Kıyı çizgisi pozisyonlarını tahmin etmek için kalman filtresi modelinin uygulanması: Şile, İstanbul örneği

Year 2024, Issue: 85, 47 - 53, 30.06.2024

Hatice Kılar Olgu Aydın

https://doi.org/10.17211/tcd.1469434

Abstract

Kıyı bölgeleri, yeryüzünün en verimli ve çeşitli alanlarıdır, fakat aynı zamanda oldukça kırılgan bir ekosisteme sahiptir. Bu nedenle, kıyı şeritlerindeki hem zamansal hem de mekânsal değişimlerin incelenmesi ve gelecekteki kıyı şeridi konumunun tahmin edilmesi, kıyı bölgelerinin sürdürülebilirliğinin sağlanması açısından kritik öneme sahiptir. Bu çalışmada Şile'nin tarihsel kıyı şeridi değişimi (Şile limanın batısı ve Kumbaba Plajının doğu kıyısı), Dijital Kıyı Şeridi Değişim Analiz Sisteminin (DSAS), Uç Nokta Oranı (EPR), Net Kıyı Şeridi Hareketi (NSM) ve Doğrusal Regresyon Oranı (LRR) istatistikleri kullanılarak analiz edilmiştir. Gelecekteki kıyı şeridi tahmini, DSAS aracındaki Kalman Filtresi yöntemi ile belirlenmiştir. Çalışma sahasının tarihsel kıyı çizgisi değişiminin belirlenmesinde, 2002 ve 2021 yılları arasında Google Earth pro uydu görüntülerinden üretilmiş 18 veri setinden yararlanılmıştır. Çalışmanın istatistiksel sonucu Şile'nin 2002–2021 yılları arasında maksimum kıyı şeridi ilerlemesinin NSM için 41.3 m ve LRR için 2.6 m/yıl olduğunu, maksimum kıyı şeridi gerilemesinin ise NSM için 26.2 m ve EPR için 1.3 m/yıl olduğunu göstermektedir. Şile için öngörülen gelecekteki kıyı şeridi, en önemli kıyı şeridi ilerlemesinin 2031 ile 2041 yılları arasında, özellikle bölge I, bölge II ve bölge III gibi belirlenmiş alanlarda gerçekleşecektir. Tersine, aynı dönemlerde IV. bölgede kıyı şeridinde önemli bir gerilemenin meydana geleceği tahmin edilmektedir. Sonuç olarak, Şile kıyı şeridi tarihi boyunca önemli kıyı şeridi değişikliklerine sahne olmuştur ve gelecekte de önemli kıyı değişikliklerinin yaşanmaya devam etmesi beklenmektedir.

Keywords

Kıyı şeridi değişimi, Kıyı Hattı Tahmini, Kalman Filtresi, Şile, Dijital Kıyı Şeridi Analiz Sistemi (DSAS)

Applying the kalman filter model to forecast shoreline positions: A case study in Şile, İstanbul

Abstract

Coastal zones are remarkably productive and diverse environments on Earth, yet they are also highly vulnerable ecosystems. Therefore, examining both temporal and spatial variations in shorelines, as well as forecasting future shoreline position, is critical for ensuring the sustainability of coastal zones. In this study, historical shoreline change of the Şile (between western part of Şile port and eastern part of the Kumbaba Beach) was analyzed using End Point Rate (EPR), Net Shoreline Movement (NSM), and Linear Regression Rate (LRR) statistics of Digital Shoreline Change Analyses System (DSAS). Future shoreline forecasting was estimated using Kalman Filter method within DSAS tool. To analyze the historical shoreline changes in Şile, 18 shoreline data sets were generated from Google Earth Pro spanning the period from 2002 to 2021. The statistical result of the study indicates that the maximum shoreline progression of Şile between 2002 and 2021 was 41.3 m for NSM and 2.6 m/yr for LRR, while the maximum shoreline regression was -26.2 m for NSM and -1.3 m/yr for EPR. The projected future shoreline for Şile suggests that the most substantial shoreline advancement is anticipated to occur between 2031 and 2041, particularly in designated areas such as zone I, zone II, and zone III. Conversely, significant shoreline regression is forecasted to transpire in zone IV during the same periods. As a result, the shoreline of Şile has witnessed notable shoreline alterations throughout its history, and it is expected to continue experiencing significant changes in the future.

