images with and Radar images in order to analyze the
coastal erosion in West Africa.
Similar to radar systems are Light Detection And
Ranging (LiDAR) instruments which are usually
mounted on an airplane platform. The main difference
is that LiDAR uses laser light pulses to scan the environment. It works on the short wavelength and so it can
detect small objects on the surface. Moreover, it has
limited usage in nighttime and cloudy weather while it
is quite an expensive technology. Horacio et al. (Jesús
et al., 2019) combined LiDAR images with historical
air photographs and images obtained by an UAV in
order to observe a coastal-valley evolution in Galicia,
Spain. Nahon et al. (Alphonse et al., 2019) used a
combined photogrammetry technique based on
ground and kinematic control points, and mobile
laser scanning to construct an accurate 3D model of
beach-to-dune transition in Cap Ferret area, SW
France. Gonçalves et al. (Gil et al., 2019) used UASs,
LiDAR, historical images and orthophotos in conjunction to construct digital surface models and estimate
the local shoreline changes for the years 2011 and 2015
in Furadouro beach located in the Northern Portugal.
Ruggiero et al. (Peter & Kaminsky George, 2003) used
LiDAR data in determining historical and modern
shoreline mapping uncertainties and implications for
conducting change analyses along the coast of southwest Washington State. Gares et al. (Gares Paul et al.,
2006) used LIDAR images to discern the beach’s limits
from dune’s and to evaluate the volumetric changes
for each area in North Carolina, USA. AlmonacidCaballer et al. (Jaime et al., 2016) used multi-data
satellite sets combined with LiDAR data to assess the
annual rate of shoreline derived from Landsat (TM,
ETM and OLI) imagery and using it as an indicator to
estimate the mid-term of beach trend in El Saler,
Spain. Burningham and French (2017) used multitemporal data based on historical map, aerial photos
and LiDAR for a period of 1881 to 2015 to determinate
a shoreline trend analysis in Suffolk coast of England.
Google Earth (GE) provides an alternative high
resolution freely available satellite imagery. These
images have been taken by different time periods and
thus are valuable to those who study the land use
evolution. But there are some limitations in using
Google Earth images as they are not able to obtain
image classification as well as original multispectral
band data is absent.
As well as the spatial resolution is very high, these
data can be used by someone to execute on-screen
digitizing of water bodies, hydrographic networks
and other geological structures in GIS environment
(Malarvizhi et al., 2016). In an older study by
Nikolakopoulos and Dimitropoulos (Nikolakopoulos
Konstantinos & George, 2017) has been evaluated the
usage of Google Earth data for mapping. Accuracy
measurements were performed through roads being
digitized from GE images and overlapped them to
those vectorized from Quickbird. Mörner and Klein
(2017), used Google Earth images to observe the beach
erosion procedure caused by human action on the
Yasawa Islands in Fiji.
Mitra et al. (2017) used GE to check the accuracy of
the shoreline detection method by using the LISS III
and LANDSAT images to discern land from water.
Martínez et al. (Carolina et al., 2018) use GE pro imagery in conjunction with aerial photographs and toposheets to delineate the shoreline evaluation from 1964 to
2017 in Valparaíso Bay, central Chile. Fatima et al.
(2018) studied the role played in the construction of a
new marina in the historical evolution of the coastline
in Tangier Bay, Morocco, Strait of Gibraltar, based on a
combination of air photos, GE images, in-situ observations and surveys. Shenbagaraj et al. (2014) used
Landsat (TM, MSS, ETM and OLI) images, topographic
maps and GE images to join a technique named “isodata classification” to estimate the shoreline movement
in India. Prasita (2015) used Landsat Thematic Mapper
(TM), GE images and topographical map to determinate the shoreline movement in Pamurbaya, Indonesia.
Paravolidakis et al. (2016) used aerial images and GE
data to apply an algorithm in order to digitize the coastline in Georgioupoli region, located at North West
Crete, Greece.
Senevirathna et al. (2018) conducted a survey using
the past and recent Quick Bird data derived from GE
to investigate the coastal zone trend in Unawatuna
beach of Sri Lanka. Ratna et al. (2018), used GE images
to check Sentinel-2, ASTER and Landsat TM imagery
accuracy, a combination of them were used to study
the shoreline changes in Indonesia.
Global Navigation Satellite Systems (GNSS) or
Global Positioning Systems (GPS) can be used to
delineate the shoreline position. Such systems are the
Real-Time Kinematic (RTK) systems that are being
used in field surveys. Despite the great technological
evolution that satellites, radar, and UAVs brought in
remote sensing field of science, topographic surveys
are still a high accurate tool that offer both acquiring
shoreline data and deriving shoreline positions.
Nevertheless, such surveys are high cost and timeconsuming and researchers tried to avoid or replace
with others equally accurate as sometimes is impossible to be used for an entire coastal system survey.
Ruggiero et al. (Peter & Kaminsky George, 2003)
used among other data, GPS-based topographic surveys to extract the shoreline position in an area of
Pacific Northwest, Washington State. Aiello et al.
(Antonello et al., 2013) used GPS surveys to quantify
and analyze the amount of coastal erosion in the Gulf
of Taranto, Italy. Vandebroek et al. (2017) used RTKGPS topographic surveys in order to validate the
shoreline position that derived from high-resolution
Terra SAR-X satellite data in Sand Motor area,
EUROPEAN JOURNAL OF REMOTE SENSING
247
coastal erosion in West Africa.
