Shoreline change rate dynamics analysis and prediction of future positions using satellite imagery for the southern coast of Kuwait: A case study

• Autorzy: Aladwani Najeeb S.

Identyfikatory

DOI

10.1016/j.oceano.2022.02.002

• Języki publikacji: EN

Abstrakty

EN

The studied shoreline extends 73 kilometers, starting from the Kuwait-Saudi Arabia borders northward. This work represents a limited work that analyses the historical shoreline positions. Five-dates satellite images along a period of 35 years between 1986 and 2021 were used to calculate the historical shoreline change rates and predict future shoreline positions in 2030 and 2050. First, the historical shoreline was extracted using the Normalized Difference Wader index (WI 2015). Then, the shoreline change rates were quantified statistically using the End Point Rate (EPR) and the Linear Regression Rate (LRR) models of the Digital Shoreline Analysis System (DSAS) tool in ArcMap. It has been found that the maximum erosion rate is –9.73 m/year, and the highest accretion is 10.88 m/year. Also, the positions of shorelines in the year 2030 and 2050 were predicted and defined on the map, with mapping of gain and loss surfaces. The results defined the most stable areas for future development and the areas needing urgent protection. It has been found that the resulting model can be affected by the topographical changes of the beaches due to human activities, where the coast alongside the Al-Khiran project will be eroded and accreted less than predicted because of the presence of protection facilities.

Słowa kluczowe

EN

shoreline change rates; shoreline position extraction; future shoreline position; EPR models; LRR models; Al-Khiran project

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2022
• Tom: No. 64 (3)
• Strony: 417--432
• Opis fizyczny: Bibliogr. 35 poz., map., rys., tab., wykr.

Twórcy

autor

Aladwani Najeeb S.

• Department of Earth and Environmental Sciences, Faculty of Science, Kuwait University, Kuwait

