Deep and machine learning methods for the (semi-)automatic extraction of sandy shoreline and erosion risk assessment basing on remote sensing data (case of Jerba island -Tunisia)

Contenu

Boussetta, Amina, Niculescu, Simona, Bengoufa, Soumia, et Zagrarni, Mohamed Fawzi. 2022. « Deep and Machine Learning Methods for the (semi-)automatic Extraction of Sandy Shoreline and Erosion Risk Assessment Basing on Remote Sensing Data (case of Jerba Island -Tunisia) ». Remote Sensing Applications: Society and Environment 32: 101084. doi:10.1016/j.rsase.2023.101084, bibliographie, consulté le 3 avril 2025, https://ibadica.org/s/bibliographie/item/48736

Titre: Deep and machine learning methods for the (semi-)automatic extraction of sandy shoreline and erosion risk assessment basing on remote sensing data (case of Jerba island -Tunisia)
Créateur: Boussetta, Amina Voir tous les contenus avec cette valeur; Niculescu, Simona Voir tous les contenus avec cette valeur; Bengoufa, Soumia Voir tous les contenus avec cette valeur; Zagrarni, Mohamed Fawzi Voir tous les contenus avec cette valeur
Sujet: Géologie -- Djerba Voir tous les contenus avec cette valeur
Résumé: 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.
Est une partie de: Remote Sensing Applications: Society and Environment Voir tous les contenus avec cette valeur
volume: 32
pages: 101084
Date: 2023
Langue: eng
doi: 10.1016/j.rsase.2023.101084
issn: 2352-9385
uri: https://www.sciencedirect.com/science/article/pii/S2352938523001660

Position : 26072 (13 vues)