Keywords

Shoreline change, Shoreline Prediction, Kalman Filter, Şile, Digital Shoreline Analysis System (DSAS).

Ethical Statement

Dear Editor, I present to you our manuscript titled “Applying the kalman filter model to forecast shoreline positions: a case study in Şile, İstanbul”. I and the other author certify that this study has not been published in any journal, that it is not under consideration for publication elsewhere, and that its submission for publication in “Türk Coğrafya Dergisi” has been approved by all of the authors and the institution where the work was carried out. Any change in my address, telephone or fax will immediately be directed to the Editorial Office. We affirm that we have no financial affiliation (e.g., employment, direct payment, stock holdings, retainers, consultant ships, patent licensing arrangements, or honoraria) or involvement with any commercial organization with direct financial interest in the subject or materials discussed in this manuscript, nor have any such arrangements existed in the past three years. Any other potential conflict of interest is disclosed. I will be happy if you kindly accept our manuscript for editorial review. I look forward to hearing from you soon. Yours faithfully.

References

• Acharyya, R., Mukhopadhyay, A., & Habel, M. (2023). Coupling of SWAT and DSAS Models for Assessment of Retrospective and Prospective Transformations of River Deltaic Estuaries. Remote Sensing, 15(4). https://doi.org/10.3390/rs15040958
• Anthony, E. J. (2015). Wave influence in the construction, shaping and destruction of river deltas: A review. Marine Geology, 361, 53–78. https://doi.org/10.1016/j.margeo.2014.12.004
• Armenio, E., De Serio, F., Mossa, M., & Petrillo, A. F. (2019). Coastline evolution based on statistical analysis and modeling. Natural Hazards and Earth System Sciences, 19(9), 1937–1953. https://doi.org/10.5194/nhess-19-1937-2019
• Awad, M., & El-Sayed, H. M. (2021). The analysis of shoreline change dynamics and future predictions using automated spatial techniques: Case of El-Omayed on the Mediterranean coast of Egypt. Ocean and Coastal Management, 205, 105568. https://doi.org/10.1016/j.ocecoaman.2021.105568
• Aydın, M., & Uysal, M. (2014). Risk assessment of coastal erosion of Karasu coast in Black Sea. Journal of Coastal Conservation, 18(6), 673–682. https://doi.org/10.1007/s11852-014-0343-y
• Baş, N., Koç, A. (2023). An effective approach for analysis of shoreline change and determination of its future location using satellite imagery: A case study of the Lake Burdur, Turkey. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 14, 61–72. https://doi.org/10.17714/gumusfenbil.1259676
• Bheeroo, R. A., Chandrasekar, N., Kaliraj, S., & Magesh, N. S. (2016). Shoreline change rate and erosion risk assessment along the Trou Aux Biches–Mont Choisy beach on the northwest coast of Mauritius using GIS-DSAS technique. Environmental Earth Sciences, 75(5), 1–12. https://doi.org/10.1007/s12665-016-5311-4
• Chrisben Sam, S., & Gurugnanam, B. (2022). Coastal transgression and regression from 1980 to 2020 and shoreline forecasting for 2030 and 2040, using DSAS along the southern coastal tip of Peninsular India. Geodesy and Geodynamics, 13(6), 585–594. https://doi.org/10.1016/j.geog.2022.04.004
• Dang, K. B., Dang, V. B., Ngo, V. L., Vu, K. C., Nguyen, H., Nguyen, D. A., Nguyen, T. D. L., Pham, T. P. N., Giang, T. L., Nguyen, H. D., & Hieu Do, T. (2022). Application of deep learning models to detect coastlines and shorelines. Journal of Environmental Management, 320, 115732. https://doi.org/10.1016/j.jenvman.2022.115732
• Deepika, B., Avinash, K., & Jayappa, K. S. (2014). Shoreline change rate estimation and its forecast: Remote sensing, geographical information system and statistics-based approach. International Journal of Environmental Science and Technology, 11(2), 395–416. https://doi.org/10.1007/s13762-013-0196-1