• https://orcid.org/0000-0003-2081-3124

Bibliografia

• 1. Alizadeh, M.J., Alinejad-Tabrizi, T., Kavianpour, M.R., Shamshirband, S., 2020. Projection of spatiotemporal variability of wave power in the Persian Gulf by the end of 21st century: GCM and CORDEX ensemble. J. Cleaner Prod. 256, 120400. https://doi.org/10.1016/j.jclepro.2020.120400
• 2. Atiquzzaman, M., Kandasamy, J., 2015. Prediction of hydrological time-series using extreme learning machine. J. Hydroinf. 18 (2), 345-353. https://doi.org/10.2166/hydro.2015.020
• 3. Boak, E.H., Turner, I.L., 2005. Shoreline definition and detection: a review. J. Coast Res. 21 (4), 688-703. https://doi.org/10.2112/03-0071.1
• 4. Barik, K.K., Annaduari, R., Mohanty, P.C., Mahendra, R.S., Tripathy, J.K., Mitra, D., 2019. Statistical assessment of long-term shoreline changes along the Odisha coast. Indian J. Geo Mar. Sci. 48 (12), 1990-1998.
• 5. Deepika, B., Avinash, K., Jayappa, K.S., 2014. Shoreline changerate estimation and its forecast: remote sensing, geographical information system and statistics-based approach. Int. J. Environ. Sci. Technol. 11, 395-416. https://doi.org/10.1007/s13762-013-0196-1
• 6. Ding, Y., Wang, S.S.Y., 2008. Development and application of a coastal and estuarine morphological process modeling system. J. Coast Res. 52, 127-140. https://doi.org/10.2112/1551-5036-2.sp1.127
• 7. Esmail, M., Mahmod, W.E., Fatha, H., 2019. Assessment and prediction of shoreline change using multi-temporal satellite images and statistics: a case study of Damietta coast. Egypt. Appl. Ocean Res. 82, 274-282. https://doi.org/10.1016/j. apor.2018.11.009
• 8. Fenster, M.S., Dolan, R., Elder, J.F., 1993. A new method for predicting shoreline positions from historical data. J. Coast Res. 9 (1), 147-171.
• 9. Fisher, A., Flood, N., Danaher, T., 2016. Comparing Landsat water index methods for automated water classification in eastern Australia. Rem. Sens. Environ. 175, 167-182. https://doi.org/10.1016/j.rse.2015.12.055
• 10. Frihy, O.E., 2009. Morphodynamic implications for shoreline management of the western Mediterranean sector of Egypt. Environ. Geol. 58, 1177-1189. https://doi.org/10.1007/s00254-008-1595-3
• 11. Gokceoglu, C., Nefeslioglu, H.A., Turer, D., Akgun, A., Ayas, Z., Temimhan, M., 2015. Determination of coastal border line: an integrated approach for a part of Antalya coast (Turkey). Arab. J. Geosci. 8, 1145-1154. https://doi.org/10.1007/s12517-014-1287-0
• 12. Goharnejad, H., Nikaein, E., Perrie, W., 2021. Assessment of wave energy in the Persian Gulf: An evaluation of the impacts of climate change. Oceanologia 63 (1), 27-39. https://doi.org/10.1016/j.oceano.2020.09.004
• 13. Hakkou, M., Maanan, M., Belrhaba, T., El khalidi, K., El Ouai, D., Benmohammadi, A., 2018. Multi-decadal assessment of shoreline changes using geospatial tools and automatic computation in Kenitra coast. Morocco. Ocean Coast Manage. 163, 232-239. https://doi.org/10.1016/j.ocecoaman.2018.07.003
• 14. Hassan, A., 2018. The Environmental Geomorphological Assessment of the Urban Expansion in Al-Khiran Marine City. Kuwait. J. Social Sci. 46 (1), 31-54.
• 15. Hassan, A., 2016. Human encroachments and their environmental geomorphological effects on the sustainable development for coastal areas in Kuwait. Ph.D. thesis, Geograph. Dep., Faculty of Social Sciences, Kuwait University.
• 16. Huang, C., Wu, M., Sun, W., Bian, G., He, J., Deng, K., Zhai, G., 2019. Improving the definition and algorithms of China’s coastline considering the diversity of tidal characteristics. Mar. Geodes. 42 (4), 382-405. https://doi.org/10.1080/01490419.2019.1610816
• 17. Kanwal, S., Ding, X., Sajjad, M., Abbas, S., 2020. Three decades of coastal changes in Sindh, Pakistan (1989—2018): a geospatial assessment. Rem. Sens. 12 (1), 8. https://doi.org/10.3390/rs12010008
• 18. Khalaf, F.I., Gharib, I.M., Al-Hashash, M.Z., 1984. Types and characteristics of the recent surface deposits of Kuwait, Arabian Gulf. J. Arid Environ. 7, 9-33.