Similar to radar systems are Light Detection And
Ranging (LiDAR) instruments which are usually
mounted on an airplane platform. The main difference
is that LiDAR uses laser light pulses to scan the environment. It works on the short wavelength and so it can
detect small objects on the surface. Moreover, it has
limited usage in nighttime and cloudy weather while it
is quite an expensive technology. Horacio et al. (Jesús
et al., 2019) combined LiDAR images with historical
air photographs and images obtained by an UAV in
order to observe a coastal-valley evolution in Galicia,
Spain. Nahon et al. (Alphonse et al., 2019) used a
combined photogrammetry technique based on
ground and kinematic control points, and mobile
laser scanning to construct an accurate 3D model of
beach-to-dune transition in Cap Ferret area, SW
France. Gonçalves et al. (Gil et al., 2019) used UASs,
LiDAR, historical images and orthophotos in conjunction to construct digital surface models and estimate
the local shoreline changes for the years 2011 and 2015
in Furadouro beach located in the Northern Portugal.
Ruggiero et al. (Peter & Kaminsky George, 2003) used
LiDAR data in determining historical and modern
shoreline mapping uncertainties and implications for
conducting change analyses along the coast of southwest Washington State. Gares et al. (Gares Paul et al.,
2006) used LIDAR images to discern the beach’s limits
from dune’s and to evaluate the volumetric changes
for each area in North Carolina, USA. AlmonacidCaballer et al. (Jaime et al., 2016) used multi-data
satellite sets combined with LiDAR data to assess the
annual rate of shoreline derived from Landsat (TM,
ETM and OLI) imagery and using it as an indicator to
estimate the mid-term of beach trend in El Saler,
Spain. Burningham and French (2017) used multitemporal data based on historical map, aerial photos
and LiDAR for a period of 1881 to 2015 to determinate
a shoreline trend analysis in Suffolk coast of England.
Google Earth (GE) provides an alternative high
resolution freely available satellite imagery. These
images have been taken by different time periods and
thus are valuable to those who study the land use
evolution. But there are some limitations in using
Google Earth images as they are not able to obtain
image classification as well as original multispectral
band data is absent.
As well as the spatial resolution is very high, these
data can be used by someone to execute on-screen
digitizing of water bodies, hydrographic networks
and other geological structures in GIS environment
(Malarvizhi et al., 2016). In an older study by
Nikolakopoulos and Dimitropoulos (Nikolakopoulos
Konstantinos & George, 2017) has been evaluated the
usage of Google Earth data for mapping. Accuracy
measurements were performed through roads being
digitized from GE images and overlapped them to
those vectorized from Quickbird. Mörner and Klein
(2017), used Google Earth images to observe the beach
erosion procedure caused by human action on the
Yasawa Islands in Fiji.
Mitra et al. (2017) used GE to check the accuracy of
the shoreline detection method by using the LISS III
and LANDSAT images to discern land from water.
Martínez et al. (Carolina et al., 2018) use GE pro imagery in conjunction with aerial photographs and toposheets to delineate the shoreline evaluation from 1964 to
2017 in Valparaíso Bay, central Chile. Fatima et al.
(2018) studied the role played in the construction of a
new marina in the historical evolution of the coastline
in Tangier Bay, Morocco, Strait of Gibraltar, based on a
combination of air photos, GE images, in-situ observations and surveys. Shenbagaraj et al. (2014) used
Landsat (TM, MSS, ETM and OLI) images, topographic
maps and GE images to join a technique named “isodata classification” to estimate the shoreline movement
in India. Prasita (2015) used Landsat Thematic Mapper
(TM), GE images and topographical map to determinate the shoreline movement in Pamurbaya, Indonesia.
Paravolidakis et al. (2016) used aerial images and GE
data to apply an algorithm in order to digitize the coastline in Georgioupoli region, located at North West
Crete, Greece.
Senevirathna et al. (2018) conducted a survey using
the past and recent Quick Bird data derived from GE
to investigate the coastal zone trend in Unawatuna
beach of Sri Lanka. Ratna et al. (2018), used GE images
to check Sentinel-2, ASTER and Landsat TM imagery
accuracy, a combination of them were used to study
the shoreline changes in Indonesia.
Global Navigation Satellite Systems (GNSS) or
Global Positioning Systems (GPS) can be used to
delineate the shoreline position. Such systems are the
Real-Time Kinematic (RTK) systems that are being
used in field surveys. Despite the great technological
evolution that satellites, radar, and UAVs brought in
remote sensing field of science, topographic surveys
are still a high accurate tool that offer both acquiring
shoreline data and deriving shoreline positions.
Nevertheless, such surveys are high cost and timeconsuming and researchers tried to avoid or replace
with others equally accurate as sometimes is impossible to be used for an entire coastal system survey.
Ruggiero et al. (Peter & Kaminsky George, 2003)
used among other data, GPS-based topographic surveys to extract the shoreline position in an area of
Pacific Northwest, Washington State. Aiello et al.
(Antonello et al., 2013) used GPS surveys to quantify
and analyze the amount of coastal erosion in the Gulf
of Taranto, Italy. Vandebroek et al. (2017) used RTKGPS topographic surveys in order to validate the
shoreline position that derived from high-resolution
Terra SAR-X satellite data in Sand Motor area,
EUROPEAN JOURNAL OF REMOTE SENSING
247