Destek Vektör Makineleri ile MODIS Verisinden Fraksiyonel Kar Örtüsünün Ilgaz Orman İşletme Müdürlüğü Bölgesinde Belirlenmesi

Bu çalışmada, Çankırı ve Kastamonu il sınırları içinde yer alan Ilgaz Orman İşletme Müdürlüğü bölgesinde, orta çözünürlüklü görüntüleme spektroradyometresi (MODIS) verisinden etkili kar kaplı alan (EKKA) haritalaması amacıyla destek vektör makineleri (DVM) tasarımı araştırılmıştır. DVM modellerin eğitilmesinde, Mart 2000 ve Nisan 2016 tarihleri arasında alınan MODIS görüntülerinden elde edilen toplam 10 bağımsız değişken; MODIS bant 1-7 atmosfer üstü reflektans değerleri, normalize fark kar indisi, normalize fark vejetasyon indisi ve arazi sınıfı kullanılmıştır. Referans EKKA haritaları daha yüksek mekânsal çözünürlüğe sahip ilgili Landsat 7/8 görüntülerinden üretilmiştir. DVM modellerinin doğruluğu, eğitim verilerinin boyutuna ve örneklem türüne göre değerlendirilmiştir. Kernel türünün DVM modellerinin doğruluğu üzerindeki etkisi de incelenmiştir. Sonuçlara göre, doğrusal, 2., 3. ve 4. dereceden polinomların yanı sıra radyal temel fonksiyonu (RBF) kernelleri ile eğitilmiş tüm DVM modelleri, ilgili referans EKKA haritaları ile yüksek korelasyon oranları vermektedir (R ≥ 0,91). Öte yandan, MODIS'in standart EKKA ürünü olan MOD10A1, ortalama R = 0,77 ile biraz daha zayıf performans sergilemektedir. Eğitim aşamasında harcanan CPU zamanlarına göre hesaplama etkinliği bakımından, RBF kernelinin, küçük, orta ve büyük boyutlu eğitim veri setleri için sırasıyla 279, 2300 ve 8457 saniyelik ortalama model oluşturma süreleriyle daha üstün olduğu görülmüştür.

Estimation of Fractional Snow Cover from MODIS Data in Ilgaz Forest District Region by Support Vector Machines

This study is focused on the assessment of support vector machines (SVM) in order to estimate the fractional snow cover (FSC) from coarse spatial resolution moderate resolution imaging spectroradiometer (MODIS) imagery in Ilgaz Forest District area located within the cities of Çankırı and Kastamonu. SVM model training is carried out by employing 10 predictor variables obtained from MODIS images taken between March 2000 and April 2016, namely, MODIS top-of-atmospheric reflectance values of bands 1-7, normalized difference snow index, normalized difference vegetation index and land cover class. Higher resolution Landsat 7/8 images are used to generate the corresponding reference FSC maps. Accuracy of SVM models are assessed with respect to the size of the training data and the sampling type. The impact of the kernel type on the accuracy of the SVM models is also investigated. According to the results, all SVM models trained with linear, 2nd, 3rd and 4th order polynomials as well as radial basis function (RBF) kernels give high correlation rates with the associated reference FSC maps (R ≥ 0,91). On the other hand, MOD10A1, the standard FSC product of MODIS, exhibits slightly poorer performance with average R = 0,77. In terms of computational efficiency with respect to CPU times spent during the training stage, RBF kernel is found to be superior with average model building times of 279, 2300 and 8457 seconds for small-, medium- and large-sized training data sets, respectively.

Keywords:

Remote sensing of snow, MODIS Landsat, support vector regression,

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