Satellite image processing for the coarse-scale investigation of sandy coastal areas

Latella, Melissa; Luijendijk, Arjen; Moreno-Rodenas, Antonio M.; Camporeale, Carlo

doi:10.3390/rs13224613

Satellite image processing for the coarse-scale investigation of sandy coastal areas

Title

Satellite image processing for the coarse-scale investigation of sandy coastal areas

Author

Latella, Melissa (Politecnico di Torino)
Luijendijk, Arjen (TU Delft Coastal Engineering; Deltares)
Moreno-Rodenas, Antonio M. (Deltares)
Camporeale, Carlo (Politecnico di Torino)

Date

2021

Abstract

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 ecomor-phological 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.

Subject

Beach monitoring
Coastal vegetation
Geomorphology
Google Earth Engine
Satellite images
Shoreline detection

To reference this document use:

http://resolver.tudelft.nl/uuid:c0d56610-2c26-4700-88ba-d56f96a2619d

DOI

https://doi.org/10.3390/rs13224613

ISSN

2072-4292

Source

Remote Sensing, 13 (22)

Part of collection

Institutional Repository