Identification of streamside landslides with the use of unmanned aerial vehicles (UAVs) in Greece, Romania, and Turkey

Basit Öğe Kaydı
dc.authorid0000-0002-1481-2849
dc.authorid0000-0001-6430-719X
dc.authorid0000-0001-6118-2256
dc.authorid0000-0002-9358-3326
dc.authorid0000-0002-3350-2897
dc.contributor.authorYavuz, Mehmet
dc.contributor.authorKoutalakis, Paschalis
dc.contributor.authorDiaconu, Daniel Constantin
dc.contributor.authorGkiatas, Georgios
dc.contributor.authorZaimes, George N.
dc.contributor.authorTüfekçioğlu, Mustafa
dc.contributor.authorMarinescu, Maria
dc.date.accessioned2025-07-25T07:09:42Z
dc.date.available2025-07-25T07:09:42Z
dc.date.issued2023
dc.departmentAÇÜ, Orman Fakültesi, Orman Mühendisliği Bölümü
dc.description.abstractThe 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.
dc.identifier.doi10.3390/rs15041006
dc.identifier.issn20724292
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85149199683
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/11494/5857
dc.identifier.volume15
dc.indekslendigikaynakScopus
dc.institutionauthorYavuz, Mehmet
dc.institutionauthorTüfekçioğlu, Mustafa
dc.institutionauthorid0000-0002-1481-2849
dc.institutionauthorid0000-0002-3350-2897
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofRemote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDrone
dc.subjectErosion
dc.subjectGIS
dc.subjectImage-based techniques
dc.subjectLandslide mapping
dc.subjectMudflow
dc.subjectOrthomosaic analysis
dc.subjectProtect
dc.subjectRemote sensing
dc.subjectStreams
dc.subjectStreamside
dc.titleIdentification of streamside landslides with the use of unmanned aerial vehicles (UAVs) in Greece, Romania, and Turkey
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
5857.pdf
Boyut:
19.98 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Orman Mühendisliği Bölümü Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu