Spektral İndeks Kombinasyonlarının Rastgele Orman (RO) Sınıflandırması Kullanarak Mevsimsel Arazi Kullanımı ve Bitki Örtüsü (AKBÖ) Değişiklikleri Üzerindeki Etkilerinin Belirlenmesi: Güneydoğu Marmara Bölgesi Örneği 2016-2020

Year 2024, , 12 - 25, 30.06.2024

Eda Aşci , Levent Genç

https://doi.org/10.51489/tuzal.1395189

Abstract

Düzensiz nüfus artışı, göç hareketliliği ve insanların vejetasyon dinamiklerine etkileri Arazi Kullanım ve Bitki Örtüsü (AKBÖ) değişimlerine yol açabilmektedir. AKBÖ değişiklikleri sanayi ile ilişkili kıyı bölgelerinde oldukça önemlidir. Türkiye’nin önemli kıyı alanlarından olan Güneydoğu Marmara alanı da çevredeki değişimlerden etkilenmektedir. Çalışma alanı, Sentinel-2 tabanlı bitki örtüsü indeksleri kombinasyonlarını kullanarak gerek AKBÖ değişimini gerekse sınıflandırmanın doğruluğunu belirlemek amacıyla seçilmiştir. Çalışma alanında Gemlik- Bursa Kuzey Kavşağı yatırım alanı ve yeni inşa edilen TOGG (Türkiye'nin Otomobili Girişim Grubu) fabrikası yer almaktadır. Çalışma alanı, Yalova ili Armutlu ilçesi ve Bursa ili Osmangazi, Mudanya ve Gemlik ilçelerini kapsayan alanda kıyıdan anakaraya 5 km’lik tampon bölge oluşturularak belirlenmiştir. Rastgele Orman (RO) sınıflandırma tekniği, 2016 ve 2020 yıllarında 3 sezon boyunca Sentinel-2 multispektral uydu görüntülerinden elde edilen indeksler kullanılarak orijinal bantlara ve 21 yeni bant kombinasyonuna uygulanmıştır. Sınıflandırma için kullanılan yeni bant kombinasyonları, normalize edilmiş bitki örtüsü indeksleri (NDVI), orijinal bantlar ve basit oran (SR) formülünden elde edilen bantlar eklenerek oluşturulmuştur. En yüksek doğruluk sonuçları 2016 yılı kış, ilkbahar ve yaz mevsimleri için OI12 (%82,93), ORF (%84,44) ve yine ORF (%84,67) indekslerinde gözlemlenirken, 2020 yılında OI5 (%85,89), ORF (%84,75) ve OI6 (%84,63) indekslerinde gözlemlenmiştir. Güneydoğu Marmara'da ulusal düzeyde alınan yatırım kararları bölgede nüfus artışına yol açmıştır. NDVI ve SR gibi orijinal bantlara spektral özelliklerin eklenmesiyle sınıflandırma doğruluğunda önemli bir değişiklik olmadığı gözlemlenmiş olsa da verilerin gelecekte farklı istatistiksel ve makine öğrenimi yöntemleriyle test edilmesinin sınıflama doğruluğunu daha fazla artırabilir.

Keywords

Uzaktan Algılama, AKBÖ, Vejetasyon İndeksler, Güney-Doğu Marmara Bölgesi

Ethical Statement

yok

Thanks

yok

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

Abstract

The 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.

Keywords

Remote Sensing, LULC, Vegetation İndices, South – Eastern Marmara Region

Ethical Statement

no

Supporting Institution

This study is part of the Eda ASCI’s Master Thesis on Graduate School of Çanakkale Onsekiz Mart University, School of Graduate Studies, Department of Geographical Information Technology, Turkey

Thanks

no

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