Furthermore, there were those researchers who
used high-resolution satellite data (Worldview-2,
Pleiades, QuickBird, IKONOS, etc.) or a combination
of them in order to have very high accuracy to their
measurements. Pantanahiran (2019) used Pleiades,
IKONOS, Quick Bird and Worldview-2 satellite
images to measure the coastal changes in Koh Kho
Khao island in Thailand after the 2004 tsunami. Mann
& Westphal (Thomas & Hildegard, 2014) used
QuickBird and WorldView-1/2 images to determine
changes in shoreline position in Papua New Guinea
islands for 70 years. Sesli et al. (Faik et al., 2008) used
Quickbird satellite image data for Monitoring the
changing position of the shoreline at Eastern coast of
Trabzon, Turkey.
In the last 20 years, a new low-cost, high-resolution
digital image acquisition tool has emerged and is being
used by researchers. These are unmanned aerial vehicles (UAV) that are equipped with high-tech instruments that can overcome some of the problems we
face with satellites and airplanes (Papakonstantinou et
al., 2016).
The coastal surveys due to UAV’s provide a lowcost method for rapid and high-accurate spatial resolution (2–5 cm) data acquisition. Drummond et al.
(Christopher et al., 2015) present an overview of the
use of UAV’s as an alternate and potentially improved
option for mapping, surveying and monitoring the
coastal zone.
Over the last 10–15 years the first surveys have been
taken place by UAV’s equipped with a variety of
expensive sensors and cameras which only few
researchers could afford. Nowadays, everyone can get
one in low cost and make his own research as well as
the home UAV’s are technologically in advanced and
can produce high-resolution data at a low cost for a
specific area.
UAV’s images for shoreline monitoring used by:
Jesús et al. (2019); Nahon et al. (Alphonse et al.,
2019) and Gonçalves et al. (Gil et al., 2019), Nunziata
et al. (Ferdinando et al., 2018); Benqing et al. (Benqing
Chen et al., 2018); Nikolakopoulos et al.
(Nikolakopoulos Konstantinos & George, 2017);
Letortu et al. (Pauline et al., 2018); Casella et al.
(2016); Turner et al. (Ian et al., 2016);
Papakonstantinou et al. (2016); Chikhradze et al.
(Nino et al., 2015); Marcaccio et al. (James et al.,
2015); Drummond et al. (Christopher et al., 2015);
Elsner et al. (Elsner Paul et al., 2015).
Chikhradze et al. (Nino et al., 2015) used
unmanned aerial vehicle (UAV) to produce 2D orthophotos and 3D digital elevation models in order to
investigate the geoecological state in North-West
Portuguese zone. Elsner et al. (Elsner Paul et al.,
2015) used unmanned aerial systems to study the
evolution in beaches consisted of gravel and sand in
England. Nikolakopoulos et al. (Nikolakopoulos
Konstantinos et al., 2017) used UAV for mapping
the coast in Rio beach in Western Greece.
Apart from the optical satellite data many researchers used Radar data (Mohammad et al., 2018;
Ferdinando et al., 2018; Vandebroek et al., 2017;
Francesca et al., 2016; Fugara et al., 2011; Hervé et
al., 2005; Cooley Paul & Barber David, 2003). Radar
scans the Earth’s surface operating at the microwave
region of the electromagnetic spectrum and its results
depended on the backscattering coefficient on the
physical properties of the observed surfaces. SAR
images, depending on their intensity and coherence,
provide information about the type of surfaces and
their dielectric properties as well as the modification of
the shape of anthropogenic and natural objects
(Ganzorig et al., 2006).
There are a lot of advantages by combining optical
and radar images as the electro-magnetic radiation
have the characteristic that an object which is invisible in passive sensor image could emerge in the image
of the active sensor and this procedure can work
inversely (Alessandro et al., 2008; Ganzorig et al.,
2006).
Radar data provides information about the shape
and structure of a surface and less about its type
(Herold et al., 2004; Marghany & Hashim, 2010a).
For this reason, radar data are suggested to be used
in cases of dealing with natural hazards or when we are
interested in data at specific times (Pradhan, 2009;
Pradhan & Shafie, 2009). Radar has the advantage
that offers potential for coastal monitoring application. It has great penetration capability as it works at
microwave frequency which is independent of weather
conditions, clouds and sunlight and it can be used day
or night. Moreover, radar offers a high spatial resolution imagery and it is an active remote sensing system
as it provides its own energy source.
Cooley and Barber (Cooley Paul & Barber David,
2003) used RADARSAT 1 and SPOT 4 images to
corelate the rock, sand, and vegetated coast characteristics after classification process in Africa where the
vegetation is particularly dense. Taha and Elbeih
(LGEd & Elbeih, 2010) fussed radar and Landsat
images to classify and discern with better accuracy
the watery from nonwatery areas regardless of the
cloud cover. Psomiadis et al. (2005) used a combination of Landsat images and a Temporal Differentiate
Image (TDI), created by three radar images (SAR.PRI/
ERS-2) in order to investigate the shoreline changes in
Sperchios River, Greece. Trebossen et al. (Hervé et al.,
2005) used radar images in a study of coastal development and monitoring coastal risks off the coast of
French Guiana. Al_Fugara et al. (2011) used
RADARSAT-1 images for monitoring coastal evolution of shoreline on Kuala Terrengganu of Malaysia.
