Abstract
Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images (<20 m). This study focuses on shoreline extraction and shoreline evolution using high spatial resolution satellite images in the presence of foam. A multispectral supervised classification technique is selected, namely the Support Vector Machine (SVM) and applied with three classes which are land, foam and water. The merging of water and foam classes followed by a segmentation procedure enables the separation of land and ocean pixels. The performance of the method is evaluated using a validation dataset acquired on two study areas (south and north of the bay of Sendaï—Japan). On each site, WorldView-2 multispectral images (eight bands, 2 m resolution) were acquired before and after the Fukushima tsunami generated by the Tohoku earthquake in 2011. The consideration of the foam class enables the false negative error to be reduced by a factor of three. The SVM method is also compared with four other classification methods, namely Euclidian Distance, Spectral Angle Mapper, Maximum Likelihood, and Neuronal Network. The SVM method appears to be the most efficient to determine the erosion and the accretion resulting from the tsunami, which are societal issues for littoral management purposes.
Highlights
Shorelines mark the transition between land and sea
It can be observed that for both images, the Spectral Angle Mapper (SAM) and Maximum Likelihood (ML) classification methods wrongly retrieved a significant number of foam pixels inland which were not removed by the segmentation whereas the Euclidean Distance (ED) (Figures 7 and 8), the Neural Network (NN) (Figures 7 and 8) and the Support Vector Machine (SVM) wrongly retrieved a significant number of water pixels inland which were removed by the segmentation process
The purpose of this study was to propose a method for monitoring shorelines using high spatial resolution images containing foam pixels contrary to most of the previous studies which ignore them
Summary
Shorelines mark the transition between land and sea. They are vulnerable to nearshore currents, human modification, winds and waves. Among the most serious consequences of climate change, sea-level rise threatens to significantly alter shorelines leading to erosion and coastal flooding [2]. High and medium spatial resolution satellite sensors are typically characterized by medium spectral resolution and by a revisit period longer than 1 day. Examples of this type of sensors are the OLI (Operational Land Imager))/LANDSAT instrument (30 m, 7 bands, 16 days, NASA/USGS (United States Geological Survey), [3]), the multispectral SPOT (Satellite pour l’observation de la Terre) instrument
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