Netherlands. Kwasi (Addo, 2009) used GPS surveys to
validate the coastline position derived from a map that
was used as reference in the coast around Accra,
Ghana. Martínez et al. (2018) used dual frequency
GPS to determinate the shoreline position.
Shoreline spatial extraction methods
The spatial extraction of the shoreline is the main issue
which the researchers must overtake. It is the fundamental element for further estimations such as coastline movement (erosion or accretion), coastal zone
management, natural hazards prediction, etc. Data
sources such as topographic maps, aerial-photos,
ground surveys, satellite imagery with a variety of
spatial resolution and more recently UAV’s images
have been used for this purpose. Αt the same time a
lot of software has been developed to help with this
effort. Through the literature, there are two main
methods that have been used for shoreline evolution
observation (1) In-situ measurements which could
involve topographic survey techniques along the
coast and (2) Analysis of shoreline positions through
multitemporal data sets coming from aerial and satellite images as well as unmanned aerial vehicles (UAV).
Except for the primary data collection, methods
being used for the shoreline extraction by the
researchers it is important to be qualified and quantified. The methods in general can be divided in manual
and semi-automatic as fully automatic does not exist.
Semi-automatic shoreline extraction is the process
that uses some mathematic algorithms on satellite
images to separate the land from water body. All
these calculations have been named by researchers as
indices and algorithms for water line extraction and
they are carried out through appropriate software such
as ArcMap, ENVI, ERDAS Imagine, etc. Manual
shoreline extraction is the procedure that someone
follows, in order to manually digitize the shoreline.
The uncertainty and the risk of this method is quite
high, and the accuracy depends on the users experience and the quality of the handling images.
Through the studied literature, the authors found
the usage of those two methods for the shoreline
procession and digitalization semi-automatic and
manual. In semi-automatic category belong: a) the
usage of NIR waveband to distinguish sea from the
land (Valderrama-Landeros & Flores-de-santiago,
2019; Joevivek & Saravanan Sakthivel, 2018; Sandeep
et al., 2018; Andredaki et al., 2014; Sedar et al., 2014;
Liu et al. 2013; Tuncay et al., 2011; Annibale Guariglia
et al., 2009; Brock et al., 2001), b) RGB band combinations used to discern the land from water (George et
al., 2015; Sedar et al., 2014; Xuejie & Damen, 2010;
Shui-sen Chen et al., 2005). Furthermore, c) support
vector machines (SVMs) used to improve the mapping
process (George et al., 2015; Joevivek & Saravanan
Sakthivel, 2013; Yawo et al., 2018), d) edge detection
used as a shoreline proxy (Vasilis et al., 2018;
Paravolidakis et al., 2016; Murray, 2013; Fugara et al.,
2011), e) Supervised Classification used by Shalaby
and Tateishi (Adel & Ryutaro, 2007), f)
Unsupervised Classification used by Thampanya et
al. (2006), g) Natural Breaks (Jenks) used by
Andredaki et al. (2014), h) LDA Linear Discriminant
Analysis used by Cooley and Barber (Cooley Paul &
Barber David, 2003) and i) ISODATA classification
technique used by Do et al. (2018); Mitra et al. (2017);
Sekovski et al. (Ivan et al., 2014); Shenbagaraj et al.
(2014) and Annibale et al. (Annibale Guariglia et al.,
2009).
On the other hand, in manual category belong a)
the on-screen digitizing (Pantanahiran, 2019; Asib et
al., 2018; Senevirathna et al., 2018; Carolina et al.,
2018; George et al., 2015; Prasita, 2015; Andredaki et
al., 2014; Kim et al., 2013; El-Asmar and Hereher 2011;
Tateishi 2007; Hervé et al., 2005; Fromard et al., 2004;
Aris et al., 2008; Faik et al., 2008) and b) Differential
Global Positioning System (DGPS) field measurements (Addo, 2009; Andredaki et al., 2014; Annibale
Guariglia et al., 2009; Antonello et al., 2013; Elsner
Paul et al., 2015; Ferdinando et al., 2018; Gil et al.,
2019; Da Guia Albuquerque et al., 2013; Hervé et al.,
2005; Jaime et al., 2016; Jesús et al., 2019; John & Scott,
2010; Peter & Kaminsky George, 2003; Vandebroek et
al., 2017; Vassilakis & Papadopoulou-Vrynioti, 2014).
2.1.4 A summary of indicators, techniques, and
models of shoreline movement through studied
literature
A shoreline indicator is an element that can represent the “‘true’” shoreline position. In literature there
is recorded two main categories of shoreline indicators
which have different base. The first based on the discrimination of land and water boundaries by visual
observation and the second based on the tidal datum
of the area. Worldwide in coastal evolution studies
there have been listed 45 coastline indicators by Boak
and Turner (Elizabeth & Ian, 2005) while a summary
of shoreline indicators was recently represented by
Toure et al. (2019). Gens (2010) summarize and
record some various techniques to assess the coastline
position from a variety of data while the most common models for predicting the coastal erosion and
accretion was presented by Prasad and Kumar (2014).
Indices
Many indices which have been used by the researchers
to delineate water from land, were found in the studied literature and have been recorded by the authors.
The dominant index is the Normalized Difference
Water Index (NDWI), introduced by McFeeters
(1996), which researchers seem to prefer most and it
is calculated using near-infrared (NIR) and short248
D. APOSTOLOPOULOS AND K. NIKOLAKOPOULOS
validate the coastline position derived from a map that
was used as reference in the coast around Accra,
Ghana. Martínez et al. (2018) used dual frequency
GPS to determinate the shoreline position.
