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Les côtes basses sont connues pour leur grande vulnérabilité à l’élévation récente du niveau marin et les risques associés. Cette vulnérabilité est souvent accentuée par des enjeux socio- économiques. L’île de Jerba, appartenant à la façade orientale de la Tunisie, fait partie de telles côtes. Trois principaux risques la menacent : l’érosion marine, la submersion marine et la salinisation des terres. La vulnérabilité de l’île à de tels risques a déjà fait l’objet de nombreuses études (APAL, 2012 ; Oueslati et al., 2015 ; Oueslati, 2018). Mais les travaux ont porté surtout sur les côtes sableuses de la façade orientale et ont souvent été ponctuels dans le temps et dans l’espace et se sont intéressés avant tout à la question de l’érosion marine. Ce travail est une tentative de cartographie et d’analyse des risques menaçant l’île dans une conjoncture d’élévation du niveau marin en mettant en exergue la fragilité du cadre naturel. Il aboutit, en s’appuyant sur la méthode multicritères et des observations directes du terrain, à une carte de synthèse de la vulnérabilité de l’ensemble des rivages de l’île.
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Against the backdrop of the environmental crisis, the socio-economic, ecological and cultural importance of the coastal zone calls for greater awareness of how coastal resources function, evolve, are managed and enhanced. This study aims to develop a high-performance (semi-)automatic coastal monitoring method based on Landsat-5 and Sentinel-2 multispectral satellite images for spatiotemporal analysis of shoreline changes and erosion risk assessment along Jerba Island (Tunisia) using remote sensing data and geospatial tools. A comparative study between the band ratioing (BR) method and the pixel-based image analysis (PBIA) and object-based image analysis (OBIA) methods has led to the development of machine learning (ML), random forest (RF), deep learning (DL) and convolutional neural network (CNN) algorithms. Using these classification methods, 15 different shorelines were successively detected in 1989, 2015 and 2023 and then compared with a digitized reference shoreline from the Landsat-5 and Sentinel-2 images. Following a quantitative evaluation, the accuracy of the classification model shows that the combined CNN-OBIA approach provided the least accurate results, with an overall accuracy (OA) index of 67%, while the OBIA-RF classification method provided the most accurate results (OA of 95%). This comparative study identified an accurate and improved extraction method for quantifying changes in the position of the shoreline on the east coast of Jerba Island, enabling managers to make better decisions on coastal protection and adaptation to climate change.
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Zenith Energy Ltd., an international independent oil & gas company with production, exploration, and development assets in Africa and Italy has announced that it is preparing to begin an oil well (Robbana ...
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نفت اليوم الأحد 06 ديسمبر 2020 وداد بوشماوي، الرئيسة السابقة […]
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The present study aims to locate and rank suitable sites for Soil Aquifer Treatment (SAT) in Jerba Island (South Tunisia) by integrating a single-objective AHP method into a GIS model. The methodology represents a first step in SAT basin construction projects before field investigations. It considers several criteria related to technical, environmental and cost aspects. The result showed that a large area, covering 1489 ha, is suitable for SAT, which exceeds the required area to infiltrate the total treated wastewater produced by all the island wastewater plants. Being situated inside agricultural areas and near coastlines, potential sites for SAT could increase groundwater availability for irrigation and also mitigate existing marine intrusion problem facing Jerba aquifer. The best sites are found closer to Aghir plant than the rest of island WWTPs.
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