Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30826
Title: Identification and forecasting of coastal erosion using aerial and UAV images
Authors: Themistocleous, Kyriacos 
Major Field of Science: Engineering and Technology
Field Category: ENGINEERING AND TECHNOLOGY
Keywords: Unmanned aerial vehicles;Sand;Photogrammetry;Cameras;Image segmentation;Image processing;Point clouds;RGB color model;Coastal modeling
Issue Date: 21-Sep-2023
Start page: 1
End page: 8
Project: EXCELSIOR: ERATOSTHENES Centre of Excellence for Earth Surveillance and Space-Based Monitoring of the Environment : Teaming Phase1 GA 763643 
Conference: Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 2023, Ayia Napa, Cyprus 
Abstract: This study seeks to establish a methodology in order to determine the rate of coastal erosion using temporal aerial and UAV images. Part of the methodology focuses on forecasting coastal erosion and mapping Posidonia oceanica. Initial results indicate that there may be a correlation between coastal erosion and other related dynamics, such as the presence of Posidonia oceanica. UAV images acquired using UAVS in the Spyros Beach area near Larnaca, Cyprus were compared with aerial photos provided by the Lands and Surveys Department of the Government of Cyprus in order to estimate coastal erosion. In this study, the results indicate that, instead of coastline erosion, the coastline is actually expanding at a constant rate over the forecasted period. The beach nourishment observed may be related to the reduction of Posidonia oceanica. This reduction of the Posidonia oceanica forms a natural breaker between the shoreline and the sea, leading to a reduction of wave energy which thereby results in an enhanced accumulation of sand at the beach.
URI: https://hdl.handle.net/20.500.14279/30826
DOI: https://doi.org/10.1117/12.2683057
Rights: CC0 1.0 Universal
Type: Conference Papers
Affiliation : ERATOSTHENES Centre of Excellence 
Cyprus University of Technology 
Appears in Collections:EXCELSIOR H2020 Teaming Project Publications

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