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