Monitoring Tidal Inlet Dominance Shifts in a Cyclically Migrating Barrier Beach System

Borrelli, Mark; Giese, Graham S.; Keon, Theodore L.; Legare, Bryan; Smith, Theresa L.; Adams, Mark D. O.; Mague, Stephen T.

doi:10.1142/9789811204487_0167

Cited by 3 publications

(1 citation statement)

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“…Figure 8 shows an illustrative example of time evolution analysis of a coastal site supported by Landsat image processing over one of the fifty AoIs considered for the algorithm validation. The selected site comprises part of the barrier islands in front of Chatham (Massachusetts, USA), whose morphological evolution is extensively documented by scientific studies (see [40], for further information) and press releases. The oldest images in the Landsat collection, dating up to the 1980s, show a coastal barrier protecting Chatham from the Atlantic Ocean's flood.…”

Section: Multitemporal Analysismentioning

confidence: 99%

Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas

et al. 2021

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In recent years, satellite imagery has shown its potential to support the sustainable management of land, water, and natural resources. In particular, it can provide key information about the properties and behavior of sandy beaches and the surrounding vegetation, improving the ecomorphological understanding and modeling of coastal dynamics. Although satellite image processing usually demands high memory and computational resources, free online platforms such as Google Earth Engine (GEE) have recently enabled their users to leverage cloud-based tools and handle big satellite data. In this technical note, we describe an algorithm to classify the coastal land cover and retrieve relevant information from Sentinel-2 and Landsat image collections at specific times or in a multitemporal way: the extent of the beach and vegetation strips, the statistics of the grass cover, and the position of the shoreline and the vegetation–sand interface. Furthermore, we validate the algorithm through both quantitative and qualitative methods, demonstrating the goodness of the derived classification (accuracy of approximately 90%) and showing some examples about the use of the algorithm’s output to study coastal physical and ecological dynamics. Finally, we discuss the algorithm’s limitations and potentialities in light of its scaling for global analyses.

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Section: Multitemporal Analysismentioning

confidence: 99%