A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery

This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (<i<DDWI</i<). A new technique based on the buffer overlay meth...
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Autor*in:	Hazem Usama Abdelhady [verfasserIn] Cary David Troy [verfasserIn] Ayman Habib [verfasserIn] Raja Manish [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	shoreline detection shoreline evolution shoreline timeseries water index LiDAR

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 3, p 557
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:3, p 557

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14030557

Katalog-ID:	DOAJ046054634

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allfieldsGer	10.3390/rs14030557 doi (DE-627)DOAJ046054634 (DE-599)DOAJ9ecc9fdad9e34d93948835870c1896db DE-627 ger DE-627 rakwb eng Hazem Usama Abdelhady verfasserin aut A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (<i<DDWI</i<). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a <99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide. shoreline detection shoreline evolution shoreline timeseries water index LiDAR Science Q Cary David Troy verfasserin aut Ayman Habib verfasserin aut Raja Manish verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 3, p 557 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:3, p 557 https://doi.org/10.3390/rs14030557 kostenfrei https://doaj.org/article/9ecc9fdad9e34d93948835870c1896db kostenfrei https://www.mdpi.com/2072-4292/14/3/557 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 3, p 557
allfieldsSound	10.3390/rs14030557 doi (DE-627)DOAJ046054634 (DE-599)DOAJ9ecc9fdad9e34d93948835870c1896db DE-627 ger DE-627 rakwb eng Hazem Usama Abdelhady verfasserin aut A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (<i<DDWI</i<). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a <99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide. shoreline detection shoreline evolution shoreline timeseries water index LiDAR Science Q Cary David Troy verfasserin aut Ayman Habib verfasserin aut Raja Manish verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 3, p 557 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:3, p 557 https://doi.org/10.3390/rs14030557 kostenfrei https://doaj.org/article/9ecc9fdad9e34d93948835870c1896db kostenfrei https://www.mdpi.com/2072-4292/14/3/557 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 3, p 557
language	English
source	In Remote Sensing 14(2022), 3, p 557 volume:14 year:2022 number:3, p 557
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authorswithroles_txt_mv	Hazem Usama Abdelhady @@aut@@ Cary David Troy @@aut@@ Ayman Habib @@aut@@ Raja Manish @@aut@@
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author	Hazem Usama Abdelhady
spellingShingle	Hazem Usama Abdelhady misc shoreline detection misc shoreline evolution misc shoreline timeseries misc water index misc LiDAR misc Science misc Q A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery
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topic_title	A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery shoreline detection shoreline evolution shoreline timeseries water index LiDAR
topic	misc shoreline detection misc shoreline evolution misc shoreline timeseries misc water index misc LiDAR misc Science misc Q
topic_unstemmed	misc shoreline detection misc shoreline evolution misc shoreline timeseries misc water index misc LiDAR misc Science misc Q
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title	A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery
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title_full	A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery
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title_sort	simple, fully automated shoreline detection algorithm for high-resolution multi-spectral imagery
title_auth	A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery
abstract	This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (<i<DDWI</i<). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a <99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.
abstractGer	This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (<i<DDWI</i<). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a <99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.
abstract_unstemmed	This paper develops and validates a new fully automated procedure for shoreline delineation from high-resolution multispectral satellite images. The model is based on a new water–land index, the Direct Difference Water Index (<i<DDWI</i<). A new technique based on the buffer overlay method is also presented to determine the shoreline changes from different satellite images and obtain a time series for the shoreline changes. The shoreline detection model was applied to imagery from multiple satellites and validated to have sub-pixel accuracy using beach survey data that were collected from the Lake Michigan (USA) shoreline using a novel backpack-based LiDAR system. The model was also applied to 132 satellite images of a Lake Michigan beach over a three-year period and detected the shoreline accurately, with a <99% success rate. The model out-performed other existing shoreline detection algorithms based on different water indices and clustering techniques. The resolution shoreline position timeseries is the first satellite image-extracted dataset of its kind in terms of its high spatial and temporal resolution, and paves the road to obtaining other high-temporal-resolution datasets to refine models of beaches worldwide.
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title_short	A Simple, Fully Automated Shoreline Detection Algorithm for High-Resolution Multi-Spectral Imagery
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