Denetimli Sınıflandırıcılarla Taşkın Haritalaması: 2021 Gediz Ovası Seli

Yıl 2023, , 100 - 113, 28.03.2023

Enis Arslan , Serkan Kartal

https://doi.org/10.48123/rsgis.1220879

Öz

Taşkın haritalarının oluşturulması, taşkın sebepli risklerin değerlendirilmesinde oldukça faydalıdır. Sel-taşkın haritalaması, eşikleme ile değişiklik tespiti (DT) ve makine öğrenimi tabanlı (MÖ) yöntemler gibi birçok uzaktan algılama tekniği ile gerçekleştirilebilmektedir. Bu çalışmalarda farklı uydu sistemleri tarafından sağlanan optik ve sentetik açıklıklı radar (SAR) görüntüleri yaygın olarak kullanılmaktadır. Bu çalışmada, denetimli MÖ algoritmaları ile Google Earth Engine'de (GEE) Sentinel-1 SAR ve Sentinel-2 MSI uydu verileri kullanılmıştır. Çalışma alanı olarak Türkiye'nin Gediz Ovası seçilmiştir ve bu alan çoğunlukla ekili arazilerle kaplıdır. Bu çalışmada 2021 yılı Şubat ayının ikinci günü meydana gelen taşkın olayı incelenmiş ve çalışma alanı için taşkın haritası oluşturulmuştur. Çalışma için, Support Vector Machines (SVM), Random Forest (RF) ve K-nearest Neighbor (KNN) MÖ algoritmaları seçilmiş ve modeller GEE'de manuel olarak oluşturulan etiketlenmiş verilerle eğitilmiştir. Ayrıca geleneksel yaklaşımla olay öncesi ve sonrası SAR görüntülerine DT uygulanmıştır. RF sınıflandırıcısı, %94 genel sınıflandırma doğruluğu ile Sentinel-2 MSI görüntülerinde en iyi performansı gösterirken, KNN sınıflandırıcı, Sentinel-1 SAR veri kümesi için %93,3 doğruluk değeri vererek SAR görüntülerinin tüm hava koşulları için uygunluğunu göstermektedir.

Anahtar Kelimeler

Taşkın haritalaması, GEE, Sınıflandırıcı, Sentinel-1, Sentinel-2

Flood Inundation Mapping with Supervised Classifiers: 2021 Gediz Plain Flood

Öz

Generation of flood inundation maps is beneficial in flood risk assessment and evaluation. Flood inundation mapping can be achieved by many remote sensing techniques like change detection (CD) with thresholding and machine learning-based (ML) methods. Optical and synthetic aperture radar (SAR) imagery are widely used, provided by different satellite systems. This study used Sentinel-1 SAR and Sentinel-2 MSI satellite data in Google Earth Engine (GEE) with supervised ML algorithms. Gediz Plain, Turkey was selected as the study area, which is an agricultural area covered mostly by croplands. A flood event that occurred on February 2, 2021, was examined and flood inundation map for the study area was composed. Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) ML algorithms were selected and models were trained with manually created labelled data in GEE. Also, CD was applied on after and before event SAR images in a traditional approach. RF classifier performs best in Sentinel-2 MSI imagery with 94% overall classification accuracy where KNN classifier gives 93.3% accuracy value for Sentinel-1 SAR dataset, indicating the robustness of SAR imagery for all-weather conditions.

Anahtar Kelimeler

Flood mapping, GEE, Classifier, Sentinel-1, Sentinel-2

Kaynakça

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