Research Article
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Determination of Black Sea Coastline Length with Oblique Stereographic Projection Using Affine Transformation

Year 2023, , 179 - 186, 26.12.2023
https://doi.org/10.30897/ijegeo.1399910

Abstract

The Black Sea basin has rich oil and natural gas resources. As a result, determining the continental shelf in international relations and maritime law is a critical issue for countries with a shoreline on the Black Sea, which is a semi-enclosed sea. Global projections are generally used for the projection of satellites used to image the earth. The use of global projections causes increased deformation in applications in local areas. For this reason, the deformation of satellite images used as a base can be reduced by converting them to the appropriate projection. In this study, the coastline lengths of the countries neighboring the Black Sea were calculated by using remote sensing images and selecting the appropriate cartographic projection due to the aim of minimum map deformation. By using different numbers of control points to determine the six parameters of the 2D Affine transformation used for coordinate transformation, the change in the accuracy of the transformation depending on the number of control points was examined. The Black Sea coastline, which was digitized in the local coordinate system via the MODIS satellite image, was transformed into coordinates in the oblique stereographic projection system with 2D affine transformation. 11 test points were used in the affine transformation parameters calculated using different control points, and root mean square error (RMSE) of approximately 6 km on the X axis and approximately 10 km on the Y axis was achieved using 25 control points. As a result of the transformation, the coastline lengths of each country bordering the Black Sea were determined in oblique stereographic projection.

References

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  • Uçar, D., İpbüker, C., Bildirici, İ. Ö. (2004). Matematiksel kartografya: harita projeksiyonları teorisi ve uygulamaları. Atlas Yayın Dağıtım.
  • Wang, Q., Liu, Y., Guo, Y., Wang, S., Zhang, Z., Cui, X., Zhang, H. (2022). A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry. Remote Sensing, 14(21), 5377.
  • Yılmaz, İ. (2009). The Basic Principals in Choosing Appropriate Map Projection. Electronic Journal of Map Technologies, 1 (2) , 31-42
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Year 2023, , 179 - 186, 26.12.2023
https://doi.org/10.30897/ijegeo.1399910

