Detection of Shoreline Changes due to Abrasion and Accretion Using Landsat Imagery – A Case Study in the Coastal Areas of Supiori Regency, Indonesia

• Autorzy: Rumahorbo Basa T. , Warpur Maklon , Hamuna Baigo

Identyfikatory

DOI

10.12912/27197050/169358

• Języki publikacji: EN

Abstrakty

EN

Shoreline changes have become a serious problem in all coastal areas worldwide. This study aimed to detect shoreline changes and analyze the shoreline change rate caused by abrasion and accretion in the coastal area of Supiori Regency, Indonesia. Landsat 8/9 imagery was used to determine the position of the coastline in 2013 and 2023. The shoreline movement (Net Shoreline Movement) and the shoreline change rate (End Point Rate) were analyzed using the Digital Shoreline Analysis System installed on ArcMap software. The results of this study indicate that there has been abrasion and accretion where there are several very significant locations. The maximum distance of the shoreline movements due to abrasion and accretion occurred in the Supiori Selatan District as far as -67.15 and 92.86 m, respectively. The average shoreline movement caused by abrasion ranges from -11.37 to -13.59 m and from 9.75 to 15.64 m in the case of accretion. From the comparison of abrasion and accretion, only the Kepulauan Aruri District has a positive value (dominant accretion), while the other four districts have a negative value (dominant abrasion). The shoreline changes rates in the study area caused by abrasion and accretion ranged from -1.22 to -1.46 m/yr and 1.05 to 1.68 m/yr, respectively. Abrasion and accretion in the study area are predominantly caused by natural factors such as waves, currents, and river flows, as well as caused by non-natural factors mainly due to human activities. Information on shoreline changes in the study area is an important aid for stakeholders involved in coastal area management. Therefore, planning, strategies, and mitigation efforts are urgently needed to anticipate increased coastal erosion and possible negative impacts.

Słowa kluczowe

EN

abrasion; accretion; DSAS; Landsat; shoreline movement; shoreline change rates

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2023
• Tom: Vol. 24, iss. 7
• Strony: 161--171
• Opis fizyczny: Bibliogr. 44 poz., rys., tab.

Twórcy

autor

Rumahorbo Basa T.

• Department of Marine Science and Fisheries, Cenderawasih University, Kamp Wolker Street, Jayapura City 99333, Papua Province, Indonesia

• https://orcid.org/0000-0002-0189-3619

autor

Warpur Maklon

• Department of Marine Science and Fisheries, Cenderawasih University, Kamp Wolker Street, Jayapura City 99333, Papua Province, Indonesia

• https://orcid.org/0009-0007-1149-5140

autor

Hamuna Baigo

• Department of Marine Science and Fisheries, Cenderawasih University, Kamp Wolker Street, Jayapura City 99333, Papua Province, Indonesia

