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The process of modelling the elevation surface of a coastal area using the fusion of spatial data from different sensors

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Information regarding the depth distribution in a specific aquatic area is not also crucial for the safety of navigation, but also for modelling environmental processes, such as the quick establishment of marine-land boundaries or assessments of flood risk areas. Using elevation data from different available sources can be very convenient for individuals who wish to conduct quick analysis or need to obtain data covering a large area without the need for data collection and surveys. This study proposes a method of combining spatial data from different sources during surface modelling of a coastal area. The spatial data used for elevation surface modelling included hydrographic and topographic data, which are often collected separately for various purposes. Data are saved in different formats with various resolutions and accuracies; thus, a uniform surface model that will allow for easy and accurate analysis is currently lacking. The main aim of this study was to create a model of the surface of a coastal area using input data from various sources with the highest possible accuracy. This paper presents the available spatial data sources for coastal areas, along with the data pre-processing process. Furthermore, spatial data fusion is introduced, along with the results. The entire process of creating the uniform surface model consisted of several steps that are described in detail and visualised. The obtained model was visualised using a three-dimensional map.
Czasopismo
Rocznik
Strony
22--34
Opis fizyczny
Bibliogr. 26 poz., fot., rys., tab., wykr.
Twórcy
  • Maritime University of Szczecin, Szczecin, Poland
  • Maritime University of Szczecin, Szczecin, Poland
  • Maritime University of Szczecin, Szczecin, Poland
Bibliografia
  • 1. Alberoni, A.A.L., Jeck, I.K., Silva, C.G., Torres, L.C., 2019. The new Digital Terrain Model (DTM) of the Brazilian Continen-tal Margin: detailed morphology and revised undersea feature names. Geo-Mar. Lett. 40 (6), 1-16. https://doi.org/10.1007/s00367-019-00606- x
  • 2. Amante, C., Eakins, B.W., 2009. ETOPO1 Global Relief Model converted to PanMap layer format. NOAA-National Geophysical Data Center. PANGAEA. https://doi.org/10.1594/PANGAEA.769615
  • 3. Danielson, J.J., Poppenga, S.K., Brock, J.C., Evans, G.A.,Tyler, D.J., Gesch, D.B., Thatcher, C.A., Barras, J.A., 2016. Topobathymetric elevation model development using a new methodology: Coastal national elevation database. J. Coastal Res. 76 (Sp. Iss. 1), 75-89. https://doi.org/10.2112/SI76-008
  • 4. Duan, X., Li, L., Zhu, H., Ying, S., 2017. A high-fidelity multiresolution digital elevation model for Earth systems. Geosci. Model Devel. 10, 239-253. https://doi.org/10.5194/gmd-10-239-2017
  • 5. Fezzani, R., Zerr, B., Mansour, A., Legris, M., Vrignaud, C., 2019. Fusion of Swath Bathymetric Data: Application to AUV Rapid Environment Assessment. IEEE J. Oceanic Eng. 44 (1), 111-120. https://doi.org/10.1109/JOE.2017.2773139
  • 6. Grall, P., Kochanska, I., Marszal, J., 2020. Direction-of-Arrival Estimation Methods in Interferometric Echo Sounding. Sensors Sp. Iss. 20 (12), 3556. https://doi.org/10.3390/s20123556 Gesch, D., Wilson, R., 2001. Development of a seamless multi-source topographic/bathymetric elevation model of Tampa Bay. Mar. Technol. Soc. J. 35 (4), 58-64. https://doi.org/10.4031/002533201788058062
  • 7. Global Mapper — Knowledge Base, Combine/Compare Terrain Layers, accessed on 13 July 2021. https://www.bluemarblegeo.com/knowledgebase/global- mapper- 19/Create_ElevationGrid_from_3D_Vector_Data.htm
  • 8. Global Mapper — Knowledge Base, Elevation Grid Creation, accessed on 13 July 2021. https://www.bluemarblegeo.com/knowledgebase/global- mapper-19/Create_Elevation_Grid_from_3D_Vector_Data.htm
  • 9. Holland, M.M., Becker, A., Smith, J.A., Everett, J.D., Suthers, I.M.,2021. Characterizing the three-dimensional distribution of schooling reef fish with a portable multibeam echosounder. Limnol. Oceanogr. Methods 19 (5), 340-355. https://doi.org/10.1002/lom3.10427
  • 10. IHO B-11, 2019. The IHO-IOC GEBCO Cook Book, IHO Publication B-11. https://www.star.nesdis.noaa.gov/socd/lsa/GEBCO_Cookbook/
  • 11. IHO C-13. 2013. Manual on Hydrography.
