In Africa, several new seaport developments are being considered. In sedimentary environments, such port developments can have adverse impacts on the evolution of adjacent coastlines. To learn from past port engineering practice, we created a unique database containing the coastline evolution and characteristics of 130 existing African seaports. Whereas the systematic mapping of coastal impacts was previously hampered by data availability, innovative automated satellite image processing techniques have enabled us to intercompare ports at an unprecedented continental scale. We found large geographical differences with respect to the beach evolution. The total detected changes in the beach area between 1984 and 2018 totaled 44 km2, of which ca. 23 km2 is accretion and ca. 21 km2 is erosion. The top 10% “hotspot” ports account for more than 65% of these changes. These hotspots exhibit common characteristics, namely: they are located on open coastlines, have large alongshore sediment transport potential, and have large cross-shore breakwaters. Although these driving characteristics are well established in coastal engineering theory, our results indicate that the beaches adjacent to the existing seaports have been and remain seriously affected by these drivers. Our results can be used to inform beach maintenance strategies for existing seaports and to support planners and engineers to minimize long-term coastal impacts of port expansions and new port developments in Africa in the future.
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Measured trends and variability in shoreline position are used by coastal managers, scientists and engineers to understand and monitor coastal systems. This paper presents a new and generic method for automated shoreline detection from the largely unexplored collection of publicly available satellite imagery. The position of the obtained Satellite Derived Shoreline (SDS) is tested for accuracy for 143 images against high resolution in-situ data along a coastal stretch near the Sand Motor, a well-documented mega-scale nourishment along the Dutch coast. In this assessment, we quantify the effects of potential inaccuracy drivers such as the presence of clouds and wave-induced foam. The overall aim of this study is to verify whether the SDS is suitable to study structural coastline trends for coastal engineering practice.

In the ideal case of a cloud free satellite image without the presence of waves, with limited morphological changes between the time of image acquisition and the date of the in-situ measurement, the accuracy of the SDS is with subpixel precision (smaller than 10–30 m, depending on the satellite mission) and depends on intertidal beach slope and image pixel resolution. For the highest resolution images we find an average offset of 1 m between the SDS position and the in-situ shoreline in the considered domain. The accuracy deteriorates in the presence of clouds and/or waves on the image, satellite sensor corrections and georeferencing errors. The case study showed that especially the presence of clouds can lead to a considerable seaward offset of the SDS of multiple pixels (e.g. order 200 m). Wave-induced foam results in seaward offsets in the order of 40 m.

These effects can largely be overcome by creating composite images, which results in a continuous dataset with subpixel precision (10–30 m, depending on the satellite mission). This implies that structural trends can be detected for coastlines that have changed with at least the pixel resolution within the considered timespan.

Given the accuracy of composite images along the Sand Motor in combination with the worldwide availability of public satellite imagery covering the last decades, this technique can potentially be applied at other locations with large (structural) coastline trends.
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