• https://orcid.org/0000-0002-0706-2496

Bibliografia

• 1. Amukti R., Adji A.S., Ruslan S. 2020. Analysis of shoreline shift using satellite imagery near Makassar City. Journal of Geoscience, Engineering, Environment, and Technology, 5(3), 119–123.
• 2. Armenio E., Serio F.D., Mossa M., Petrillo A.F. 2019. Coastline evolution based on statistical analysis and modeling. Natural Hazards and Earth System Sciences, 19(9), 1937–1953.
• 3. Badan Pusat Statistik Kabupaten Supiori. 2022. Kabupaten Supiori Dalam Angka 2022. Badan Pusat Statistik Kabupaten Supiori, Supiori. [in Indonesian]
• 4. Baig M.R.I., Ahmad I.A., Shahfahad S., Tayyab M., Rahman A. 2020. Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: An application of digital shoreline analysis system (DSAS). Annals of GIS, 26(4), 361–376.
• 5. Burke L., Selig E., Spalding M. 2002. Reefs at Risk in Southeast Asia. World Resources Institute, Washington DC.
• 6. CendreroA. 1989. Mapping and evaluation of coastal areas for planning. Ocean and Shoreline Management, 12(5–6), 427–462.
• 7. Cui B.-L., Li X.-Y. 2011. Coastline change of the Yellow River estuary and its response to the sediment and runoff (1976–2005). Geomorphology, 127(1–2), 32–40.
• 8. Dewi E.K., Trisakti B. 2016. Comparing atmospheric correction methods for Landsat OLI data. International Journal of Remote Sensing and Earth Sciences, 13(2), 105–120.
• 9. Dronkers J. 2005. Dynamics of Coastal Systems. World Scientific, Singapore.
• 10. Fan Y., Chen S., Zhao B., Pan S., Jiang C., Ji H. 2018. Shoreline dynamics of the active Yellow River delta since the implementation of Water-Sediment Regulation Scheme: A remote-sensing and statistics-based approach. Estuarine, Coastal and Shelf Science, 200, 406–419.
• 11. Goksel C., Senel G., Dogru A.O. 2020. Determination of shoreline change along the Black Sea coast of Istanbul using remote sensing and GIS technology. Desalination and Water Treatment, 177, 242–247.
• 12. Hamuna B., Kalor J.D., Tablaseray V.E. 2019. The impact of tsunami on mangrove spatial change in eastern coastal of Biak Island, Indonesia. Journal of Ecological Engineering, 20(3), 1–6.
• 13. Hanzu R., Duse A., Grosan N., Varsami C. 2015. New Perspectives for Marine Environment Protection Through Innovative Marine Propulsion Systems. International Journal of Modern Manufacturing Technologies, 7(2), 62–65.
• 14. Hartati R., Pribadi R., Astuti R.W., Yesiana R., Yuni H.I. 2016. A study of coastal security and protection in the coastal area of Tugu and Genuk Districts, Semarang City. Jurnal Kelautan Tropis, 19(2), 95–100. [in Indonesian]
• 15. Hawati P., Sugianto D.N., Anggoro S., Wirasatriya A., Widada S. 2017. Waves Induce Sediment Transport at Coastal Region of Timbulsloko Demak. IOP Conf. Series: Earth and Environmental Science, 55, 012048.
• 16. Kaly U., Pratt C., Mitchell J. 2004. The Environmental Vulnerability Index (EVI) 2004. SOPAC Technical Report 384.
• 17. Kankara R.S., Selvan S.C., Markose V.J., Rajan B., Arockiaraj S. 2015. Estimation of long and short term shoreline changes along Andhra Pradesh coast using remote sensing and GIS techniques. Procedia Engineering, 116, 855–862.
• 18. Ko B.C., Kim H.H., Nam J.Y. 2015. Classification of potential water bodies using Landsat 8 OLI and a combination of two boosted random forest classifiers. Sensors, 15(6), 13763–13777.
• 19. Koroglu A., Ranasinghe R., Jiménez J.A., Dastgheib A. 2019. Comparison of coastal vulnerability index applications for Barcelona Province. Ocean and Coastal Management, 178, 104799.
• 20. Koulibaly C.T., Ayoade J.O. 2021. The application of GIS and remote sensing in a spatiotemporal analysis of coastline retreat in Rufisque, Senegal. Geomatics and Environmental Engineering, 15(3), 55–80.
• 21. Kuleli T., Guneroglu A., Karsli F., Dihkan M. 2011. Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey. Ocean Engineering, 38(10), 1141–1149.
• 22. Liwun M.K.L., Ismanto A., Indrayanti E., Munandar B., Siagian H. 2023. Prediction of coastline changes in Tanjung Lesung Beach, Panimbang Regancy, Pandeglang District, Banten (Case Study: 2022-2047). Buletin Oseanografi Marina, 12(2), 270–277. [in Indonesian]
• 23. Mafi-Gholami D., Baharlouii M. 2019. Monitoring long-term mangrove shoreline changes along the Northern Coasts of the Persian Gulf and the Oman Sea. Emerging Science Journal, 3(2), 88–100.
• 24. Marfai M.A. 2014. Impact of sea level rise to coastal ecology: a case study on the northern part of Java Island, Indonesia. Quaestiones Geographicae, 33(1), 107–114.
