Analysis of Shoreline Changes in Ikoli River in Niger Delta Region Yenagoa, Bayelsa State Using Digital Shoreline Analysis System (DSAS)

Egai Ayibawari Obiene (Department of Geology, Niger Delta University, Wilberforce Island, Bayelsa State, Nigeria)
Eteh Desmond Rowland (Department of Geology, Niger Delta University, Wilberforce Island, Bayelsa State, Nigeria)
Inko-Tariah Ibiso Michael (Department of Geography and Environmental Management, Rivers State University, Rivers State, Nigeria)

Article ID: 4197


The use of Digital Shoreline Analysis System was used to determine shoreline changes in Ikoli River, Yenagoa, Bayelsa State. Shoreline data were extracted from satellite imagery over thirty years (1991-2021). The basis of this study is to use Digital Shoreline Analysis System to determine erosion and accretion areas. The result reveals that the average erosion rate in the study area is 1.16 m/year and the accretion rate is 1.62 m/year along the Ikoli River in Ogbogoro Community in Yenagoa, Bayelsa State. The mean shoreline length is 5.24 km with a baseline length of 5.2 km and the area is classified into four zones to delineate properly area of erosion and accretion based on the five class of Linear regression rate, endpoint rate and weighted linear rate of which zone I contain very high erosion and high erosion with an area of landmass 255449.93 m2 of 38%, zone II contain moderate accretion, very high accretion and high accretion with a land area of 1666816.46 m2 with 24%, zone III has very high erosion and high erosion with an area of landmass 241610.85 m2 of 34 % and zone IV contain moderate accretion and high accretion with land area 30888.08 m2 with 4%. Out of the four zones, zone I and II were found to be eroding with 72% and zone II and IV contain accretion with 28%. The result shows that 44% of the area have been eroded. Therefore, coastal engineers, planners, and shoreline zone management authorities can use DSAS to create more appropriate management plans and regulations for coastal zones and other coastal parts of the state with similar geographic features.


Satellite imagery; Erosion; Accretion Yenagoa; Linear regression rate; Endpoint rate; Weighted linear rate; Digital Shoreline Analysis System

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[1] Armenio, E., 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. DOI:

[2] Mujabar, P.K., 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, 647-666. DOI:

[3] Mentaschi, L., Vousdoukas, M.I., Pekel, J.F., Voukouvalas, E., Feyen, L., 2018. Global long-term observations of coastal erosion and accretion. Scientific Reports. 8(1), 12876.

[4] 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.

[5] Zhang, Y., Xie, J., Liu, L., 2011. Investigating sea-level change and its impact on Hong Kong’s coastal environment. Annals of GIS. 17(2), 105-112. DOI: 19475683.2011.576268.

[6] Lillesand, T.M., Kiefer, R.W., Chipman, J.W., 2015. Remote Sensing and Image Interpretation.

[7] Chandrasekar, N., Viviek, V.J., Saravanan, S., 2013. Coastal vulnerability and shoreline changes for southern tip of india-remote sensing and GIS approach. Journal of Earth Science & Climatic Change. 04(4), 1000144. DOI: 7617.1000144.

[8] Kaliraj, S., Chandrasekar, N., Magesh, N., 2013. Evaluation of coastal erosion and accretion processes along the south-west coast of Kanyakumari, Tamil Nadu using geospatial techniques. Arabian Journal of Geosciences. 8(1), 239-253. DOI:

[9] Sebat, M., Salloum, J., 2018. Estimate the rate of shoreline change using the statistical analysis technique (Epr). Business & It Viii (1). pp. 59-65. DOI:

[10] Burningham, H., French, J., 2017. Understanding coastal change using shoreline trend analysis supported by cluster-based segmentation. Geomorphology. 282, 131-149. DOI:

[11] Oyedotun, T.D.T., 2014. Shoreline Geometry: DSAS as a Tool for Historical Trend Analysis. In Geomorphological Techniques edited by Clarke, L. and Nield, J. M.. British Society for Geomorphology: London,UK. 1-12. ISSN:2047-0371.


[13] Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., Farris, A.S., 2018. Digital shoreline analysis system (DSAS), version 5.0 user guide. U.S. Geological Survey Open-File Report 2018-1179. 2331-1258 (online). pp. 110. DOI:

[14] Genz, A., Fletcher, C., Dunn, R., Frazer, L., Rooney, J., 2007. The predictive accuracy of shoreline change rate methods and alongshore beach variation on Maui, Hawaii. Journal of Coastal Research. 231, 87-105. DOI:

[15] Fletcher, C.H., Romine, B.M., Genz, A.S., Barbee, M.M., Dyer, M., Anderson, T.R., Lim, S.C., Vitousek, S., Bochicchio, C., Richmond, B.M., 2011. National Assessment of Shoreline Change: Historical Shoreline Changes in the Hawaiian Islands. Washing-ton, DC: U.S. Geological Survey Open-File Report. 2011-1051.

[16] Chand, P., Acharya, P., 2010. Shoreline change and sea level rise along coast of Bhitarkanika wildlife sanctuary, Orissa: an analytical approach of remote sensing and statistical techniques. International Journal of Geomatics and Geosciences. 1, 436-455.

[17] Thieler, E.R., Himmelstoss, E.A., Miller, T., 2005. Digital Shoreline Analysis System (DSAS) version 3.0: An ArcGIS extension for calculating shoreline change. In, Extension for ArcGIS.

[18] Prukpitikul, S., Buakaew, V., Keshdet, W., Kongprom, A., Kaewpoo, N., 2012. Shoreline Change Prediction Model for Coastal Zone Management in Thailand. Journal of Shipping and Ocean Engineering. 2, 238-243.



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