Abstract
The prediction of the future position of the shoreline is of great importance in planning studies in coastal areas, in making effective decisions in coastal management, and in determining changes occurring on the coast. In this study, coastal change analyses were performed in two different study areas (the Gulf of Izmit and the Göksu Delta) by using satellite images of different dates, and the accuracy of the Kalman filter-based future shoreline prediction method was determined by statistical methods. In this context, by using the 19-period Landsat satellite image belonging to different dates between 1975 and 2019 for the Gulf of Izmit and the 10-period Landsat satellite image belonging to different dates between 1984 and 2018 for the Göksu Delta, shorelines were extracted automatically, and coastal changes were analyzed at a 95% CI by the statistical methods of end point rate, linear regression rate, and weighted linear regression rate (WLR). Afterward, the shorelines extracted automatically on the determined dates were compared with 10-year and 20-year predicted shorelines by the Kalman filter-based prediction method, and their accuracy was statistically analyzed. As a result, the 10-year predicted shorelines by the WLR method were found to provide the highest accuracy.
Similar content being viewed by others
References
Addo KA, Jayson-Quashigah P, Codjoe SNA, Martey F (2018) Drone as a tool for coastal flood monitoring in the Volta Delta, Ghana. Geoenviron Disasters 5(17):2–13. https://doi.org/10.1186/s40677-018-0108-2
Basiouny ME, El Kafrawy SB, Ghanem EA, Taha a (2017) Shoreline change rate detection and future prediction using remote sensing and GIS techniques: a case study of Ras EL-Hekma, North Western Coast, Egypt. J Geogr Environ Earth Sci Int 9(3):1–14. https://doi.org/10.9734/JGEESI/2017/32086
Çelik B, Degirmenci F, Yıldırım S, Boguşlu H (2000) Giresun, Trabzon ve Rize İllerindeki Kıyı Çizgisi Değişimleri ve Çözüm Önerileri. III. Ulusal Kıyı Mühendisliği Sempozyumu, Çanakkale, pp 215–230
Ceylan M (2012) Uzaktan Algılama ve CBS ile Kıyı Çizgisi Değişiminin Belirlenmesi; İzmit Körfezi Örneği. Hava Harp Okulu Havacılık ve Uzay Teknolojileri Enstitüsü, Yüksek Lisans Tezi
Cırıtcı D (2020) İzmit Körfezi Kıyı Değişiminin Coğrafi Bilgi Sistemleri Ve Uzaktan Algılama Yöntemleriyle Otomatik Belirlenmesi. Sivas Cumhuriyet Üniversitesi Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi
Çölkesen İ, Sesli AF (2007) Kıyı Çizgisinde Meydana Gelen Zamansal Değişimlerin Bilgi Teknolojileri ile Belirlenmesi: Trabzon Örneği. TMMOB Harita ve Kadastro Mühendisleri Odası Ulusal Coğrafi Bilgi Sistemleri Kongresi, 30 Ekim–02 Kasım, KTÜ, Trabzon
Daniel WW, Terrell JC (1995) Business statistics for management and economics, 7th edn. Houghton Mifflin Company, Boston (MA)
Davidson MA, Lewis RP, Turner IL (2010) Forecasting seasonal to multi-year shoreline change. Coast Eng 57:620–629. https://doi.org/10.1016/j.coastaleng.2010.02.001
Deepika B, Avinash K, Jayappa KS (2014) Shoreline change rate estimation and its forecast: remote sensing, geographical information system and statistics-based approach. Int J Environ Sci Technol 11(2):395–416. https://doi.org/10.1007/s13762-013-0196-1
Demir N, Bayram B, Şeker DZ, Oy S, İnce A, Bozkurt S (2019) Advanced lake shoreline extraction approach by integration of SAR image and LIDAR data. Mar Geod 42(2):166–185
Esteves LS (2004) Shoreline changes and coastal evolution as parameters to identify priority areas for management in Rio Grande do Sul, Brazil. Revista Pesquisas em Geociências 31(2):15–30. https://doi.org/10.1590/s1679-87592018009806602