• Di Stefano, A., De Pietro, R., Monaco, C., & Zanini, A. (2013). Anthropogenic influence on coastal evolution: A case history from the Catania Gulf shoreline (eastern Sicily, Italy). Ocean and Coastal Management, 80, 133–148. https://doi.org/10.1016/j.ocecoaman.2013.02.013
• Ertek, T. A. (2016, Ekim 13-14). İnsan Faaliyetlerine Bağlı Jeomorfolojik Yıkımlar. TÜCAUM Uluslararası Coğrafya Sempozyumu, (s. 201–219). Ankara.
• Farris, A. S., Long, J. W., & Himmelstoss, E. A. (2023). Accuracy of shoreline forecasting using sparse data. Ocean and Coastal Management, 239, 106621. https://doi.org/10.1016/j.ocecoaman.2023.106621
• Ferreira, T. A. B., Aquino da Silva, A. G., Reyes Perez, Y. A., Stattegger, K., & Vital, H. (2021). Evaluation of decadal shoreline changes along the Parnaíba Delta (NE Brazil) using satellite images and statistical methods. Ocean and Coastal Management, 202, 105513. https://doi.org/10.1016/j.ocecoaman.2020.105513
• Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S. (2018). Digital Shoreline Analysis System (DSAS) version 5.0 user guide: U.S. Geological Survey OpenFile Report. 1–126. https://doi.org/10.3133/ ofr20181179.
• Himmelstoss, E. A., Henderson, R. E., Kratzmann, M. G., & Farris, A. S. (2021). Digital shoreline analysis system ( DSAS ) version 5.1 user guide (Publication No. 2021–1091). U.S. Geological Survey. https://doi.org/10.3133/ofr20211091.
• İncekara, S. (2001). Integrated coastal zone management and sustainable development: A case study of Şile using GIS. (Yüksek Lisans Tezi, Fatih Üniversitesi, Sosyal Bilimler Enstitüsü, Coğrafya Ana Bilim Dalı). Erişim adresi: https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp? id=spUIejX3okRnm_OCCa2OSA&no=NQYYi7mJCdQbYsbjwwxwcA
• Jangir, B., Satyanarayana, A. N. V., Swati, S., Jayaram, C., Chowdary, V. M., & Dadhwal, V. K. (2016). Delineation of spatio-temporal changes of shoreline and geomorphological features of Odisha coast of India using remote sensing and GIS techniques. Natural Hazards, 82(3), 1437–1455. https://doi.org/10.1007/s11069-016-2252-x
• Kazı, H., & Karabulut, M. (2023). Monitoring the shoreline changes of the Göksu Delta (Türkiye) using geographical information technologıes and predictions for the near future. Lnternational Journal of Geography and Geography Education, 50, 329–352. https://doi.org/10.32003/igge.1304403
• Kılar, H., & Çiçek, İ. (2019). Kıyı Çizgisinin Gelecekteki Konumunun Belirlenmesinin Önemi: Göksu Deltası Örneği, Mersin (Türkiye). Coğrafi Bilimler Dergisi, 17(1), 193–216. https://doi.org/10.33688/aucbd.559328
• Mahapatra, M., Ratheesh, R., & Rajawat, A. S. (2014). Shoreline Change Analysis along the Coast of South Gujarat, India, Using Digital Shoreline Analysis System. Journal of the Indian Society of Remote Sensing, 42(4), 869–876. https://doi.org/10.1007/s12524-013-0334-8
• Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of High Resolution Google Earth Satellite Imagery in Landuse Map Preparation for Urban Related Applications. Procedia Technology, 24, 1835–1842. https://doi.org/10.1016/j.protcy.2016.05.231
• Mishra, M., Chand, P., Beja, S. K., Santos, C. A. G., Silva, R. M.assessment of present and the future potential threat of coastal erosion along the Odisha coast using geospatial tools and statistical techniques. Science of The Total Environment, 875, 162488. https://doi.org/10.1016/j.scitotenv.2023.162488
• Muskananfola, M. R., & Febrianto, S. (2020). Spatio-temporal analysis of shoreline change along the coast of Sayung Demak , Indonesia using Digital Shoreline Analysis System. Regional Studies in Marine Science, 34, 101060. https://doi.org/10.1016/j.rsma.2020.101060