  • 12. IHO S-44. 2020. IHO S-44 Standards for Hydrographic Surveys. https://iho.int/en/standards- and- specifications
  • 13. Jakobsson, M., Mayer, L., Bringensparr, C., Castro, C., Mohammad, R., Johnson, P., Ketter, T., Accettella, D., Amblas, D., An, L., Arndt, J.E., Canals, M., Casamor, J.L., Chauché, N.,Coakley, B., Danielson, S., Demarte, M., Dickson, M.-L.,Dorschel, B., Zinglersen, K., 2020. The International Bathymetric Chart of the Arctic Ocean Version 4.0. Sci. Data 7 (1), 176. https://doi.org/10.1038/s41597-020-0520-9
  • 14. Janowski, Ł., Kubacka, M., Pydyn, A., Popek, M., Gajewski, Ł., 2021. From acoustics to underwater archaeology: Deep investigation of a shallow lake using high-resolution hydroacoustics—The case of Lake Lednica. Poland. Archaeometry 63 (5), 1059-1080. https://doi.org/10.1111/arcm.12663
  • 15. Legleiter, C.J., 2012. Remote measurement of river morphology via fusion of LiDAR topography and spectrally based bathymetry. Earth Surf. Proc. Land. 37 (5), 499-518. https://doi.org/10.1002/esp.2262
  • 16. Maleika, W., Forczma ́nski, P., 2020. Adaptive Modeling and Compression of Bathymetric Data With Variable Density. IEEE J. Oceanic Eng. 45 (4), 1353-1369. https://doi.org/10.1109/JOE.2019.2941120
  • 17. Maleika, W., 2020. Inverse distance weighting method optimization in the process of digital terrain model creation based on data collected from a multibeam echosounder. Appl. Geomat. 12, 397-407. https://doi.org/10.1007/s12518-020-00307-6
  • 18. Mandlburger, G., Pfennigbauer, M., Schwarz, R., Flöry, S., Nussbaumer, L., 2020. Concept and performance evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR sensor. Remote Sens. 12 (6), 986. https://doi.org/10.3390/rs12060986
  • 19. Maune, D.F., 2007. Digital Elevation Model Technologies and Applications: The DEM Users Manual. 2nd Edn., ASPRS Publ., 655 pp.
  • 20. Mills, M., 2020. Elevation Grid Creation in Global Mapper: Creating a DTM. Blue Marble Geographics. https://blog.bluemarblegeo.com/2020/06/23/elevation-grid-creation-in-global-mapper-creating-a-dtm/
  • 21. Quadros, N., Collier, P., Fraser, C., 2008. Integration of bathymetric and topographic Lidar: a preliminary investigation. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences Beijing XXXVII (B8), 1299-1304.
  • 22. Ramasamy, S.M., Saravanavel, J., Kathiresan, P., Kumanan, C.,Rajasekhar, D., 2020. Detection of submerged harbour using GEBCO and MBES data, in the offshore region of ancient port city Poompuhar, South India. Curr. Sci. 119, 526-534. https://doi.org/10.18520/cs/v119/i3/526-534
  • 23. Ruan, X., Cheng, L., Chu, S., Yan, Z., Zhou, X., Duan, Z., Li, M.,2020. A new digital bathymetric model of the South China Sea based on the subregional fusion of seven global seafloor topography products. Geomorphology 370, 107403. https://doi.org/10.1016/j.geomorph.2020.107403
  • 24. Shan, J., Toth, C.K. (Eds.), 2017. Topographic Laser Ranging and Scanning: Principles and Processing. CRC Press, 654 pp.
  • 25. Somoza, L., Medialdea, T., González, F.J., Machancoses, S., Candón, J.A., Cid, C., Calado, A., Afonso, A., Pinto Ribeiro, L.,Blasco, I., Albuquerque, M., Asensio-Ramos, M., Betten-court, R., De Ignacio, C., López-Pamo, E., Ramos, B., Rincón-Tomás, B., Santofimia, E., Souto, M., Madureira, P., 2021. High-resolution multibeam bathymetry of the northern Mid-Atlantic Ridge at 45-46° N: the Moytirra hydrothermal field. J. Maps 17 (2), 184-196. https://doi.org/10.1080/17445647.2021.1898485
  • 26. Starek, M.J., Giessel, J., 2017. Fusion of uas-based structure-from-motion and optical inversion for seamless topo-bathymetric mapping. School of Engineering and Computing Sciences and Conrad Blucher Institute 2999-3002. https://doi.org/10.1109/IGARSS.2017.8127629
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).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-665062d7-7bab-4720-bab9-672c11eb9dfa
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