Sentinel-2 görüntüleri kullanılarak Burdur Gölü'nde kuraklık izleme

Yıl 2024, Cilt: 13 Sayı: 3, 882 - 891, 15.07.2024

Ümit Haluk Atasever , Hussein Hadi Abbas

https://doi.org/10.28948/ngumuh.1411803

Öz

Bu çalışma, Burdur Gölü'nde kuraklık izlemesi için Sentinel-2 uydu verileri ve K-means kümeleme kullanılarak otomatik bir iş akışı sunmaktadır. 2019'dan 2023'e kadar beş Sentinel-2 görüntüsü, spektral su indekslerini üretmek için işlenmiştir. Su indekslerinin eşiklenmesiyle bir su maskesi oluşturulmuş, böylece su kütlelerinin ayırt edilmesi sağlanmıştır. K-means kümeleme, zaman içinde göl alanındaki değişiklikleri ölçümlenmiştir. Sonuçlar, Ağustos 2019'dan Ağustos 2023'e kadar su genişliğinde azalan bir eğilim ortaya koymaktadır. Ağustos 2019'da su genişliği yaklaşık %18.53 (138.9456 Km2) iken, Ağustos 2023'e kadar yaklaşık %16.64'e (124.7500 Km2) düşmüş, başlangıç ve bitiş yılları arasında yaklaşık %10.3'lük bir su genişliği azalmasına işaret etmektedir. Bu yaklaşım, serbestçe erişilebilir uydu verileri ve makine öğrenmesi algoritmalarının operasyonel kuraklık izlemeye entegrasyonu için değerli bir çerçeve sunmaktadır.

Anahtar Kelimeler

Kuraklık İzleme, Su Maskesi, Sentinel-2 Görüntüleme, K-Means Kümeleme, Burdur Gölü Türkiye

Drought monitoring in Burdur Lake using Sentinel-2 images

Öz

This study presents an automated workflow for drought monitoring in Burdur Lake, Turkey, utilizing Sentinel-2 satellite data and K-means clustering. Five Sentinel-2 images from 2019 to 2023 were processed to derive spectral water indices. A water mask was generated by thresholding the indices, allowing for the distinction of water bodies. K-means clustering quantified changes in the lake area over time. The results reveal a decreasing trend in water extent from August 2019 to August 2023. In August 2019, the water extent was approximately 18.53% (138.9456 Km2), which declined to around 16.64% (124.7500 Km2) by August 2023, signifying an approximately 10.3% reduction in water extent between the start and end years. This approach demonstrates a valuable framework for the integration of freely available satellite data and machine learning algorithms in operational drought monitoring.

Anahtar Kelimeler

Drought Monitoring, Water Mask, Sentinel-2 Imagery, K-Means Clustering, Burdur Lake Türkiye

Kaynakça

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