Wang, C.; Yang, J.; Li, J., and Chu, J., 2020. Deriving natural coastlines using multiple satellite remote sensing images. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 296-302. Coconut Creek (Florida), ISSN 0749-0208.
The exact boundary of a coastline is difficult to define accurately, and it is even more difficult to determine from remote sensing images. One definition of coastlines is the average spring tide lines. However, establishing the position of this time-sensitive location accurately from temporally sparse remote sensing observations is challenging, especially for natural coastlines such as sandy and muddy coasts. This paper proposes a method that uses multiple remote sensing images to derive coastlines under the assumption that shorelines are invariable over short periods. First, instantaneous water edges between water and non-water regions are extracted from multiple remote sensing images. Second, the extracted water edges are clustered into two groups through application of the K-means algorithm. Next, two average water edges are calculated separately from the two groups. Then, in combination with known tidal levels (amplitudes) at the image capture times, a method for deriving coastline is presented under the assumption that the coastal slope is gentle and the water depths of the locations in the same water edges are equal. Five images acquired by the Operational Land Image sensor of Landsat 8 are used to extract a coastline with known tidal amplitudes to validate the method. Experimental results show that our proposed method could derive highly accurate coastlines from multiple remote sensing images for the regions of artificial coasts and muddy coasts (except estuary areas).