• 19. Kumar, A., Jayappa, K.S., 2009. Long and short-term shoreline changes along Mangalore coast. India. Int. J. Environ. Res. 3, 177-188.
• 20. Masek, J.G., Vermote, E.F., Saleous, N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., Lim, T., 2006. A Landsat surface reflectance dataset for North America, 1990—2000. IEEE Geosci. Rem. Sens. Lett. 3, 68-72. https://doi.org/10.1109/LGRS.2005.857030
• 21. Moore, L., 2000. Shoreline mapping techniques. J. Coast Res. 16, 111-124 https://www.jstor.org/stable/4300016 (accessed 18February 2021).
• 22. Nandi, S., Ghosh, M., Kundu, A., Dutta, D., Baksi, M., 2016. Shoreline shifting and its prediction using remote sensing and GIS techniques: a case study of Sagar Island, West Bengal (India). J. Coast Conserv. 20, 61-80. https://doi.org/10.1007/s11852-015-0418-4
• 23. Natesan, U., Parthasarathy, A., Vishnunath, R., Kumar, G.E.J., Ferrer, V.A., 2015. Monitoring longterm shoreline changes along Tamil Nadu, India using geospatial techniques. Aquat. Procedia 4, 325-332. https://doi.org/10.1016/j.aqpro.2015.02.044
• 24. Nassar, K., Fath, H., Mahmod, W.E., Masria, A., Nadaoka, K., Negm, A., 2018. Automatic detection of shoreline change: case of North Sinai coast. Egypt. J. Coast Conserv. 22, 1057-1083. https://doi.org/10.1007/s11852-018-0613-1
• 25. Pardo-Pascual, J.E., Almonacid-Caballer, J., Ruiz, L.A., Palomar-Vázquez, J., 2012. Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision. Remote Sens. Environ. 123, 1-11.
• 26. Qiao, G., Mi, H., Wang, W., Tong, X., Li, Z., Li, T., Liu, S., Hong, Y., 2018. 55-year (1960—2015) spatiotemporal shoreline change analysis using historical DISP and Landsat time series data in Shanghai. Int. J. Appl. Earth Obs. Geoinf. 68, 238-251. https://doi.org/10.1016/j.jag.2018.02.009
• 27. Rady, A., 2011. Profile of Sustainability in Some Mediterranean Tourism Destinations. Case Studies in Egypt: Marsa Matrouh, Al Alamein, Siwa Oasis (Matrouh Governorate). Final Report. UNEP/MAP Regional Activity Centre, Plan Bleu, Sophia Antipolis.
• 28. Rakha, K., Al-Salem, K., Neelamani, S., 2007. Hydrodynamic atlas for the Arabian Gulf. J. Cost. Res. 550-554.Ruiz-Beltran, A.P., Astorga-Moar, A., Salles, P., Appendini, C.M.,2019. Short-term shoreline trend detection patterns using SPOT-5 image fusion in the northwest of Yucatan. Mexico. Estuar. Coast 42, 1761-1773. https://doi.org/10.1007/s12237-019-00573-7
• 29. Salmon, C., Duvat, V.K.E., Laurent, V., 2019. Human- and climate-driven shoreline changes on a remote mountainous tropical Pacific island: Tubuai, French Polynesia. Anthropocene 25, 100191. https://doi.org/10.1016/J.ANCENE.2019.100191
• 30. Taebi, S., Golshani, A., Chegini, V., 2008. An approach towards wave climate study in The Persian Gulf and the Gulf of Oman: simulation and validation 11-26.
• 31. Tong, K., Granat, M., 1999. A practical gait analysis system using gyroscopes. Med. Eng. Phys. 21, 87-94. https://doi.org/10.1016/S1350-4533(99)00030-2
• 32. Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., Ayhan, E., 2009. Digital Shoreline Analysis System (DSAS) Version 4.0 (Updated for Version 4.3) — an ArcGIS Extension for Calculating Shoreline Change: U.S. Geological Survey. Open-File Report 2008—1278. https://cmgds.marine.usgs.gov/publications/DSAS/of20081278/data/DSASv4_3.pdf (accessed 21 July 2020)
• 33. Vermote, E., Justice, C., Claverie, M., Franch, B., 2016. Preliminary analysis of the performance of the Landsat 8/OLI land Surface reflectance product. Rem. Sens. Environ. 185, 46-56. https://doi.org/10.1016/j.rse.2016.04.008
• 34. Vieira, F., Cavalcante, G., Campos, E., 2020. Analysis of wave climate and trends in a semi-enclosed basin (Persian Gulf) using a validated SWAN model. Ocean Eng. 196, 106821. https://doi.org/10.1016/j.oceaneng.2019.106821
• 35. Žilinskas, G., Janušait ̇e, R., Jarmalaviˇcius, D., Pupienis, D., 2020. The impact of Klaip ̇eda Port entrance channel dredging on the dynamics of coastal zone. Lithuania. Oceanologia 62 (4A), 489-500. https://doi.org/10.1016/j.oceano.2020.08.002

Uwagi

PL

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).