Detection of Shoreline and Land Cover Changes around Rosetta Promontory, Egypt, Based on Remote Sensing Analysis

Abstract

Rosetta Promontory, Egypt has been suffering from a continuous erosion problem. The dramatic retreatment was observed during the last century. It is basically due to the construction of Aswan High Dam in 1964, which reduced the flow and sediment discharges. In this paper, four Landsat images (two Thematic Mapper and two Enhanced Thematic Mapper) covering the period from 1984 to 2014 were used. These Landsat images were radio-metrically and geometrically corrected, and then, multi-temporal post-classification analysis was performed to detect land cover changes, extracting shoreline positions to estimate shoreline change rates of the Nile delta coast around Rosetta Promontory. This method provides a viable means for examining long-term shoreline changes. Four categories, including seawater, developed (agriculture and urban), sabkhas (salt-flat), and undeveloped areas, were selected to evaluate their temporal changes by comparing the four selected images. Supervised classification technique was used with support vector machine algorithm to detect temporal changes. The overall accuracy assessment of this method ranged from 97% to 100%. In addition, the shoreline was extracted by applying two different techniques. The first method is based on a histogram threshold of Band 5, and the other uses the combination of histogram threshold of Band 5 and two band ratios (Band 2/Band 4 and Band 2/Band 5). For land cover change detection from 1984 to 2014, it was found that the OPEN ACCESSLand 2015, 4 217 developed area that increased by 9% although the land in the study area has been contracted by 1.6% due to coastal erosion. The shoreline retreat rate has decreased more than 70% from 1984 to 2014. Nevertheless, it still suffers from significant erosion with a maximum rate of 37 m/year. In comparison to ground survey and different remote sensing techniques, the established trend of shoreline change extracted using histogram threshold was found to be closely consistent with these studies rather than combining band ratio with histogram threshold.

Historical Monitoring of Shoreline Changes in the Cua Dai Estuary, Central Vietnam Using Multi-Temporal Remote Sensing Data

“…Studies on temporal and spatial coastal shoreline changes are very important in order to establish a scientific background for disciplines such as environmental science, biology, and geography, and assist planners and policy makers in making relevant policies [1,2]. However, the shoreline is considered one of the most changeable areas in the coastal zone.…”

“…Shoreline changes has been analyzed using a variety of different methods and techniques which include field surveys, fixed monitoring stations, Geographic Information System (GIS), satellite remote sensing images, and transect lines techniques e.g., [1,[3][4][5][6][7][8][9]. Field survey trips to measure the point and transect lines are usually time-and labor-consuming [4].…”

Testing A Combination Of Hard And Soft Measures To Enhance The Stability Of Rosetta Outlet

“…The important parameters used in calibration hydrodynamic time step (300, 450 and 600 sec), Manning coefficient (0.009, 0.125, 0.025, 0.04 and 0.05), different transport formulas (Van Rijn, Land-Cirp and Watanabe), scaling factor (0.5, 0.7, 0.9, 1.0, 1.3 and 2.0) for bed load and suspended load, total adaptation length (1,5,10,20, 50 and 100), and also the effect of smoothing the bathymetric contour.…”

“…The validation of PTM was conducted through comparing its results with a fluorescent tracer study performed by Abo zed and Shereet [10,11] along Rosetta coast to predict the direction of sediment movement and to estimate the sediment dispersion at the western side of the promontory and near Rosetta outlet shown in Figure 6. The results of PTM simulation gives a good correlation with the experimental one as it gives almost the same grain size distribution.…”

Multitemporal image analysis for monitoring fuzzy shorelines

“…Several works have investigated this approach to monitor shoreline changes using different combination of band for example ratioing band 5 and band 2 (b5/b2) of Landsat TM and ETM (Ervita and Marfai, 2017;Kuleli, 2010;Sarwar and Woodroffe, 2013), and combination of band 2 and band 4 (b2/b4) and band 2 and band 5 (b2/b5) of Landsat TM (Masria et al, 2015;Ozturk et al, 2015). McFeeters (1996) proposed normalized difference water index (NDWI) to discriminate open water.…”