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

Apostolopoulos, Dionysios N.; Nikolakopoulos, Konstantinos

doi:10.1117/12.2635727

Cited by 1 publication

(1 citation statement)

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“…The Linear Regression statistical rates (LRR) provided by the Digital Shoreline Analysis System (DSAS) [13,14] tool, were computed and compared utilizing the R-squared, the Root Mean Square Error (RMSE), and the Mean Annual Error (MAE) coefficient methods [15,16] . These methods and their equations have been already described in detail in previous studies [17,18] . The LRR (m/yr) is a statistic index expressing the shoreline movement rate (m/yr) along each transect line and includes all the available data, regardless of the accuracy they have [19] .…”

Section: Methodsmentioning

confidence: 99%

Quantitative comparison of temporal changes in coastline utilizing Google Earth, Landsat and Sentinel-2 historical time series case study: the Mytikas coast, Aitoloakarnania Prefecture, Greece

Verrou,

Apostolopoulos,

Nikolakopoulos

2023

Earth Resources and Environmental Remote Sensing/Gis Applications XIV

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Google Earth (GE) is a useful, free of charge platform which provide images from the Earth’s surface during time. The present study investigates the usage of the GE integrated timeline module, to assess the shoreline temporal displacement. The results were compared to the respective results derived from Landsat and Sentinel-2 images. The littoral area of the bay of Mytika situated in the Prefecture of Aitoloakarnania’s, Greece, has been used as test site. Past shoreline positions for the 2003-2022 period have been demarcated, through on-screen digitizing method, from the GE images in a Geographic Information System (G.I.S.) environment, using the wetted boundaries that where visible on the images. Moreover, automatic indices such as NDWI, NDVI and MNDWI were used to delineate the land from the water from the satellite images respectively. The shoreline evolution was estimated based on the cross-shore transects method and compared for each dataset via the Linear Regression Rate (LRR). In addition, the future shoreline position (forecasted shoreline), in ten and 20 years from 2022, was estimated using the integrated forecasting function of the fifth version of the Digital Shoreline Analysis System (DSAS) tool of the ArcGIS software. The mean value of declination among the future shorelines emerged, were statistically compared via the Shoreline Change Envelope (SCE) rates. The satellite images have been orthorectified and georeferenced to the Hellenic Geodetic Reference System of 1987 (Greek Grid) using the Leica Photogrammetry Suite (LPS), while perpendicular to the coast lines (transects) were created every 30 meters spanning along the coastline and used for the measurements. The results revealed that the NDVI predicted shoreline based on the Sentinel-2 images, yielded the lower value of declination from the GE shores, for both time periods (ten - and 20- year), ranging from 4.45 m to 7.37 m respectively, while the Landsat datasets emerged high rates of declination.

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