Shoreline change analysis and its application to prediction: A remote sensing and statistics based approach

Shoreline change analysis and prediction are important for integrated coastal zone management, and are conventionally performed by field and aerial surveys. This paper discusses an alternative cost-effective methodology involving satellite remote sensing images and statistics. Multi-date satellite images have been used to demarcate shoreline positions, from which shoreline change rates have been estimated using linear regression. Shoreline interpretation error, uncertainty in shoreline change rate, and cross-validation of the calculated past shorelines have been performed using the statistical methods, namely, Regression coefficient (R-2) and Root Mean Square Error (RMSE). This study has been carried out along 113.5 km of coast adjoining Bay of Bengal in eastern India, over the time interval 1973 to 2003. The study area has been subdivided into seven littoral cells, and transects at uniform interval have been chosen within each cell. The past and future shoreline positions have been estimated over two time periods of short and long terms in three modes, viz., transect-wise, littoral cell-wise and regionally. The result shows that 39% of transects have uncertainties in shoreline change rate estimations, which are usually nearer to cell boundaries. On the other hand, 69% of transects exhibit lower RMSE values for the short-term period, indicating better agreement between the estimated and satellite based shoreline positions. It is also found that cells dominated by natural processes have lower RMSE, when considered for long term period, while cells affected by anthropogenic interventions show better agreement for the short-term period. However, on regional considerations, there is not much difference in the RMSE values for the two periods. Geomorphological evidence corroborates the results. The present study demonstrates that combined use of satellite imagery and statistical methods can be a reliable method for shoreline related studies. (C) 2008 Elsevier B.V. All rights reserved.