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Spatiotemporal Analysis of Coastal Erosion and Accretion Patterns in the Keta Region
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Coastal erosion and accretion remain critical challenges for sustainable coastal zone management, particularly in regions facing intensified environmental changes and human interventions. With rising sea levels projected to exacerbate these challenges, this study investigates the shoreline dynamics of Ghana’s eastern coastline over a nearly four-decade period. Using automated shoreline extraction via CoastSat and change analysis with the Digital Shoreline Analysis System (DSAS), the research quantified erosion and accretion rates and revealed significant spatial variability across the study area.To facilitate a focused analysis, the coastline was divided into three zones (Zone A, Zone B, and Zone C), categorized based on geomorphological characteristics and coastal management practices. Three statistical models—Linear Regression Rate (LRR), End Point Rate (EPR), and Net Shoreline Movement (NSM)—were employed to quantify shoreline changes. Zone A exhibited a balance of erosion and accretion, with rates ranging from −10.5 m/year to +10.8 m/year (EPR) and −10.4 m/year to +12.0 m/year (LRR). Zone B showed relatively stable dynamics, with EPR values from −3.1 m/year to +4.1 m/year and LRR values from −1.6 m/year to +5.0 m/year. Zone C displayed the most pronounced variability, with erosion rates peaking at −30.5 m/year (EPR) and −28.7 m/year (LRR), alongside accretion rates up to +9.7 m/year (LRR) and +8.8 m/year (EPR). Cumulative shoreline movements (NSM) averaged 15.2 m, 20.42 m, and 33.5 m for Zones A, B, and C, respectively.This study underscores the value of remote sensing and GIS in monitoring shoreline dynamics. By automating shoreline extraction with CoastSat, human error is minimized, and reproducibility is enhanced. The findings provide actionable insights into shoreline management and highlight the potential of these techniques for broader applications in coastal monitoring. This approach can empower stakeholders to devise effective, data-driven strategies for resilience against coastal erosion and sustainable coastal management practices.
Title: Spatiotemporal Analysis of Coastal Erosion and Accretion Patterns in the Keta Region
Description:
Coastal erosion and accretion remain critical challenges for sustainable coastal zone management, particularly in regions facing intensified environmental changes and human interventions.
With rising sea levels projected to exacerbate these challenges, this study investigates the shoreline dynamics of Ghana’s eastern coastline over a nearly four-decade period.
Using automated shoreline extraction via CoastSat and change analysis with the Digital Shoreline Analysis System (DSAS), the research quantified erosion and accretion rates and revealed significant spatial variability across the study area.
To facilitate a focused analysis, the coastline was divided into three zones (Zone A, Zone B, and Zone C), categorized based on geomorphological characteristics and coastal management practices.
Three statistical models—Linear Regression Rate (LRR), End Point Rate (EPR), and Net Shoreline Movement (NSM)—were employed to quantify shoreline changes.
Zone A exhibited a balance of erosion and accretion, with rates ranging from −10.
5 m/year to +10.
8 m/year (EPR) and −10.
4 m/year to +12.
0 m/year (LRR).
Zone B showed relatively stable dynamics, with EPR values from −3.
1 m/year to +4.
1 m/year and LRR values from −1.
6 m/year to +5.
0 m/year.
Zone C displayed the most pronounced variability, with erosion rates peaking at −30.
5 m/year (EPR) and −28.
7 m/year (LRR), alongside accretion rates up to +9.
7 m/year (LRR) and +8.
8 m/year (EPR).
Cumulative shoreline movements (NSM) averaged 15.
2 m, 20.
42 m, and 33.
5 m for Zones A, B, and C, respectively.
This study underscores the value of remote sensing and GIS in monitoring shoreline dynamics.
By automating shoreline extraction with CoastSat, human error is minimized, and reproducibility is enhanced.
The findings provide actionable insights into shoreline management and highlight the potential of these techniques for broader applications in coastal monitoring.
This approach can empower stakeholders to devise effective, data-driven strategies for resilience against coastal erosion and sustainable coastal management practices.
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