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

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 unde...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Melissa Latella, Arjen Luijendijk, Antonio M. Moreno-Rodenas, Carlo Camporeale
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Google Earth Engine satellite images shoreline detection geomorphology coastal vegetation beach monitoring Science Q
Acceso en línea:	https://doaj.org/article/3c570a78991b443bbbd86fe561ec7d71
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3c570a78991b443bbbd86fe561ec7d71
record_format	dspace
spelling	oai:doaj.org-article:3c570a78991b443bbbd86fe561ec7d712021-11-25T18:54:44ZSatellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas10.3390/rs132246132072-4292https://doaj.org/article/3c570a78991b443bbbd86fe561ec7d712021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4613https://doaj.org/toc/2072-4292In 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.Melissa LatellaArjen LuijendijkAntonio M. Moreno-RodenasCarlo CamporealeMDPI AGarticleGoogle Earth Enginesatellite imagesshoreline detectiongeomorphologycoastal vegetationbeach monitoringScienceQENRemote Sensing, Vol 13, Iss 4613, p 4613 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Google Earth Engine satellite images shoreline detection geomorphology coastal vegetation beach monitoring Science Q
spellingShingle	Google Earth Engine satellite images shoreline detection geomorphology coastal vegetation beach monitoring Science Q Melissa Latella Arjen Luijendijk Antonio M. Moreno-Rodenas Carlo Camporeale Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas
description	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.
format	article
author	Melissa Latella Arjen Luijendijk Antonio M. Moreno-Rodenas Carlo Camporeale
author_facet	Melissa Latella Arjen Luijendijk Antonio M. Moreno-Rodenas Carlo Camporeale
author_sort	Melissa Latella
title	Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas
title_short	Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas
title_full	Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas
title_fullStr	Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas
title_full_unstemmed	Satellite Image Processing for the Coarse-Scale Investigation of Sandy Coastal Areas
title_sort	satellite image processing for the coarse-scale investigation of sandy coastal areas
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3c570a78991b443bbbd86fe561ec7d71
work_keys_str_mv	AT melissalatella satelliteimageprocessingforthecoarsescaleinvestigationofsandycoastalareas AT arjenluijendijk satelliteimageprocessingforthecoarsescaleinvestigationofsandycoastalareas AT antoniommorenorodenas satelliteimageprocessingforthecoarsescaleinvestigationofsandycoastalareas AT carlocamporeale satelliteimageprocessingforthecoarsescaleinvestigationofsandycoastalareas
_version_	1718410517979070464

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

Ejemplares similares