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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472
EISSN: 1735-2630
Vol. 11, No. 2, 2014, pp. 395-416
Bioline Code: st14041
Full paper language: English
Document type: Research Article
Document available free of charge

International Journal of Environment Science and Technology, Vol. 11, No. 2, 2014, pp. 395-416

 en Shoreline change rate estimation and its forecast: remote sensing, geographical information system and statistics-based approach
Deepika, B.; Avinash, K. & Jayappa, K. S.

Abstract

The present study indicates that coastal geomorphology is controlled by the natural processes and anthropogenic activities. The changes in shoreline positions of Udupi coast, western India, are investigated for a period of 98 years using multi-dated satellite images and topographic maps. The study area has been divided into four littoral cells and each cell into a number of transects at uniform intervals. Further, past shoreline positions have been demarcated and future positions are estimated for 12 and 22 years. The shoreline change rate has been estimated using statistical methods—end point rate, average of rates and linear regression—and cross-validated with correlation coefficient and root-mean-square error (RMSE) methods. Resultant changes from natural processes and human interventions have been inferred from the estimated values of the back-calculated errors. About 53 % of transects exhibit ±10 m RMSE values, indicating better agreement between the estimated and satellite-based shoreline positions, and the transects closer to the cell boundaries exhibit ≈57 % uncertainties in shoreline change rate estimations. Based on the values of correlation coefficient and RMSE, the influence of natural processes and human interventions on shoreline changes have been calculated. The cells/ transects dominated by natural processes record low RMSE values, whereas those influenced by human interventions show lower correlation coefficient and higher RMSE values. The present study manifests that the results of this study can be very useful in quantifying shoreline changes and in prediction of shoreline positions.

Keywords
Coastal management; Correlation coefficient; Human intervention; Linear regression; Littoral cell; Root-mean-square error

 
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