Pardo-Pascual, J.E., J. Almonacid-Caballer, C. Cabezas-Rabadán, A. Fernández-Sarría, C. Armaroli, P. Ciavola, J. Montes, P.E. Souto-Ceccon, J. Palomar-Vázquez.
The definition of the shoreline position from satellite imagery is of great interest among coastal monitoring techniques. Understanding the reality mapped by the resulting shorelines and defining their accuracy is of paramount importance. The assessment described in this paper constitutes a validation of the shorelines obtained by using the novel tool SAET (Shoreline Analysis and Extraction Tool) for automatic shoreline extraction. The resulting shorelines applying the different parameters available in SAET are assessed in 9 test sites with diverse morphology and oceanographic conditions along the Atlantic European and Western Mediterranean coasts. The reference data is obtained along large coastal segments (covering up to about 240 km) from nearly coincident very high-resolution satellite images.
Different image processing levels and extraction methods have been tested, showing their key role in the accuracy of shoreline position. When defining the approximate shoreline position the Automated Water Extraction Index for images without shadows (AWEInsh) with a 0 threshold generally constitutes the best segmentation method. In turn, the employment of the mathematical morphological operations of dilation or erosion considerably improves the results in certain coastal typologies. On the contrary, the employment of atmospherically-corrected images has a smaller influence on the accuracy of the SDSs.
Results support the idea that the magnitude of the errors is strongly related to the specific coastal conditions- In general, the lowest errors appear in low-energetic microtidal sites, contrary to the energetic and mesotidal coasts with gentle slopes.
The shoreline errors range between 3.7 m and 13.5 m RMSE (root-mean-square error) among the different coastal types when selecting the most appropriate extraction parameters. The shoreline position identified with SAET shows a similar or better accuracy to that obtained by other tools.
How to Cite: Pardo-Pascual, J.E., J. Almonacid-Caballer, C. Cabezas-Rabadán, A. Fernández-Sarría, C. Armaroli, P. Ciavola, J. Montes, P.E. Souto-Ceccon, J. Palomar-Vázquez: Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET. Coastal Engineering, 2023, Vol.188, 2024, 104426, ISSN 0378-3839, https://doi.org/10.1016/j.coastaleng.2023.104426.