Uzaktan algılama teknikleri kullanarak RUSLE-C faktör haritalarının oluşturulması: Kırıkkale/Sarıkızlı havzası

Öz

Peyzaj bozulmalarına sebep olan en önemli fiziksel etkiler arasında erozyon başta gelmektedir. Toprak kayıpları sonucunda birçok habitat etkilenebilmektedir. Erozyon sonucu bozulmuş peyzajların iyileştirilmesi için toprak kayıplarının dağılımının ve miktarının bilinmesi gerekmektedir. Yenilenmiş Evrensel Toprak Kaybı Eşitliği (RUSLE) toprak kayıplarının tahmin edilmesin de kullanılan matematiksel bir modeldir. Bu çalışmada, Sarıkızlı Çayı havzasında uzaktan algılama ve Coğrafi Bilgi Sistemleri (CBS) kullanılarak RUSLE modeli parametrelerinden olan bitki yönetim faktörünün (C faktör) aylık olarak tahmin edilmesi ve haritalarının oluşturulması amaçlanmıştır. RUSLE-C faktör değerleri uydu görüntülerinden Normalize Edilmiş Fark Bitki İndeksi (NDVI) haritaları üretilerek ve sonra üstel regresyon eşitliği kullanılarak mekânsal olarak hesaplanmıştır. RUSLE-C faktörü en düşük haziran ayında 0,24±0,20, en yüksek aralık ayında 0,75±0,18 değerini almıştır. RUSLE-C faktörün en önemli özelliği değişiminin izlenmesi ile hassas alanların belirlenmesi ve de toprak koruma önlemlerinin nasıl alınması gerektiği konusunda yardımcı olmasıdır. Sonuç olarak, uzaktan algılama/CBS/erozyon model entegrasyonu ile peyzaj bozulmalarının mekânsal olarak daha hızlı sürede belirlenmesi onarımın daha hızlı, ekonomik ve doğru olarak yapılmasını sağlayacaktır.

Anahtar Kelimeler

• Peyzaj bozulması
• erozyon
• C Faktör
• NDVI
• RUSLE

Creation of RUSLE C factor maps using remote sensing techniques: Kırıkkale/Sarıkızlı basin

Abstract

Erosion is one of the most important physical mechanisms causing landscape degradation. Many habitats can be affected as a result of soil losses. It is necessary to know the distribution and amount of soil losses in order to improve landscapes degraded by erosion. The Revised Universal Soil Loss Equation (RUSLE) is a mathematical model used to estimate soil losses. In this study, it is aimed to estimate and map the cover management factor (C factor), which is one of the parameters of the RUSLE model, on a monthly by remote sensing and Geographic Information System (GIS) in the Sarıkızlı basin. For RUSLE-C factor value, Normalized Difference Vegetation Index (NDVI) map was produced from satellite images and then spatially calculated using an exponential regression equation. RUSLE-C factor has the lowest value of 0.24 ± 0.20 in June and the highest value of 0.75 ± 0.18 in December. The most important feature of the RUSLE-C factor is that it helps in determining the areas sensitive to degradation by monitoring the change and how to take soil conservation measures. As a result, the spatial determination of landscape degradation in a faster time with remote sensing/GIS /erosion model integration will enable the reclamation to be done faster, economically, and accurately.

Keywords

• Landscape degradation
• erosion
• C Factor
• NDVI
• RUSLE

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