Shoreline change detection along North Sebou-Moulay Bousselham, based on remote sensing analysis

Sustainable management of the shoreline is crucial. Shoreline mapping is very essential to investigate the impact of the dynamic nature of the coastal area over a certain period. Coastal zone at the north Sebou estuary experience some issues as shoreline retreats as well as shoreline advancement in some locations. Shoreline mapping along the northern area of Sebou estuary was implemented by applying remote sensing technology to detect and analyze shoreline changes.Multi-temporal satellite images with different periods during the last 37 years (1984-2021) were used to detect changes as well as analyze shoreline change rates. Landsat images (Landsat 5-TM, Thematic Mapper TM, Landsat7 ETM+, Landsat 8 OLI_TIRS) were corrected from geometric and radiometric perspectives, then classified by assigning six different classes for analyzing the evolution of the coastal zone from 1984 to 2021. To detect changes in both land use and land cover, the Support Vector Machine algorithm supervised classification was executed to classify the images. As a powerful statistical analysis tool, DSAS was used to assess the rate of shoreline change by EPR technique and to anticipate the required coastal protection measures along the coastline.The classification results show that agricultural land, bare land, and sand-dune beaches have decreased by -8.26%, -5.92%, and -4.53% respectively. On the other hand, statistical analysis of DSAS shows that the northern area of Sebou estuary experienced an average accretion rate of (+0.96 m/yr) while erosion rates achieved an average of (-1.04 m/yr). A drastic erosion was noticeable at the right side of the estuary with (-3.1 m/yr). The decision matrix by EPR method has been developed. It illustrates the evolution, risk level, and proposed decision at each sector along the shorelines during the preceding three decades. The final goal of this study is to support decision-makers in implementing preventive management techniques in the most vulnerable locations. (c) 2023 Elsevier B.V. All rights reserved.