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Coastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) / Yawo Konko in Advances in Remote Sensing, vol 9 n° 2 (June 2020)  

Public [article]  inAdvances in Remote Sensing > vol 9 n° 2 (June 2020) . - pp 85 - 100 Titre : Coastline change modelling induced by climate change using geospatial techniques in Togo (West Africa) Type de document : Article/Communication Auteurs : Yawo Konko, Auteur ; Appollonia Okhimambe, Auteur ; Pouwèréou Nimon, Auteur ; Jerry Asaana, Auteur ; Jean-Paul Rudant , Auteur ; Kouami Kokou, Auteur Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 85 - 100 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] classification non dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données multisources [Termes IGN] érosion côtière [Termes IGN] image Landsat-ETM+ [Termes IGN] image Sentinel-MSI [Termes IGN] niveau de la mer [Termes IGN] Normalized Difference Water Index [Termes IGN] outil d'aide à la décision [Termes IGN] régression linéaire [Termes IGN] série temporelle [Termes IGN] surveillance du littoral [Termes IGN] Togo [Termes IGN] trait de côte Résumé : (auteur) Climate change is a major concern of humanity. One of the consequences of climate change is global warming causing melting glaciers, rising sea levels and shoreline regression. In Togo, the regression of shoreline leads to coastal erosion with significant damage on socio-economic infrastructures and human habitats. This research, basing on geospatial techniques, focuses on coastal erosion monitoring from 1988 to 2018 in Togo. It is interested in the extraction of shoreline and in the analysis of change. Various satellite images indexes have been developed for shoreline extraction but the major scientific problem concerns the precision of the different classification algorithms methods used for the extraction of the shoreline from these water index. This study used NDWI index from multisource satellite images. It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline on NDWI index. The topographic morphology such as linear and non-linear coastal surfaces have been considered. The estimation of the rates of change of the shoreline was performed using the statistical linear regression method (LRR). The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods. For the kinematics of the shoreline, the southwest of the Togolese coast has an average erosion rate ranging from 2.49 to 5.07 m per year. The results obtained will serve as decision-making support tools for the design and implementation of appropriate adaptations plans to avoid the immersion of the asphalt road by sea, displacement of population and disturbance of human habitats. Numéro de notice : A2020-795 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.4236/ars.2020.92005 Date de publication en ligne : 08/06/2020 En ligne : https://doi.org/10.4236/ars.2020.92005 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96622 [article]