Shoreline Extraction Based on LiDAR Data Obtained Using an USV

¹ Marine Technology Ltd., Gdynia, Poland
² University of Porto (FEUP), Porto, Portugal
³ Gdynia Maritime University, Gdynia, Poland
⁴ Gdańsk University of Technology, Gdańsk, Poland

DOI: 10.12716/1001.17.02.22

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ABSTRACT: 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.

KEYWORDS: Geodesy, Hydrography, Laser Scanning, Unmanned Surface Vessel (USV), Unmanned Vehicles (UVs), Shoreline, Shoreline extraction, LiDAR

REFERENCES

Li, Z.; Zhai, J.; Wu, F. Shape Similarity Assessment Method for Coastline Generalization. ISPRS Int. J. Geo-Inf. 2018, 7, 283. - doi:10.3390/ijgi7070283

Sui, L.; Wang, J.; Yang, X.; Wang, Z. Spatial-temporal Characteristics of Coastline Changes in Indonesia from 1990 to 2018. Sustainability 2020, 12, 3242. - doi:10.3390/su12083242

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. - doi:10.3390/rs12010008

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. - doi:10.3390/ijgi8110489

Zhang, Y.; Hou, X. Characteristics of Coastline Changes on Southeast Asia Islands from 2000 to 2015. Remote Sens. 2020, 12, 519. - doi:10.3390/rs12030519

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. - doi:10.3390/jmse7040097

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. - doi:10.3390/su9060900

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. - doi:10.3390/jmse6030080

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. - doi:10.1016/j.geomorph.2011.07.027

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. - doi:10.1016/j.jseaes.2013.01.002

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. - doi:10.3390/rs3071516

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. - doi:10.1016/j.oceaneng.2011.05.006

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. - doi:10.3390/jmse8060464

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. - doi:10.3390/rs6109930

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. - doi:10.1088/1755-1315/95/3/032011

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. - doi:10.3390/app9183867

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. - doi:10.1016/j.geomorph.2006.10.019

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. - doi:10.3390/rs12040737

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. - doi:10.3390/rs11182186

Boak, E.H.; Turner, I.L. Shoreline Definition and Detection: A Review. J. Coast. Res. 2005, 214, 688–703. - doi:10.2112/03-0071.1

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.

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). - doi:10.3133/ofr20181121

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 Tide-coordinated Shorelines from Coastal Elevation Models. J. Coast. Res. 2012, 28, 683–699. - doi:10.2112/JCOASTRES-D-11-00107.1

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. - doi:10.1088/1742-6596/2006/1/012048

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. - doi:10.4236/jgis.2011.32007

Xu, S.; Ye, N.; Xu, S. A New Method for Shoreline Extraction from Airborne LiDAR Point Clouds. Remote Sens. Lett. 2019, 10, 496–505. - doi:10.1080/2150704X.2019.1569277

Rusu, R.B. Semantic 3d Object Maps for Everyday Manipulation in Human Living Environments. KI - Künstliche Intelligenz 2010, 24, 345–348. - doi:10.1007/s13218-010-0059-6

Rusu, R.B. Semantic 3d Object Maps for Everyday Manipulation in Human Living Environments. PhD Thesis, Technische Universität München, München, Germany, 2009. - doi:10.1007/s13218-010-0059-6

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. - doi:10.1007/s12524-018-0788-9

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. - doi:10.1016/j.rse.2013.07.004

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. - doi:10.3390/rs14164075

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.

Lee, I.-C.; Cheng, L.; Li, R. Optimal Parameter Determination 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.

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. - doi:10.2112/05-0580.1

Niedermeier, A.; Romaneeßen, E.; Lehner, S. Detection of Coastlines in SAR Images Using Wavelet Methods. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2270–2281. - doi:10.1109/36.868884

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. - doi:10.1117/12.2015801

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. - doi:10.1117/1.OE.53.1.011006

Trucco, E.; Verri, A. Introductory Techniques for 3-D Computer Vision; Prentice Hall: Hoboken, NJ, USA, 1998.

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.

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. - doi:10.3390/en14175370

IHO. IHO Standards for Hydrographic Surveys, 6th ed.; Special Publication No. 44; IHO: Monaco, Monaco, 2020.

Citation note:

Halicki A., Specht M., Stateczny A., Specht C., Specht O.: Shoreline Extraction Based on LiDAR Data Obtained Using an USV. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 17, No. 2, doi:10.12716/1001.17.02.22, pp. 445-453, 2023

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