Abstract

References

  • Ackerman, D., Weisberg, S.B. (2003). Relationship between rainfall and beach bacterial concentrations on Santa Monica Bay Beaches. Journal of Water Health, 1(2), 85-90.
  • Aguilar, W. G., Salcedo, V. S., Sandoval, D. S., Cobeña, B. (2017). Developing of a video-based model for UAV autonomous navigation. In Computational Neuroscience: First Latin American Workshop, LAWCN 2017, Porto
  • Alegre, Brazil, November 22–24, 2017, Proceedings (pp. 94-105). Springer International Publishing.
  • Alcaras, E., Parente, C., Vallario, A. (2020). The importance of the coordinate transformation process in using heterogeneous data in coastal and marine geographic information system. Journal of Marine Science and Engineering, 8(9), 708.
  • Ansari, K., Corumluoglu, O., Verma, P. (2018). The triangulated affine transformation parameters and barycentric coordinates of Turkish permanent GPS network. Survey Review, 50(362), 412-415.
  • Cupples, A.M., Xagorarki, I., Rose, J. (2010). New molecular methods for detection of woterbone pathogens. In: Mitchell, R., Gu, J.D. (Eds.), Environmental Microbiology (pp. 150-211), New Jersey, NJ: Wiley-Blackwell.
  • Erhan, E. (1977). Kıta Sahanlığı Jeolojisi-Ekonomisi-Politikası. Jeoloji Mühendisliği Dergisi, 1(2), 4-12.
  • FAO (2008). Information on fisheries management in Bulgaria and Romania. Retrieved 26 June 2013 from http://www.fao.org/fi/fcp/en/ROM/body.htm
  • Gazioğlu, C., Gökaşan, E., Algan, O. Yücel, Z. Y., Tok, B., Doğan, E., (2002). Morphologic features of the Marmara Sea from multi-beam data, Mar. Geol., 190(1– 2): 397–420.
  • Gedik, A., Saltoğlu, T., Kaplan, H. (1979). Karadeniz'in güncel çökelleri ve uranyum içerikleri. Bulletin of the Mineral Research and Exploration, 92(92), 69-69.
  • Giannini, M. B., Maglione, P., Parente, C., Santamaria, R. (2011). Cartography and remote sensing for coastal erosion analysis. WIT Transactions on Ecology and the Environment, 149, 65-76.
  • Goksel, C., Bildirici, I. O., Ipbüker, C., Ulugtekin, N. (2001). A spatial analysis of aegean sea using remotely sensed imagery and GIS technology. In The 20th International Cartographic Conference, ICC (pp. 6-10).
  • Hagedorn, C., Blanch, A.R., Harwood, V.J. (2011). Microbial Source Tracking: Methods, Applications, and Case Studies. London: Springer.
  • Jenny, B., Hurni, L. (2011). Studying cartographic heritage: Analysis and visualization of geometric distortions. Computers & Graphics, 35(2), 402-411.
  • Laganà, A., Gavrilova, M. L., Kumar, V., Mun, Y., Tan, C. K., Gervasi, O. (Eds.). (2004). Computational Science and Its Applications-ICCSA 2004: International Conference, Assisi, Italy, May 14-17, 2004, Proceedings, Part III (Vol. 3045). Springer.
  • Mallet, C.; Michot, A.; de De La Torre, Y.; Lafon, V.; Robin, M.; Prevoteaux, B. (2023). Synthèse de référence des techniques de suivi du trait de côte. 2012. Available online: http://infoterre.brgm.fr/rapports/RP-60616-FR.pdf (accessed on 15 December 2023).
  • Reerink, T. J., Kliphuis, M. A., van de Wal, R. S. W. (2010). Mapping technique of climate fields between GCM’s and ice models, Geosci. Model Dev., 3, 13–41, doi: 10.5194.
  • Ressl, C., Pfeifer, N., Mandlburger, G. (2012). Applying 3D affine transformation and least squares matching for airborne laser scanning strips adjustment without GNSS/IMU trajectory data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38, 67-72.
  • Sánchez-García, E., Palomar-Vázquez, J. M., Pardo-Pascual, J. E., Almonacid-Caballer, J., Cabezas-Rabadán, C., Gómez-Pujol, L. (2020). An efficient protocol for accurate and massive shoreline definition from mid-resolution satellite imagery. Coastal Engineering, 160, 103732.
  • Souto-Ceccon P, Simarro G, Ciavola P, Taramelli A, Armaroli C. (2023) Shoreline Detection from PRISMA Hyperspectral Remotely-Sensed Images. Remote Sensing. 15(8):2117. https://doi.org/10.3390/rs1508211
  • Specht, M., Specht, C., Lewicka, O., Makar, A., Burdziakowski, P., Dąbrowski, P. (2020). Study on the coastline evolution in sopot (2008–2018) based on landsat satellite imagery. Journal of Marine Science and Engineering, 8(6), 464.
  • Toure, S., Diop, O., Kpalma, K., Maiga, A. S. (2019). Shoreline detection using optical remote sensing: A review. ISPRS International Journal of Geo-Information, 8(2), 75.
  • Uçar, D., İpbüker, C., Bildirici, İ. Ö. (2004). Matematiksel kartografya: harita projeksiyonları teorisi ve uygulamaları. Atlas Yayın Dağıtım.
  • Wang, Q., Liu, Y., Guo, Y., Wang, S., Zhang, Z., Cui, X., Zhang, H. (2022). A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry. Remote Sensing, 14(21), 5377.
  • Yılmaz, İ. (2009). The Basic Principals in Choosing Appropriate Map Projection. Electronic Journal of Map Technologies, 1 (2) , 31-42
  • Zeki, S. (2012). Assessing microbial water quality by membrane filtration and quantitative polymerase chain reaction (qPCR) methods at Golden Horn (PhD thesis). Istanbul University, Istanbul, Turkey.
There are 26 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Articles
Authors

Şaziye Özge Atik 0000-0003-2876-040X

Early Pub Date December 24, 2023
Publication Date December 26, 2023
Submission Date December 4, 2023
Acceptance Date December 24, 2023
Published in Issue Year 2023

Cite

APA Atik, Ş. Ö. (2023). Determination of Black Sea Coastline Length with Oblique Stereographic Projection Using Affine Transformation. International Journal of Environment and Geoinformatics, 10(4), 179-186. https://doi.org/10.30897/ijegeo.1399910