Detecting shoreline change employing remote sensing images (Case study: Beris Port - east of Chabahar, Iran)

2022

J. Appl. Rem. Sens.

The application of remote sensing (RS) in the treatment of problems caused by natural phenomena, such as coastal erosion, has proven to be very effective. An attempt was made to reveal areas that are in a state of erosion along the coastline of the Prefecture of Achaia, elongated ∼185 km. The study area comprised the Gulf of Patras in the west and a part of the Corinth Gulf to the east, which are connected through the narrow and shallow Rion Straits. The system of the two Gulfs consists of a Plio-Quaternary asymmetric grabens trending northeastsouthwest (NE-SW) and west-northwest (WNW)east-southeast (ESE), respectively. The shoreline evolution is estimated for the 1945 to 2016 period with the usage of high-resolution data, using RS and geographic information system techniques. An extension of ArcGIS, the digital shoreline analysis system tool, version 5 beta, has been used for the analysis of shoreline changes, whereas the ERDAS Imagine 2014 was used for the image georeferencing and orthomosaicing process. Various statistical indices were computed including the end point rate and the linear regression rate. Although these tools did not take into concern any of the local geological parameters, the results are similar to the respective derived from conventional survey techniques. Eight sandy areas were revealed that are under erosion regime: the Araxos, Karnari, Kalamaki, Drepano, Loggos, Akoli, Diacopto, and Akrata, while the future shoreline position was estimated for each coast using the forecasting beta tool of the DSASv.5.0, after 10 and 20 years since 2016. We found that it is very likely all sites will show the same trend of change in the next 10 and 20 years, similar to those computed using the available data from 1945 to 2016. The tectonic behavior of the Gulf of Corinth seems that it mainly affects the area's erosion/ accretion regime, while changes in the areas adjacent to the Gulf of Patras are related to natural processes (waves, currents, coastal floods, extreme rainfalls, etc.) and anthropogenic interventions. Moreover, the Araxos and Karnari coasts are at 65.71% and 64.41% under moderate erosion regime with rates of change −0.62 and −1.10 m∕year, respectively. Kalamaki, Akoli, and Loggos percentages of 82.35%, 86.67%, and 86.23% are under low erosion, with rates of change −0.28, −0.21, and −0.34 m∕year, respectively. Drepano and Diacopto are under low erosion, with rates of change −0.26 and −0.16 m∕year, corresponding to 54.55% and 38.67% of the coast, respectively. Finally, Akrata is a coast where erosion prevails at 60.42% with annual rate of change −0.16 m∕year, whereas the 43.38% of the coast correspond to stable area.

Section: Methodsmentioning

confidence: 99%

Section: Shoreline Change Rates Calculationmentioning

confidence: 99%

Identifying sandy sites under erosion regime along the Prefecture of Achaia, using remote sensing techniques

Apostolopoulos

2022

J. Appl. Rem. Sens.

“…Moreover, we computed the LRR (m/yr), which is calculated using a least square regression line from all shoreline positions along each transect and which is used to observe the trend of the shoreline evolution. The inclination of the line is the linear regression rate and corresponds to the long-term rates of coastal changes [47]. The method has the potential to use more than two shorelines and thus to overlap the EPR's disadvantages [48][49][50].…”

Section: Changes In Coastal Sea Zonementioning

confidence: 99%

Estimating Quantitative Morphometric Parameters and Spatiotemporal Evolution of the Prokopos Lagoon Using Remote Sensing Techniques

Apostolopoulos

Avramidis

2022

JMSE

The Prokopos Lagoon is part of the Kotychi Strofilias National Wetlands Park, which is supervised by the Ministry of Environment, Energy and Climate Change of Greece. The lagoon is situated at the northwestern coast of the Peloponnese and is protected by the Ramsar Convention. It is an important ecosystem with ecological services providing habitats for many plants and animals and essential goods and services for humans as well. No previous relevant studies for the wider wetland area are available, and given that lagoons are important ecosystems, their diachronic evolution should be under constant monitoring. Using remote sensing techniques in Geographic Information System (GIS) environment, alterations in critical parameters could be measured and applied for the protection of the area. The present study examines the spatiotemporal changes of the water extent of the Prokopos Lagoon, estimating landscape metrics and several morphometric parameters and indices related to the geomorphological features of the lagoon for the 1945–2021 period. Moreover, the adjacent shoreline was studied for each past decade evolution from 1945 to present, and it is discussed to whether there is a relationship between shoreline changes and the lagoon. High resolution satellite images and air photos at scale 1:30,000 were used to digitize the shorelines and the polygons of the lagoon’s surface. Linear Regression Rates (LRR), Net Shoreline Movement (NSM), End Point Rate (EPR) and Shoreline Change Envelope (SCE) provided by the Digital Shoreline Analysis System (DSAS) were used to determine the changes. Finally, future shoreline positions for 2021 and 2031 are estimated, while based on statistic models, we found that in the coastal area, the erosion–accretion cycle is predicted to be completed in 2031, after almost 86 years since 1945.

“…The Linear Regression Rate (LRR-m/year) calculated using a least square regression line from all shoreline positions along each transect and it is used to observe the trend of the shoreline evolution. The slope of the line is the linear regression rate and corresponds to the long-term rates of coastal changes [23] We computed the LRR rates to estimate the shoreline movement rate for the 1945-1996 period using for the year of 1996 the shorelines derived from the High Resolution (HR), SPOT, and IRS datasets respectively, while the results were crossvalidated using the R-squared, the Root Mean Square Error (RMSE), and the Mean Annual Error (MAE) coefficient methods. The equations obtained are as follows:…”

Section: Statistical Shoreline Movement Estimationmentioning

confidence: 99%

Comparison of the IRS and SPOT satellite data for shoreline mapping: the case of Katakolo bay, Ilia Prefecture, Greece

Apostolopoulos¹,

Earth Resources and Environmental Remote Sensing/Gis Applications XIII

2022