A review and meta-analysis of remote sensing data, GIS methods, materials
and indices used for monitoring the coastline evolution over the last twenty
years
Dionysios Apostolopoulos and Konstantinos Nikolakopoulos
Department of Geology, University of Patras, Patras, Greece
ABSTRACT
The current review study focuses on a statistical analysis (Meta-analysis) of the most common
methods, materials, software, and indices used by researchers over the last twenty years to
evaluate and quantify the shoreline evolution. Furthermore, this review targets to highlight
some critical points through studied literature such as a) the low rate of high-resolution
satellite images usage in a subject where the accuracy is prerequisite, b) the effort to derive
information from Landsat images in order to take advantage of the 50-years archive and the
freely availability c) the impulse of the UAV during the last 5 years as an alternative low cost but
high accurate source of data d) the fact that only 50% of the coastal erosion studies are funded.
One hundred thirty-eight papers and articles have been analyzed in detail by the authors and
all the key points (methods, materials, software, and indices) were sorted for further statistical
analysis. This study is not intended to criticize neither the methods nor the results being
mentioned in the previous studies but to give the opportunity to the researchers (especially the
new ones) to have an overall view of the subject for the last 20 years with just a glimpse.
ARTICLE HISTORY
Received 27 September 2020
Revised 27 February 2021
Accepted 12 March 2021
KEYWORDS
Meta-analysis; gis; remote
sensing; coastal erosion;
review
Introduction
About the shoreline
There are plenty of shoreline definitions along the time
among researchers (Alesheikh et al., 2004; E Bird, 2008;
Coastal Engineering Research Center (CERC), 1984;
Niedermeier et al., 2005; Pajak & Leatherman, 2002). A
conventional definition for the term “shoreline” is the
physical border between land and sea (Dolan et al., 1980).
The term “shoreline” is considered synonymous with the
term “coastline” and there is no difference in this review
study. During the last decades, the remote sensing science
in combination with several Geographic Information
System (GIS) applications has taken a various place in
geo-surveys such as the coastline evolution analysis.
Many studies tried to analyze and find the best approach
to this subject based on several algorithms and computations (table 1–4). Automatic classification algorithms
were also used and applied on aerial photos (A.C.
Teodoro et al., 2009) or on IKONOS-2 satellite data (A.
Teodoro et al., 2011) to identify coastal features and
coastal patterns. The coastline is a dynamic environment
where human structures and activities took place near or
along it. So, it is important to have a reliable tool to
enumerate, estimate and even, if so, to predict the shoreline movement landward or seaward. For this reason,
many numeric methods, factors and algorithms have
been established and applied the last two decades on a
variety of data types such as aerial photos, satellite images,
or Optical and RADAR data, Topographic maps and
recently Unmanned Aerial Vehicle (UAV) images. The
structure of the coastal environment and the pressures it
is under are the first things that someone should understand in order to study its development. As coastline is a
dynamic environment and it’s profile can very easily
change from coast to coast continually, there is not one
and only indicator which can match to all types of coasts.
Indicators that measure the evolution and change of the
coastline should be accurate and describe the timeless
evolution of the area as well as the natural processes
that have taken place. In figure 1 the allocation of diverse
studies areas all over the world is presented. At these
areas, shoreline evolution mapping was performed
using remote sensing data/technologies as described in
138 studies published in the last 20 years (table 1–4).
Material and datasets
Materials
This research is based on a methodological recoding of
several critical points that have been used by several
authors (Table 1–4) in the last 20 years (2000–2019)
for shoreline monitoring such as a) periods of interest,
b) type of data that have been acquired c) shoreline
spatial extraction methods, indicators, techniques and
models, d) software and e) erosion/accretion estimation methods and algorithms.
