Remote sensing-based automatic detection of shoreline position: A case study in apulia region

Anna Spinosa; Alex Ziemba; Alessandra Saponieri; Leonardo Damiani; Ghada El Serafy

doi:10.3390/jmse9060575

Remote sensing-based automatic detection of shoreline position: A case study in apulia region

Anna Spinosa, Alex Ziemba, Alessandra Saponieri, Leonardo Damiani, Ghada El Serafy

Mathematical Physics

Research output: Contribution to journal › Article › Scientific › peer-review

13 Citations (Scopus)

21 Downloads (Pure)

Abstract

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

Original language	English
Article number	575
Pages (from-to)	1-20
Number of pages	20
Journal	Journal of Marine Science and Engineering
Volume	9
Issue number	6
DOIs	https://doi.org/10.3390/jmse9060575
Publication status	Published - 2021

Keywords

Coastal monitoring
Copernicus
Remote sensing
SAR
Shoreline detection

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10.3390/jmse9060575

jmse-09-00575Final published version, 87.4 MBLicence: CC BY

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title = "Remote sensing-based automatic detection of shoreline position: A case study in apulia region",

abstract = "Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.",

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AU - Spinosa, Anna

AU - Ziemba, Alex

AU - Saponieri, Alessandra

AU - Damiani, Leonardo

AU - El Serafy, Ghada

PY - 2021

Y1 - 2021

N2 - Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

AB - Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

KW - Coastal monitoring

KW - Copernicus

KW - Remote sensing

KW - SAR

KW - Shoreline detection

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M1 - 575

ER -

Remote sensing-based automatic detection of shoreline position: A case study in apulia region

Abstract

Keywords

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