In the absence of a generic approach to study shoreline changes, this research focus on the development of a generic methodology to detect, measure, analyze, and predict shoreline changes to manage coastal environment. The unique strength of this approach is that it ncorporates image processing techniques, remotely sensed derived data into a GIS to analyze measure, and predict and visualize shoreline changes. It is independent from the study region or the remote sensing data. This methodology uses Speeded Up Robust Feature to detect the study regions from satellite images automatically. Also, it proposes a model of shoreline using the Canny edge detector on Normalized Difference Water Index image. To measure the changes, Digital Shoreline Analysis System extension of ArcGIS was used and the End Point Rate (EPR) and Linear Regression Rate (LRR) approaches were used on the modeled shoreline. The EPR is calculated by dividing the distance of shoreline movement by the time elapsed between the oldest and the most recent shoreline. A LRR statistic can be determined by fitting a least-squares regression line to all shoreline points for a particular transect. Three regions of the island of Djerba in Tunisia were selected for this study; Rass Errmall, El Kastil, and Aghir. Accretions as well as erosion processes were observed in the study areas between 1984 and 2009. The average of the erosion was around −6.95 m/year in Aghir. The average of erosion is around −4.09 m/year and accretion trend is around +11.7 m/year in Rass Errmall. El Kastil was under a remarkable accretion with 21.14 m/year during the same period.
ASJC Scopus subject areas