technische Universität München, München, Germany, 2009.
• 29. Xu, S.; Xu, S. A Minimum‐cost Path Model to the Bridge Extraction from Airborne LiDAR Point Clouds. J. Indian Soc. Remote Sens. 2018, 46, 1423–1431.
• 30. Smeeckaert, J.; Mallet, C.; David, N.; Chehata, N.; Ferraz, A. Large‐scale Classification of Water Areas Using Airborne Topographic LiDAR Data. Remote Sens. Environ. 2013, 138, 134–148.
• 31. Lewicka, O.; Specht, M.; Stateczny, A.; Specht, C.; Dardanelli, G.; Brčić, D.; Szostak, B.; Halicki, A.; Stateczny, M.; Widźgowski, S. Integration Data Model of the Bathymetric Monitoring System for Shallow Waterbodies Using UAV and USV Platforms. Remote Sens. 2022, 14, 4075.
• 32. Di, K.; Wang, J.; Ma, R.; Li, R. Automatic Shoreline Extraction from High‐resolution IKONOS Satellite Imagery. In Proceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference 2003 (ASPRS 2003), Anchorage, AK, USA, 5– 9 May 2003.
• 33. Lee, I.‐C.; Cheng, L.; Li, R. Optimal ParameterDetermination for Mean‐shift Segmentation‐based Shoreline Extraction Using Lidar Data, Aerial Orthophotos, and Satellite Imagery. In Proceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference 2010 (ASPRS 2010), San Diego, CA, USA, 26–30 April 2010.
• 34. Liu, H.; Sherman, D.; Gu, S. Automated Extraction of Shorelines from Airborne Light Detection and Ranging Data and Accuracy Assessment Based on Monte Carlo Simulation. J. Coast. Res. 2007, 236, 1359–1369.
• 35. Niedermeier, A.; Romaneeßen, E.; Lehner, S. Detection of Coastlines in SAR Images Using Wavelet Methods. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2270–2281.
• 36. Yousef, A.H.; Iftekharuddin, K.; Karim, M. A New Morphology Algorithm for Shoreline Extraction from DEM Data. In Proceedings of the SPIE Defense, Security, and Sensing 2013, Baltimore, MA, USA, 29–30 April 2013.
• 37. Yousef, A.H.; Iftekharuddin, K.M.; Karim, M.A. Shoreline Extraction from Light Detection and Ranging Digital Elevation Model Data and Aerial Images. Opt. Eng. 2013, 53, 011006.
• 38. Trucco, E.; Verri, A. Introductory Techniques for 3‐D Computer Vision; Prentice Hall: Hoboken, NJ, USA, 1998.
• 39. Lee, I.‐C.; Wu, B.; Li, R. Shoreline Extraction from the Integration of LiDAR Point Cloud Data and Aerial Orthophotos Using Mean Shift Segmentation. In Proceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference 2009 (ASPRS 2009), Baltimore, MD, USA, 9– 13 March 2009.
• 40. Specht, M.; Stateczny, A.; Specht, C.; Widźgowski, S.; Lewicka, O.; Wiśniewska, M. Concept of an Innovative Autonomous Unmanned System for Bathymetric Monitoring of Shallow Waterbodies (INNOBAT System). Energies 2021, 14, 5370.
• 41. IHO. IHO Standards for Hydrographic Surveys, 6th ed.; Special Publication No. 44; IHO: Monaco, Monaco, 2020.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).