Determination of the Effects of Various Spectral Index Combinations on Seasonal Land Use and Land Cover (LULC) Changes Using Random Forest (RF) Classification Case Study: Southeast Marmara Region 2016-2020

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dc.contributor.authorAşci, Eda
dc.contributor.authorGenç, Levent
dc.date.accessioned2025-05-29T02:54:12Z
dc.date.available2025-05-29T02:54:12Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe effects of irregular population growth, migration mobility, and vegetation dynamics by humans can lead to changes in Land Use and Land Cover (LULC). Changes in LULC are particularly significant in coastal areas associated with industrial activities. The southeastern Marmara region, which is one of Turkey's industrial coastal areas, is also affected by the surrounding changes. The study area was selected to determine LULC change and classification accuracy using Sentinel-2 vegetation indices combinations. In the study area, the Gemlik-Bursa Northern Interchange Investments Area and TOGG (Turkey's Automobile Initiative Group) factory are located. The study area was determined by creating a 5-km buffer zone from the coast to the mainland covering Armutlu district of Yalova province and Osmangazi, Mudanya, and Gemlik districts of Bursa province. Random Forest (RF) classification technique was applied both to the original bands and to 21 new band combinations that are derived from Sentinel-2 multispectral satellite imagery for 3 seasons in 2016 and 2020. The new band combinations used for classification were created by adding the normalized vegetation indices, the original bands and the bands obtained from the simple ratio formula. In 2016, the highest accuracy results for the winter, spring, and summer seasons were observed for the OI12 (82.93%), ORF (84.44%), and ORF (84.67%) indices, while in 2020 were observed for the OI5 (85.89%), ORF (84.75%), and OI6 (84.63%) indices. In Southeast Marmara, investment decisions taken at national level have led to population growth in the region. Although it was observed that there was no significant change in classification accuracy with the addition of spectral features to the original bands such as NDVI and SR, we believe that future testing of the data with different statistical and machine learning methods provide higher accuracy. © Author(s) 2024.
dc.identifier.doi10.51489/tuzal.1395189
dc.identifier.endpage25
dc.identifier.issn2687-4997
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85215434809
dc.identifier.scopusqualityN/A
dc.identifier.startpage12
dc.identifier.trdizinid1245858
dc.identifier.urihttps://doi.org/10.51489/tuzal.1395189
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1245858
dc.identifier.urihttps://hdl.handle.net/20.500.12428/29983
dc.identifier.volume6
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherOsman Orhan
dc.relation.ispartofTurkish Journal of Remote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250529
dc.subjectLULC
dc.subjectRemote Sensing
dc.subjectSouth-Eastern Marmara Region
dc.subjectVegetation Indices
dc.titleDetermination of the Effects of Various Spectral Index Combinations on Seasonal Land Use and Land Cover (LULC) Changes Using Random Forest (RF) Classification Case Study: Southeast Marmara Region 2016-2020
dc.title.alternativeSpektral İndeks Kombinasyonlarının Rastgele Orman (RO) Sınıflandırması Kullanarak Mevsimsel Arazi Kullanımı ve Bitki Örtüsü (AKBÖ) Değişiklikleri Üzerindeki Etkilerinin Belirlenmesi: Güneydoğu Marmara Bölgesi Örneği 2016-2020
dc.typeArticle

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