Abstract
Climate change poses a critical global challenge, necessitating the monitoring of shorelines to understand its impact. Satellite images and Google Earth Engine (GEE) are commonly used to monitor shoreline changes with reasonable accuracy. This study aims to improve the precision and reliability of shoreline monitoring by developing an automated deterministic technique using Landsat images and GEE. The developed technique identifies pure water and land pixels by applying spectral index thresholds. In addition, it employs Monte Carlo simulation to generate multispectral reflectance values for pixels with varying water percentages. Then a linear fitting model is employed to estimate the wetness coefficient in each pixel in the image. Finally, geospatial software, in the GIS environment, is used for estimating the shoreline changes using the estimated wetness coefficient map. To assess and verify the proposed shoreline estimation technique, five regions were selected, including Egypt’s northern coast, as well as shorelines in Morocco, India, Japan and Portugal, spanning the years from 2003 to 2022. The results show an effective estimation of shoreline changes with a root mean square error of 0.56-pixel size, indicating subpixel accuracy. A notable advantage of this method is its flexibility, as it derives information directly from the image, making it suitable for a wide range of regions with different water and soil characteristics. Therefore, it can be used to offer valuable insights for monitoring shoreline changes and supporting coastal management and planning efforts. The findings of the case studies revealed that breakwaters effectively reduced erosion in coastal areas of Egypt and Portugal, whereas the coastal regions of India and Morocco remained stable. Conversely, Japan experienced a high erosion rate (− 2.83 ± 4.08 m/year) in its coastal areas due to wave height. This emphasises the importance of monitoring shoreline changes and developing effective strategies to mitigate the negative impacts of climate change on coastal areas.
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The data presented in this study are available on request from the corresponding author.
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Acknowledgements
This work was supported by Incheon National University Research Concentration Professors Grant in 2021.
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The Jupyter Notebook created for this research is available in the following GitHub repository. https://github.com/TamerElGharbawi/ShoreLineMonitoring.
Appendix 1: Global Study Regions
Appendix 1: Global Study Regions
a Morocco case study, b shoreline change, c shows the changing topology of the river mouth within 20 years
a India case region, b shoreline change, c and d zoom-in regions in b, e LRR and EPR changes of regions c and d
a Japan case region, b shoreline change, c and d zoom-in regions in b, e LRR and EPR changes of regions c and d
a Portugal case region, b shoreline change, c and d zoom-in regions in b, e LRR and EPR changes of regions c and d
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ElGharbawi, T., Kaloop, M.R., Hu, J.W. et al. Subpixel Accuracy of Shoreline Monitoring Using Developed Landsat Series and Google Earth Engine Technique. PFG (2023). https://doi.org/10.1007/s41064-023-00265-9
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DOI: https://doi.org/10.1007/s41064-023-00265-9