CONTACT Dionysios Apostolopoulos
knikolakop@upatras.gr
Department of Geology, University of Patras, Patras 26504, Greece
EUROPEAN JOURNAL OF REMOTE SENSING
2021, VOL. 54, NO. 1, 240–265
https://doi.org/10.1080/22797254.2021.1904293
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
and indices used for monitoring the coastline evolution over the last twenty
years
Dionysios Apostolopoulos and Konstantinos Nikolakopoulos
Department of Geology, University of Patras, Patras, Greece
ABSTRACT
The current review study focuses on a statistical analysis (Meta-analysis) of the most common
methods, materials, software, and indices used by researchers over the last twenty years to
evaluate and quantify the shoreline evolution. Furthermore, this review targets to highlight
some critical points through studied literature such as a) the low rate of high-resolution
satellite images usage in a subject where the accuracy is prerequisite, b) the effort to derive
information from Landsat images in order to take advantage of the 50-years archive and the
freely availability c) the impulse of the UAV during the last 5 years as an alternative low cost but
high accurate source of data d) the fact that only 50% of the coastal erosion studies are funded.
One hundred thirty-eight papers and articles have been analyzed in detail by the authors and
all the key points (methods, materials, software, and indices) were sorted for further statistical
analysis. This study is not intended to criticize neither the methods nor the results being
mentioned in the previous studies but to give the opportunity to the researchers (especially the
new ones) to have an overall view of the subject for the last 20 years with just a glimpse.
ARTICLE HISTORY
Received 27 September 2020
Revised 27 February 2021
Accepted 12 March 2021
KEYWORDS
Meta-analysis; gis; remote
sensing; coastal erosion;
review
Introduction
About the shoreline
There are plenty of shoreline definitions along the time
among researchers (Alesheikh et al., 2004; E Bird, 2008;
Coastal Engineering Research Center (CERC), 1984;
Niedermeier et al., 2005; Pajak & Leatherman, 2002). A
conventional definition for the term “shoreline” is the
physical border between land and sea (Dolan et al., 1980).
The term “shoreline” is considered synonymous with the
term “coastline” and there is no difference in this review
study. During the last decades, the remote sensing science
in combination with several Geographic Information
System (GIS) applications has taken a various place in
geo-surveys such as the coastline evolution analysis.
Many studies tried to analyze and find the best approach
to this subject based on several algorithms and computations (table 1–4). Automatic classification algorithms
were also used and applied on aerial photos (A.C.
Teodoro et al., 2009) or on IKONOS-2 satellite data (A.
Teodoro et al., 2011) to identify coastal features and
coastal patterns. The coastline is a dynamic environment
where human structures and activities took place near or
along it. So, it is important to have a reliable tool to
enumerate, estimate and even, if so, to predict the shoreline movement landward or seaward. For this reason,
many numeric methods, factors and algorithms have
been established and applied the last two decades on a
variety of data types such as aerial photos, satellite images,
or Optical and RADAR data, Topographic maps and
recently Unmanned Aerial Vehicle (UAV) images. The
structure of the coastal environment and the pressures it
is under are the first things that someone should understand in order to study its development. As coastline is a
dynamic environment and it’s profile can very easily
change from coast to coast continually, there is not one
and only indicator which can match to all types of coasts.
Indicators that measure the evolution and change of the
coastline should be accurate and describe the timeless
evolution of the area as well as the natural processes
that have taken place. In figure 1 the allocation of diverse
studies areas all over the world is presented. At these
areas, shoreline evolution mapping was performed
using remote sensing data/technologies as described in
138 studies published in the last 20 years (table 1–4).
Material and datasets
Materials
This research is based on a methodological recoding of
several critical points that have been used by several
authors (Table 1–4) in the last 20 years (2000–2019)
for shoreline monitoring such as a) periods of interest,
b) type of data that have been acquired c) shoreline
spatial extraction methods, indicators, techniques and
models, d) software and e) erosion/accretion estimation methods and algorithms.
CONTACT Dionysios Apostolopoulos
knikolakop@upatras.gr
Department of Geology, University of Patras, Patras 26504, Greece
EUROPEAN JOURNAL OF REMOTE SENSING
2021, VOL. 54, NO. 1, 240–265
https://doi.org/10.1080/22797254.2021.1904293
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.