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Evaluating long-term shoreline change in Dakar (Senegal) using satellite data

Ojukwu, Paul Ngozi (2021) Evaluating long-term shoreline change in Dakar (Senegal) using satellite data.

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Abstract:The rate of sea-level rise (SLR) influenced by global warming has increased the risk of coastal flooding. Long-term sea-level rise induces shoreline retreat. Shoreline monitoring is necessary for beach monitoring, identifying areas that are vulnerable to coastal erosion and flooding, and sustainable management of the coastal environment. Remote sensing offers possibilities for regional monitoring and analysis of coastal dynamics in a fast, efficient, and relatively cheap way. This study investigates the long-term shoreline change in Dakar (Senegal) for the past 20 years using satellite data. Shorelines extracted from median composite images of Landsat-7 (ETM+), Landsat-8 (OLI), Sentinel-1 (SAR), and Sentinel-2 (MSI) using Modified Normalized Difference Water Index, adaptive thresholding, and Canny Edge Detection techniques were compared. The change statistics of the extracted shorelines were analysed using the Digital Shoreline Analysis System (DSAS), a software extension in ArcGIS for calculating shoreline change statistics and making forecasts, with the Net Shoreline Movement (NSM) and End Point Rate (EPR) techniques. The DSAS was used to calculate future positions of the shoreline for the next 10 and 20 years (using the Linear Regression Rate (LRR)). Finally, the study explored the influence of sea-level rise on shoreline change and identified areas vulnerable to shoreline retreat. Modified Normalized Difference Water Index (MNDWI) was more reliable in delineating the shoreline than adaptive thresholding and Canny Edge Detection techniques. Validation of the shorelines extracted from satellite data (Landsat 7, Landsat 8, and Sentinel-2) using MNDWI revealed that their positions fell within the high and low waterline. The accuracy assessment returned an overall mean (μ) error of 20.9 m (seaward bias with Root Mean Square Error (RMSE) not exceeding 33.0 m) and -11.9 m (landward bias with RMSE not exceeding 28.6m) with respect to the high and low waterline, respectively. The evolution of the shoreline from 2000 to 2020 reveals an erosive trend with an average Net Shoreline Movement (NSM) and End Point Rate (EPR) of -71.2 m of -3.6 m/year, respectively. The negative correlation obtained between shoreline displacement and increasing sea level provides insight into the potential SLR has on the retreating shoreline. Prediction of the shoreline position in 2100 (considering the high emission scenario (RCP 8.5) of SLR projection (> 1 m by 2100)) using the linear regression model equation obtained linking shoreline displacement and sea-level reveals that the shoreline is likely to retreat by 187.2 m landward from the 2020 shoreline position.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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