Changing climate is a global distress these days. Global warming is one of the men driven outcome of climate change which causes the glaciers to melt, shoreline regression and raises the level of sea. The regression of shoreline in Togo resulted in vandalization of human habitat and infrastructure. This research aims to monitor the coastal erosion utilizing the geospatial techniques in Togo from 1988 to 2020. The process of extraction and existence of change in shoreline is analyzed. Scientific problems regarding the precision of classification algorithms methods utilized for shoreline extraction using various satellite images are also considered. Thus, NDWI index derived from multisource satellite images were used in this research paper. The performance of Iso Cluster Unsupervised Classification, Otsu threshold segmentation and Sup- port Vector Machine (SVM) Supervised Classification techniques are monitored for the shoreline extraction. This study also takes into account the topographic morphology including non linear and linear coastal surfaces. The rate of change of shoreline was estimated through the statistical linear regression method (LRR). The results demonstrated that the SVM Supervised Classification method worked accurately for topographic morphology than other methods.

Author Name: Zubair Attiq

URL: View PDF

Keywords: Landsat Images, Coastal Erosion, Remote Sensing, NDWI, Shoreline, Sentinel Images, SVM

ISSN: 2618-1630

EISSN: 2909-6130

EOI/DOI:

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