Google Earth Engine kullanılarak makine öğrenmesi tabanlı iyileştirilmiş arazi örtüsü sınıflandırması: Atakum, Samsun örneği

Year 2024, Volume: 9 Issue: 3, 375 - 390

Zelalem Ayalke , Aziz Şişman

https://doi.org/10.29128/geomatik.1472160

Abstract

Uzaktan algılama görüntüleri kullanılarak üretilen arazi örtüsü (AÖ) haritaları çevre yönetimi, kentsel planlama, ekolojik araştırmalar vb. çalışmalarda önemli bir temel bileşendir. Bu çalışmada, Google Earth Engine (GEE) ortamında makine öğrenmesi yöntemleri kullanarak Atakum ilçesi sınıflandırılmış arazi örtüsü haritası üretilmesi amaçlanmıştır. Çalışmada, Rastgele Orman (RO) ve Gradyan Ağaç Hızlandırma (GTB) yöntemleri kullanılmıştır. Veri seti olarak Landsat 8 uydu görüntüleri ve ALOS DEM kullanılmıştır. Sınıflandırmayı geliştirmek için; Normalleştirilmiş Fark Bitki Örtüsü İndeksi (NDVI), Normalleştirilmiş Fark Yapılaşma İndeksi (NDBI), Normalleştirilmiş Fark Su İndeksi (NDWI), Çıplak Toprak İndeksi (BSI), Toprağa Göre Ayarlanmış Bitki Örtüsü İndeksi (SAVI) ve Geliştirilmiş Bitki Örtüsü İndeksi (EVI) kullanılmıştır. Çalışma alanında arazi örtüsü; kentsel alanlar, bitki örtüsü, tarım arazisi, çıplak arazi ve su kütleleri olarak sınıflandırılmıştır. Kullanılan modelin performansını optimize etmek için tüm girdi değişkenleri normalize edilmiştir. Modelin performansı, kullanıcı doğruluğu, üretici doğruluğu, genel doğruluk ve kappa katsayısı doğruluk değerlendirme teknikleri ile değerlendirilmiştir. Bu çalışmada, hazırlanan arazi örtüsü için RO ve GTB'nin hesaplanan kappa katsayıları sırasıyla %95,6 ve %96,0, ortalama genel doğruluk ise %96,8 ve %97,1'dır. Çalışmada kullanılan iki makine öğrenmesi yönteminden, GTB'nin RO'dan daha iyi performans gösterdiği gözlemlenmiştir.

Keywords

Arazi örtüsü sınıflandırması, Makine öğrenmesi, Landsat görüntüleri, Google Earth Engine

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