Abstract
Coastal mapping with satellite imagery is broadly used to calculate shoreline positions due to its high ecological and socioeconomic value in the context of coastal conservation and management strategies. We show the applicability of the Sentinel-1 to monitor large-scale shoreline at a country level. The present study develops a novel shoreline extraction method based on C band from SAR missions, that improves coastal ocean/land discrimination. The method considers an automated processing chain using the incorporation of GLCM-mean texture information to increase improvements in image binarization by Sauvola thresholding. Results show that the proposed method may be used for shoreline monitoring of different types of geomorphology along the Mexican coastline, thus guaranteeing its applicability in different geographic surroundings. For the six specific areas of validation, the overall agreement between binarization ranges is from 90% to 100% and Sentinel-2 images are used to evaluate the VV and VH shorelines from Sentinel-1.
Published Version
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