Shoreline spatial extraction methods
The spatial extraction of the shoreline is the main issue
which the researchers must overtake. It is the fundamental element for further estimations such as coastline movement (erosion or accretion), coastal zone
management, natural hazards prediction, etc. Data
sources such as topographic maps, aerial-photos,
ground surveys, satellite imagery with a variety of
spatial resolution and more recently UAV’s images
have been used for this purpose. Αt the same time a
lot of software has been developed to help with this
effort. Through the literature, there are two main
methods that have been used for shoreline evolution
observation (1) In-situ measurements which could
involve topographic survey techniques along the
coast and (2) Analysis of shoreline positions through
multitemporal data sets coming from aerial and satellite images as well as unmanned aerial vehicles (UAV).
Except for the primary data collection, methods
being used for the shoreline extraction by the
researchers it is important to be qualified and quantified. The methods in general can be divided in manual
and semi-automatic as fully automatic does not exist.
Semi-automatic shoreline extraction is the process
that uses some mathematic algorithms on satellite
images to separate the land from water body. All
these calculations have been named by researchers as
indices and algorithms for water line extraction and
they are carried out through appropriate software such
as ArcMap, ENVI, ERDAS Imagine, etc. Manual
shoreline extraction is the procedure that someone
follows, in order to manually digitize the shoreline.
The uncertainty and the risk of this method is quite
high, and the accuracy depends on the users experience and the quality of the handling images.
Through the studied literature, the authors found
the usage of those two methods for the shoreline
procession and digitalization semi-automatic and
manual. In semi-automatic category belong: a) the
usage of NIR waveband to distinguish sea from the
land (Valderrama-Landeros & Flores-de-santiago,
2019; Joevivek & Saravanan Sakthivel, 2018; Sandeep
et al., 2018; Andredaki et al., 2014; Sedar et al., 2014;
Liu et al. 2013; Tuncay et al., 2011; Annibale Guariglia
et al., 2009; Brock et al., 2001), b) RGB band combinations used to discern the land from water (George et
al., 2015; Sedar et al., 2014; Xuejie & Damen, 2010;
Shui-sen Chen et al., 2005). Furthermore, c) support
vector machines (SVMs) used to improve the mapping
process (George et al., 2015; Joevivek & Saravanan
Sakthivel, 2013; Yawo et al., 2018), d) edge detection
used as a shoreline proxy (Vasilis et al., 2018;
Paravolidakis et al., 2016; Murray, 2013; Fugara et al.,
2011), e) Supervised Classification used by Shalaby
and Tateishi (Adel & Ryutaro, 2007), f)
Unsupervised Classification used by Thampanya et
al. (2006), g) Natural Breaks (Jenks) used by
Andredaki et al. (2014), h) LDA Linear Discriminant
Analysis used by Cooley and Barber (Cooley Paul &
Barber David, 2003) and i) ISODATA classification
technique used by Do et al. (2018); Mitra et al. (2017);
Sekovski et al. (Ivan et al., 2014); Shenbagaraj et al.
(2014) and Annibale et al. (Annibale Guariglia et al.,
2009).
On the other hand, in manual category belong a)
the on-screen digitizing (Pantanahiran, 2019; Asib et
al., 2018; Senevirathna et al., 2018; Carolina et al.,
2018; George et al., 2015; Prasita, 2015; Andredaki et
al., 2014; Kim et al., 2013; El-Asmar and Hereher 2011;
Tateishi 2007; Hervé et al., 2005; Fromard et al., 2004;
Aris et al., 2008; Faik et al., 2008) and b) Differential
Global Positioning System (DGPS) field measurements (Addo, 2009; Andredaki et al., 2014; Annibale
Guariglia et al., 2009; Antonello et al., 2013; Elsner
Paul et al., 2015; Ferdinando et al., 2018; Gil et al.,
2019; Da Guia Albuquerque et al., 2013; Hervé et al.,
2005; Jaime et al., 2016; Jesús et al., 2019; John & Scott,
2010; Peter & Kaminsky George, 2003; Vandebroek et
al., 2017; Vassilakis & Papadopoulou-Vrynioti, 2014).
2.1.4 A summary of indicators, techniques, and
models of shoreline movement through studied
literature
A shoreline indicator is an element that can represent the “‘true’” shoreline position. In literature there
is recorded two main categories of shoreline indicators
which have different base. The first based on the discrimination of land and water boundaries by visual
observation and the second based on the tidal datum
of the area. Worldwide in coastal evolution studies
there have been listed 45 coastline indicators by Boak
and Turner (Elizabeth & Ian, 2005) while a summary
of shoreline indicators was recently represented by
Toure et al. (2019). Gens (2010) summarize and
record some various techniques to assess the coastline
position from a variety of data while the most common models for predicting the coastal erosion and
accretion was presented by Prasad and Kumar (2014).
Indices
Many indices which have been used by the researchers
to delineate water from land, were found in the studied literature and have been recorded by the authors.
The dominant index is the Normalized Difference
Water Index (NDWI), introduced by McFeeters
(1996), which researchers seem to prefer most and it
is calculated using near-infrared (NIR) and short248
D. APOSTOLOPOULOS AND K. NIKOLAKOPOULOS