Yazar "Gkiatas, Georgios" seçeneğine göre listele

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    Identification of streamside landslides with the use of unmanned aerial vehicles (UAVs) in Greece, Romania, and Turkey
    (MDPI, 2023) Yavuz, Mehmet; Koutalakis, Paschalis; Diaconu, Daniel Constantin; Gkiatas, Georgios; Zaimes, George N.; Tüfekçioğlu, Mustafa; Marinescu, Maria
    The alleviation of landslide impacts is a priority since they have the potential to cause significant economic damage as well as the loss of human life. Mitigation can be achieved effectively by using warning systems and preventive measures. The development of improved methodologies for the analysis and understanding of landslides is at the forefront of this scientific field. Identifying effective monitoring techniques (accurate, fast, and low cost) is the pursued objective. Geographic Information Systems (GISs) and remote sensing techniques are utilized in order to achieve this goal. In this study, four methodological approaches (manual landslide delineation, a segmentation process, and two mapping models, specifically object-based image analysis and pixel-based image analysis (OBIA and PBIA)) were proposed and tested with the use of Unmanned Aerial Vehicles (UAVs) and data analysis methods to showcase the state and evolution of landslides. The digital surface model (DSM)-based classification approach was also used to support the aforementioned approaches. This study focused on streamside landslides at research sites in three different countries: Greece, Romania, and Turkey. The results highlight that the areas of the OBIA-based classifications were the most similar (98%) to our control (manual) classifications for all three sites. The landslides’ perimeters at the Lefkothea and Chirlesti sites showed similar results to the OBIA-based classification (93%), as opposed to the Sirtoba site, where the perimeters of the landslides from OBIA-based classification were not well corroborated by the perimeters in the manual classification. Deposition areas that extend beyond the trees were revealed by the DSM-based classification. The results are encouraging because the methodology can be used to monitor landslide evolution with accuracy and high performance in different regions. Specifically, terrains that are difficult to access can be surveyed by UAVs because of their ability to take aerial images. The obtained results provide a framework for the unitary analysis of landslides using modern techniques and tools.
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    Identifying the sources and the contributions of inland sediment and litter pollutants to enhance the Black Sea through nature-based solutions
    (Springer Nature, 2023) Zaimes, George N.; Koutalakis, Paschalis; Gkiatas, Georgios; Iakovoglou, Valasia; Marinescu, Mirela; Ristea, Oana; Ghulijanyan, Andranik; Gevorgyan, Luiza; Kuharuk, Ecaterini; Trombitsky, Ilya; Tüfekçioğlu, Mustafa; Yavuz, Mehmet; Tüfekçioğlu, Aydın; Duman, Ahmet
    The Black Sea is considered one of the most degraded regional seas. Efforts have been made to improve the conditions of the Black Sea, but little focus has been on the inland pollutants and litter, specifically from the rivers and streams that end in the Black Sea. Streams and rivers are significant sources of sediment and litter. This study aimed to identify the primary sources of sediment and litter and their contributions. This will help to suggest the optimal nature-based solutions to mitigate pollution sustainably. The study has five pilot areas in Greece, Romania, Armenia, Moldova, and Turkey. This will allow us to investigate different riverine ecosystems around the Black Sea and the implementation of different nature-based solutions. Surface and stream bank erosion methods are applied at different scales (plot and watershed) to estimate their contributions to the Black Sea. Traditional (runoff plots erosion pins) and innovative methods (laser scanning, unmanned aerial vehicles) are utilized. This way, areas with the highest erosion or litter will be targeted, and the optimal nature-based solutions based on the specific characteristics of these areas will be implemented.