Using automatic computation to analyze the rate of shoreline change on the Kenitra coast, Morocco

Moussaid, Jalila; Fora, Abderrahmane Ait; Zourarah, Bendahhou; Maanan, Mehdi; Maanan, Mohamed

doi:10.1016/j.oceaneng.2015.04.044

Cited by 99 publications

(48 citation statements)

References 29 publications

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Cua Dai is one of the major estuarine areas in Central Vietnam that plays a significant role in local maritime transport, fisheries, and tourism activities. This paper presents a study that monitored the shoreline dynamics of the Cua Dai estuary over a period of 50 years by using field survey data, geographic information systems techniques, and multi-temporal satellite remote sensing images (ALOS-AVNIR2 and Landsat imageries). The assessment of shoreline changes was divided into three phases: 1964-1980, 1981-2000, and 2001-2014. The results revealed that over the last 50 years, shoreline changes dramatically occurred between 1964 and 1980. The general trends of erosion and accretion at the Cua Dai estuary show that the river mouth moved towards the south due to the erosion of shorelines in the north and the river bank in the south of the Cua Dai estuary. The study outcomes can provide essential information for planning, zoning, and sustainable development activities of the coastal zones in the context of climate change.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…1). It is a straight wave-dominated meso-tidal beach environment aligned about 30° to the NS direction (Moussaid et al, 2015). This coast delimits the Atlantic continental shelf and the subsiding Gharb plain located at the junction between the stable Meseta domain to the south and the Rifian domain to the north.…”

Section: Study Area Descriptionmentioning

confidence: 99%

“…The sandy beaches are mostly bordered by 5-20 m-high consolidated eolian dunes with the sediment consisting of medium sand rang-ing between 200 and 370 μm (Madouni, 1997;Benmohammadi et al, 2007) and a gentle foreshore slope of 1-3% (Moussaid et al, 2015). Beaches are interrupted by the Sebou tidal inlet formed by two walls that extend approximately 600 m seaward from the low tide level shoreline (Hakkou et al, 2011).…”

Section: Study Area Descriptionmentioning

confidence: 99%

Evaluating the impacts of sea-level rise on the Moroccan coast: quantifying coastal erosion and inundation in a Atlantic alluvial plain (Kenitra coastal)

Azidane¹,

Benmohammadi²,

Haddout³

et al. 2017

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As part of a broad assessment of climate change impacts in Morocco, an assessment of vulnerability and adaptation of coastal zones to sea-level rise was conducted. The Kenitra coast is socio-economically vulnerable to accelerated sea-level rise, due to its low topography and its high ecological and touristic value. In this study, the effects of future relative sea-level were evaluated for the low alluvial plain Atlantic (Kenitra coastal). This study area is potentially subject to coastal flooding due to its very low topography and because it is affected by a considerable subsidence. Using a GIS-based inundation analysis; the evolution of erosion that occurred in the study area, the potential physical vulnerability to accelerated sea-level rise was investigated, and the most vulnerable socio-economic sectors were assessed. Results indicate that 35% of the areas will be lost by flooding. In addition, the coast is progressive and regressive with an average rate at minimum (0.31 m/year) and at maximum (-2.39 m/year). Such results may help decision-makers in the implementation of preventive management strategies in the most sensitive areas.

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“…As shown in Figure 2, shorelines in the middle of study area change constantly without any apparent rule; especially after 2000, the activities of reclamation are more active than ever [54], which resulted in the regression coefficient being very small. In order to get better results, transects that did not meet the requirements were manually deleted or modified, such as: (a) the coastline of the estuary zone is not continuous, and thus we could not consistently get transects; (b) when the regression coefficient of coastline change rate was less than 0.5, it was replaced by artificial end point rates [52,55,56]. After the above-mentioned selections and corrections, 62 effective transects were retained, and the annual growth rate of coastline corresponding to the control points was obtained.…”

Section: Analysis Of Shoreline Evolutionmentioning

confidence: 99%

Simulation of Land Use Changes in a Coastal Reclaimed Area with Dynamic Shorelines

et al. 2017

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Abstract:Reclamation is capable of creating abundant land to alleviate the pressure from land shortages in China. Nevertheless, coastal reclamation can lead to severe environmental degradation and landscape fragmentation. It is quite important to monitor land use and cover change (LUCC) in coastal areas, assess coastal wetland change, and predict land use requirements. The siltation of tidal flats will result in the dynamic growth and continuous expansion of coastal areas. Therefore, the process of land change in coastal areas is different from that under the fixed terrestrial boundary condition. Cellular Automata and Multi-Agent System (CA-MAS) models are commonly used to simulate LUCC, and their advantages have been well proven under the fixed boundary condition. In this paper, we propose CA-MAS combined with a shoreline evolution forecast (CA-MAS-SEF) model to simulate the land change in coastal areas. Meanwhile, the newly increased area, because of the dynamic growth of tidal flats, is considered in the simulation process. The simulation results using the improved method are verified, and compared with observed patterns using spatial overlay. In comparison with simulation results that do not consider the expansion of tidal flats, the Kappa coefficient estimated while considering the dynamic growth of tidal flats is improved from 65.9% to 70.5%, which shows that the method presented here can be applied to simulate the LUCC in growing coastal areas.

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Using automatic computation to analyze the rate of shoreline change on the Kenitra coast, Morocco

Cited by 99 publications

References 29 publications

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

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

Evaluating the impacts of sea-level rise on the Moroccan coast: quantifying coastal erosion and inundation in a Atlantic alluvial plain (Kenitra coastal)

Simulation of Land Use Changes in a Coastal Reclaimed Area with Dynamic Shorelines

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