Fearnley SM, Miner MD, Kulp M, Bohling C, Penland S (2009) Hurricane impact and recovery shoreline change analysis of the Chandeleur Islands, Louisiana, USA: 1855 to 2005. Geo-Mar Lett 29(6):455–466. https://doi.org/10.1007/s00367-009-0155-5
Fenstert MS, Dolant R, Elder JF (1993) A new method for predicting shoreline position historical data. J Coastal Res 9(1):147–171
Fereydooni H, Mojeddifar S (2017) A directed matched filtering algorithm (DMF) for discriminating hydrothermal alteration zones using the ASTER remote sensing data. Int J Appl Earth Obs Geoinf 61:1–13. https://doi.org/10.1016/j.jag.2017.04.010
Ferreira MP, Wagner FH, Aragao LEOC, Shimabukuro YE, Souza Filho CR (2019) Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis. ISPRS J Photogramm Remote Sens 149:119–131. https://doi.org/10.1016/j.isprsjprs.2019.01.019
Garipağaoğlu N, Uzun SM (2014) İzmit Körfezi kıyılarında kıyı alanı kullanımı. Türk Coğrafya Dergisi 63:9–22
Gorokhovich Y, Leiserowiz A (2012) Historical and future coastal changes in Northwest Alaska. J Coast Res 28(1):174–186. https://doi.org/10.2112/JCOASTRES-D-11-00031.1
Himmelstoss EA, Farris AS, Henderson RE, Kratzmann MG, Ergul A, Zhang O, Zichichi JL, Thieler ER (2018) Digital shoreline analysis system (version 5.0): U.S. Geological Survey software. https://code.usgs.gov/cch/dsas/
Kalman RE (1960) A new approach to linear filtering and prediction problems. J Fluids Eng 82(1):35–45. https://doi.org/10.1115/1.3662552
Karakoç A, Karabulut M (2010) Göksu Deltası Kıyı Çizgisinde Meydana Gelen Değişimlerin CBS ve Uzaktan Algılama Teknikleri ile İncelenmesi. Ulusal Jeomorfoloji Sempozyumu 2010 Bildiriler Kitabı, Hatay, pp 195–205
Kermani S, Boutiba M, Guendouz M, Guettouche MS, Khelfani D (2016) Detection and analysis of shoreline changes using geospatial tools and automatic computation: case of jijelian sandy coast (East Algeria). Ocean Coast Manag 132:46–58
Kılar H, Çiçek İ (2019) Kıyı Çizgisinin Gelecekteki Konumunun Belirlenmesinin Önemi: Göksu Deltası Örneği, Mersin (Türkiye). Turk J Geogr Sci 17(1):193–216. https://doi.org/10.33688/aucbd.559328
Long JW, Plant NG (2012) Extended Kalman Filter framework for forecasting shoreline evolution. Geophys Res Lett 39:13. https://doi.org/10.1029/2012GL052180
Mukhopadhyay A, Mukherjee S, Mukherjee S, Ghosh S, Hazra S, Mitra D (2012) Automatic shoreline detection and future prediction: a case study on Puri Coast, Bay of Bengal, India. Eur J Remote Sens 45(1):201–213. https://doi.org/10.5721/EuJRS20124519
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(1):61–80. https://doi.org/10.1007/s11852-015-0418-4
Papakonstantinou A, Topouzelis K, Pavlogeorgatos G (2016) Coastline change detection using UAV, remote sensing, GIS and 3D reconstruction. ISPRS Int J Geo-Inf 5(6):75. https://doi.org/10.3390/ijgi5060075
Peponi A, Morgado P, Trindade J (2019) combining artificial neural networks and GIS fundamentals for coastal erosion prediction modeling. Sustainability 11:975. https://doi.org/10.3390/su11040975
Uzun M, Garipagaoglu N (2014) Kıyı Çizgisi Değişimin Yaratacağı Riskler Açısından İzmit Körfezi Kıyılarının Değerlendirilmesi. Uluslararası Sosyal Araştırmalar Dergisi 7(33):469–480
Acknowledgements
The authors would like to thank the United States Geological Survey (USGS) for the Landsat satellite images and DSAS 5.0 tool.
Author information
Authors and Affiliations
Corresponding author
Additional information
Editorial responsibility: Shahid Hussain.
Rights and permissions
About this article
Cite this article
Ciritci, D., Türk, T. Assessment of the Kalman filter-based future shoreline prediction method. Int. J. Environ. Sci. Technol. 17, 3801–3816 (2020). https://doi.org/10.1007/s13762-020-02733-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-020-02733-w