Please use this identifier to cite or link to this item: http://ir.lib.seu.ac.lk/handle/123456789/6392
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNijamir, Kafoor-
dc.contributor.authorAmeer, Fowzul-
dc.contributor.authorThennakoon, Sunethra-
dc.contributor.authorHerath, Jayani-
dc.contributor.authorIyoob, Atham Lebbe-
dc.contributor.authorMohamed Zahir, Ibra Lebbe-
dc.contributor.authorSabaratnam, Sajiharan-
dc.contributor.authorFathima Jisna, Mohaitheen Vava-
dc.contributor.authorMadurapperuma, Buddhika-
dc.date.accessioned2023-01-03T07:26:55Z-
dc.date.available2023-01-03T07:26:55Z-
dc.date.issued2023-02-01-
dc.identifier.citationOcean & Coastal Management Volume 232, 2023en_US
dc.identifier.issn0964-5691-
dc.identifier.urihttp://ir.lib.seu.ac.lk/handle/123456789/6392-
dc.description.abstractThe shoreline has dynamic characteristics since it experiences several coastal processes resulting in progradation and retrogradation. The shoreline is a sensitive environment where human activities interact with the natural environment. Therefore, this study examines the spatiotemporal shoreline dynamics in the east coast of Ampara District, Sri Lanka, for the periods of 1991, 2001, 2011, and 2021. It also aims to forecast the state of 2031 and 2041 coastlines to show the intensity of shoreline changes due to erosion and accretion in the study area. The shoreline of each year was delineated using the Google Earth Pro images having freed the geometric errors. To validate the position of the shoreline, 50 random ground truth points were pinpointed with the GPS tool based on the natural and human-made permanent aspects. The extracted shorelines were inserted to the DSAS (Digital Shoreline Analysis System) tool, customized in ArcGIS 10.3. The statistical methods, such as Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR) (R2 = 0.986) were computed to show the intensity of the shoreline changes. The Kalman filter model was used to predict the future trend of shoreline movement in the study area. The highest and lowest NSM rate was calculated as −300.3 m/year to −171.4 m/year in the Oluvil area and 224.1–395 m/year in the port and closest point of the port in the southward direction respectively. EPR was recorded as 8 m/year to 13.4 m/year southward of the port and in −10.2 m/year to −5.6 m/year northward of the port. The LRR was computed northward of the port where the erosion is anomalous at a rate of −5.6 m/year to −9.2 m/year and the beach advances at a rate of 19.01 m/year, whereas the accumulation was recorded as 8.9 m/year to 15.9 m/year. In 2031, the erosion would be approximately 61.4 ha meanwhile the accretion would be 33.4 ha. In 2041, at the study area the approximate erosion would be 81.7 ha whereas the accretion would be 44.9 ha. According to the forecast, in the next two decades, the erosion and accretion rate would be anomalous unless protective measures are implemented. Therefore, it is crucial to protect the coastal habitats in Ampara with the integrated coastal zone management (ICZM) process in order to mitigate the climatic and/or anthropogenic vulnerabilities.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.subjectShorelineen_US
dc.subjectSpatiotemporalen_US
dc.subjectAmparaen_US
dc.subjectGoogle Earth Proen_US
dc.subjectNSMen_US
dc.subjectEPRen_US
dc.subjectLRRen_US
dc.subjectKalman filteren_US
dc.titleGeoinformatics application for estimating and forecasting of periodic shoreline changes in the east coast of Ampara district, Sri Lankaen_US
dc.typePreprinten_US
Appears in Collections:Research Articles

Files in This Item:
File Description SizeFormat 
Ocean & Coastal Management.pdf79.88 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.