• 25. Mujabar P.S., Chandrasekar N. 2013. Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS. Arabian Journal of Geosciences, 6, 647–664.
• 26. 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 & Geotechnology, 37(1), 81–95.
• 27. Nath A., Koley B., Saraswati S., Bhatta B., Ray B.C. 2021. Shoreline change and its impact on land use pattern and vice versa: a critical analysis in and around Digha area between 2000 and 2018 using geospatial techniques. Pertanika Journal of Science and Technology, 29(1), 331–348.
• 28. Nhan N.T., Tung N.X., Anh B.T.B., Thanh N.X. 2018. Application of remote sensing, GIS and digital shoreline analysis system (DSAS) to assess the changes of the Red River Bank in the area from Son Tay to Gia Lam (Hanoi). Journal of Marine Science and Technology, 18(3), 269–277.
• 29. Passeri D.L., Hagen S.C., Medeiros S.C., Bilskie M.V., Alizad K., Wang D. 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review. Earth’s Future, 3, 159–181.
• 30. Pendleton E.A., Thieler E.R., Williams S.J. 2010. Importance of coastal change variables in determining vulnerability to sea- and lake-level change. Journal of Coastal Research, 261, 176–183.
• 31. Prieto-Amparan J.A., Villarreal-Guerrero F., Martinez-Salvador M., Manjarrez-Domínguez C., Santellano-Estrada E., Pinedo-Alvarez A. 2018. Atmospheric and radiometric correction algorithms for the multitemporal assessment of grasslands productivity. Remote Sensing, 10, 219.
• 32. Rahman A.F., Dragoni D., El-Masri B. 2011. Response of the Sundarbans coastline to sea level rise and decreased sediment flow: A remote sensing assessment. Remote Sensing of Environment, 115(12), 3121–3128.
• 33. Rumahorbo B.T., Hamuna B., Keiluhu H.J., 2020. An assessment of the coastal ecosystem services of Jayapura City, Papua Province, Indonesia. Environmental & Socio-economic Studies, 8(2), 45–53.
• 34. Rumahorbo B.T., Warpur M., Hamuna B., Tanjung R.H.R. 2022. Analysis of shoreline changes along the coastal area of Biak Island (Biak Numfor Regency, Indonesia) using multitemporal Landsat images. Journal of Degraded and Mining Lands Management, 10(1), 3861–3870.
• 35. Rumahorbo B.T., Warpur M., Tanjung R.H.R., Hamuna B. 2023. Spatial analysis of coastal vulnerability index to sea level rise in Biak Numfor Regency (Indonesia). Journal of Ecological Engineering, 10(1), 3861–3870.
• 36. Saad R., Gerard J.A., Gerard P. 2021. Detection of the shoreline changes using DSAS technique and remote sensing: A case study of Tyre southern Lebanon. Journal of Oceanography and Marine Research, 9(11), 1000004.
• 37. Sam S.C., 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, 585–594.
• 38. Thieler E.R., Hammar-Klose E.S. 1999. National Assessment of Coastal Vulnerability to Sea-Level Rise: Preliminary Results for the U.S. Atlantic Coast. U.S. Geological Survey Open-File Report 99-593. United States Geological Survey, Massachusetts.
• 39. Thieler E.R., Himmelstoss E.A., Zichichi J.L., Ergul A. 2009. Digital Shoreline Analysis System (DSAS) Version 4.0, An ArcGIS Extension for Calculating Shoreline Change. U.S. Geological Survey Open File Report 2008–1278. United States Geological Survey, Massachusetts.
• 40. Triatmodjo B. 2012. Teknik Pantai. Universitas Gajah Mada, Yogyakarta. [in Indonesian]
• 41.Tsai Y-L.S. 2022. Monitoring 23-year of shoreline changes of the Zengwun Estuary in Southern Taiwan using time-series Landsat data and edge detection techniques. Science of The Total Environment, 839, 156310.
• 42. Yerzhanova A.E., Kerimkhulle S.Y.E., Abdikerimova G.B., Makhanov M., Beglerova S.T., Taszhurekova Z.H.K. 2021. Atmospheric correction of Landsat-8/OLI data using the FLAASH algorithm: Obtaining information about agricultural crops. Journal of Theoretical and Applied Information Technology, 99(13), 3110–3119.
• 43. Yulianto F., Suwarsono S., Maulana T., Khomarudin M.R. 2019. The dynamics of shoreline change analysis based on the integration of remote sensing and geographic information system (GIS) techniques in Pekalongan coastal area, Central Java, Indonesia. Journal of Degraded and Mining Lands Management, 6(3), 1789–1802.
• 44. Zonkouan B.R.V., Bachri I., Beda A.H.Z., N’Guessan K.A.M. 2022. Monitoring spatial and temporal scales of shoreline changes in Lahou-Kpanda (Southern Ivory Coast) using Landsat data series (TM, ETM+ and OLI). Geomatics and Environmental Engineering, 16(1), 145–158.