• Nassar, K., Mahmod, W. E., Fath, H., Masria, A., Nadaoka, K., & Negm, A. (2019). Shoreline change detection using DSAS technique: Case of North Sinai coast, Egypt. Marine Georesources and Geotechnology, 37(1), 81–95. https://doi.org/10.1080/1064119X.2018.1448912
• Natarajan, L., Sivagnanam, N., Usha, T., Chokkalingam, L., Sundar, S., Gowrappan, M., & Roy, P. D. (2021). Shoreline changes over last five decades and predictions for 2030 and 2040: a case study from Cuddalore, southeast coast of India. Earth Science Informatics, 14(3), 1315–1325. https://doi.org/10.1007/s12145-021-00668-5
• Nazeer, M., Waqas, M., Shahzad, M. I., Zia, I., & Wu, W. (2020). Coastline vulnerability assessment through landsat and cubesats in a coastal mega city. Remote Sensing, 12(5), 1–24. https://doi.org/10.3390/rs12050749
• Nijamir, K., Ameer, F., Thennakoon, S., Herath, J., Iyoob, A. L., Zahir, I. L. M., Sabaratnam, S., Fathima Jisna, M. V., & Madurapperuma, B. (2023). Geoinformatics application for estimating and forecasting of periodic shoreline changes in the east coast of Ampara District, Sri Lanka. Ocean & Coastal Management, 232, 106425. https://doi.org/10.1016/J.OCECOAMAN.2022.106425
• Obiene, E. A., Rowland, E. D., & Michael, I.-T. I. (2022). Analysis of Shoreline Changes in Ikoli River in Niger Delta Region Yenagoa, Bayelsa State Using Digital Shoreline Analysis System (DSAS). Journal of Marine Science, 4(1), 34–42. https://doi.org/10.30564/jms.v4i1.4197
• Özyurt, G., & Ergin, A. (2009). Application of sea level rise vulnerability assessment model to selected coastal areas of Turkey. Journal of Coastal Research, I(56), 248–251. https://www.jstor.org/stable/25737575
• Roy, S., Mahapatra, M., & Chakraborty, A. (2018). Shoreline change detection along the coast of Odisha, India using digital shoreline analysis system. Spatial Information Research, 26(5), 563–571. https://doi.org/10.1007/s41324-018-0199-6
• San, B. T., & Ulusar, U. D. (2018). An approach for prediction of shoreline with spatial uncertainty mapping (SLiP-SUM). International Journal of Applied Earth Observation and Geoinformation, 73, 546–554. https://doi.org/10.1016/j.jag.2018.08.005
• Santos, C. A. G., Nascimento, T. V. M. do, Mishra, M., & Silva, R. M. da. (2021). Analysis of long- and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil. Science of the Total Environment, 769, 144889. https://doi.org/10.1016/j.scitotenv.2020.144889
• Saranathan, E., Chandrasekaran, R., Manickaraj, D. S., & Kannan, M. (2011). Shoreline Changes in Tharangampadi Village, Nagapattinam District, Tamil Nadu, India-A Case Study. Journal of the Indian Society of Remote Sensing, 39(1), 107–115. https://doi.org/10.1007/s12524-010-0052-4
• Selvavinayagam, K. (2008, January). ShoreLine Change Monitoring in Coastal India, Using Remote Sensing and GIS Tools.
• Shailesh Nayak. (2002). Use of satellite data in coastal zone programmes. Indian Cartographer, 5, 147–157.
• Siyal, A. A., Solangi, G. S., Siyal, Z.-A., Siyal, P., Babar, M. M., & Ansari, K. (2022). Shoreline change assessment of Indus delta using GIS-DSAS and satellite data. Regional Studies in Marine Science, 53, 102405. https://doi.org/10.1016/j.rsma.2022.102405
• Tuzlacı, E. U., & Tolon, E. (2000). Turkish folk medicinal plants, part III: Şile (Istanbul). Fitoterapia, 71, 673-685. https://doi.org/10.1016/S0367-326X(00)00234-3
• Uzun, M. (2023). Riva (İstanbul) Kıyılarında Doğal ve Antropojenik Etkenlerle Değişen Kıyı Çizgisinin DSAS Aracı ile Analizi. Jeomorfolojik Araştırmalar Dergisi, 2023(11), 95–113. https://doi.org/10.46453/jader.1335105
• Yan, D., Yao, X., Li, J., Qi, L., & Luan, Z. (2021). Shoreline Change Detection and Forecast along the Yancheng Coast Using a Digital Shoreline Analysis System. Wetlands, 41(4), 47. https://doi.org/10.1007/s13157-021-01444-3