Konko et al. (Yawo et al., 2018) made a combination of
optical Landsat (TM and ETM+) and Sentinel satellite
246
D. APOSTOLOPOULOS AND K. NIKOLAKOPOULOS
used high-resolution satellite data (Worldview-2,
Pleiades, QuickBird, IKONOS, etc.) or a combination
of them in order to have very high accuracy to their
measurements. Pantanahiran (2019) used Pleiades,
IKONOS, Quick Bird and Worldview-2 satellite
images to measure the coastal changes in Koh Kho
Khao island in Thailand after the 2004 tsunami. Mann
& Westphal (Thomas & Hildegard, 2014) used
QuickBird and WorldView-1/2 images to determine
changes in shoreline position in Papua New Guinea
islands for 70 years. Sesli et al. (Faik et al., 2008) used
Quickbird satellite image data for Monitoring the
changing position of the shoreline at Eastern coast of
Trabzon, Turkey.
In the last 20 years, a new low-cost, high-resolution
digital image acquisition tool has emerged and is being
used by researchers. These are unmanned aerial vehicles (UAV) that are equipped with high-tech instruments that can overcome some of the problems we
face with satellites and airplanes (Papakonstantinou et
al., 2016).
The coastal surveys due to UAV’s provide a lowcost method for rapid and high-accurate spatial resolution (2–5 cm) data acquisition. Drummond et al.
(Christopher et al., 2015) present an overview of the
use of UAV’s as an alternate and potentially improved
option for mapping, surveying and monitoring the
coastal zone.
Over the last 10–15 years the first surveys have been
taken place by UAV’s equipped with a variety of
expensive sensors and cameras which only few
researchers could afford. Nowadays, everyone can get
one in low cost and make his own research as well as
the home UAV’s are technologically in advanced and
can produce high-resolution data at a low cost for a
specific area.
UAV’s images for shoreline monitoring used by:
Jesús et al. (2019); Nahon et al. (Alphonse et al.,
2019) and Gonçalves et al. (Gil et al., 2019), Nunziata
et al. (Ferdinando et al., 2018); Benqing et al. (Benqing
Chen et al., 2018); Nikolakopoulos et al.
(Nikolakopoulos Konstantinos & George, 2017);
Letortu et al. (Pauline et al., 2018); Casella et al.
(2016); Turner et al. (Ian et al., 2016);
Papakonstantinou et al. (2016); Chikhradze et al.
(Nino et al., 2015); Marcaccio et al. (James et al.,
2015); Drummond et al. (Christopher et al., 2015);
Elsner et al. (Elsner Paul et al., 2015).
Chikhradze et al. (Nino et al., 2015) used
unmanned aerial vehicle (UAV) to produce 2D orthophotos and 3D digital elevation models in order to
investigate the geoecological state in North-West
Portuguese zone. Elsner et al. (Elsner Paul et al.,
2015) used unmanned aerial systems to study the
evolution in beaches consisted of gravel and sand in
England. Nikolakopoulos et al. (Nikolakopoulos
Konstantinos et al., 2017) used UAV for mapping
the coast in Rio beach in Western Greece.
Apart from the optical satellite data many researchers used Radar data (Mohammad et al., 2018;
Ferdinando et al., 2018; Vandebroek et al., 2017;
Francesca et al., 2016; Fugara et al., 2011; Hervé et
al., 2005; Cooley Paul & Barber David, 2003). Radar
scans the Earth’s surface operating at the microwave
region of the electromagnetic spectrum and its results
depended on the backscattering coefficient on the
physical properties of the observed surfaces. SAR
images, depending on their intensity and coherence,
provide information about the type of surfaces and
their dielectric properties as well as the modification of
the shape of anthropogenic and natural objects
(Ganzorig et al., 2006).
There are a lot of advantages by combining optical
and radar images as the electro-magnetic radiation
have the characteristic that an object which is invisible in passive sensor image could emerge in the image
of the active sensor and this procedure can work
inversely (Alessandro et al., 2008; Ganzorig et al.,
2006).
Radar data provides information about the shape
and structure of a surface and less about its type
(Herold et al., 2004; Marghany & Hashim, 2010a).
For this reason, radar data are suggested to be used
in cases of dealing with natural hazards or when we are
interested in data at specific times (Pradhan, 2009;
Pradhan & Shafie, 2009). Radar has the advantage
that offers potential for coastal monitoring application. It has great penetration capability as it works at
microwave frequency which is independent of weather
conditions, clouds and sunlight and it can be used day
or night. Moreover, radar offers a high spatial resolution imagery and it is an active remote sensing system
as it provides its own energy source.
Cooley and Barber (Cooley Paul & Barber David,
2003) used RADARSAT 1 and SPOT 4 images to
corelate the rock, sand, and vegetated coast characteristics after classification process in Africa where the
vegetation is particularly dense. Taha and Elbeih
(LGEd & Elbeih, 2010) fussed radar and Landsat
images to classify and discern with better accuracy
the watery from nonwatery areas regardless of the
cloud cover. Psomiadis et al. (2005) used a combination of Landsat images and a Temporal Differentiate
Image (TDI), created by three radar images (SAR.PRI/
ERS-2) in order to investigate the shoreline changes in
Sperchios River, Greece. Trebossen et al. (Hervé et al.,
2005) used radar images in a study of coastal development and monitoring coastal risks off the coast of
French Guiana. Al_Fugara et al. (2011) used
RADARSAT-1 images for monitoring coastal evolution of shoreline on Kuala Terrengganu of Malaysia.
Konko et al. (Yawo et al., 2018) made a combination of
optical Landsat (TM and ETM+) and Sentinel satellite
246
D. APOSTOLOPOULOS AND K. NIKOLAKOPOULOS