TY  - JOUR
T1  - Destek Vektör Makineleri ile MODIS Verisinden Fraksiyonel Kar Örtüsünün Ilgaz Orman İşletme Müdürlüğü Bölgesinde Belirlenmesi
TT  - Estimation of Fractional Snow Cover from MODIS Data in Ilgaz Forest District Region by Support Vector Machines
AU  - Kuter, Semih
AU  - Çiftçi, Bora Berkay
PY  - 2019
DA  - December
DO  - 10.24011/barofd.595462
JF  - Bartın Orman Fakültesi Dergisi
PB  - Bartin University
WT  - DergiPark
SN  - 1302-0943
SP  - 911
EP  - 926
VL  - 21
IS  - 3
LA  - tr
AB  - Bu çalışmada, Çankırı veKastamonu il sınırları içinde yer alan Ilgaz Orman İşletme Müdürlüğübölgesinde, orta çözünürlüklü görüntüleme spektroradyometresi (MODIS) verisindenetkili kar kaplı alan (EKKA) haritalaması amacıyla destek vektör makineleri(DVM) tasarımı araştırılmıştır. DVM modellerin eğitilmesinde, Mart 2000 veNisan 2016 tarihleri arasında alınan MODIS görüntülerinden elde edilen toplam10 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 Landsat7/8 görüntülerinden üretilmiştir. DVM modellerinin doğruluğu, eğitimverilerinin boyutuna ve örneklem türüne göre değerlendirilmiştir. Kerneltürünün DVM modellerinin doğruluğu üzerindeki etkisi de incelenmiştir.Sonuçlara göre, doğrusal, 2., 3. ve 4. dereceden polinomların yanı sıra radyaltemel fonksiyonu (RBF) kernelleri ile eğitilmiş tüm DVM modelleri, ilgilireferans EKKA haritaları ile yüksek korelasyon oranları vermektedir (R ≥ 0,91). Öte yandan, MODIS'in standartEKKA ürünü olan MOD10A1, ortalama R =0,77 ile biraz daha zayıf performans sergilemektedir. Eğitim aşamasındaharcanan CPU zamanlarına göre hesaplama etkinliği bakımından, RBF kernelinin, küçük,orta ve büyük boyutlu eğitim veri setleri için sırasıyla 279, 2300 ve 8457saniyelik ortalama model oluşturma süreleriyle daha üstün olduğu görülmüştür.
KW  - Karın uzaktan algılanması
KW  - MODIS
KW  - Landsat
KW  - destek vektör regresyonu
N2  - This study is focused on the assessment ofsupport vector machines (SVM) in order to estimate the fractional snow cover(FSC) from coarse spatial resolution moderate resolution imagingspectroradiometer (MODIS) imagery in Ilgaz Forest District area located withinthe cities of Çankırı and Kastamonu. SVMmodel training is carried out by employing 10 predictor variables obtained fromMODIS images taken between March 2000 and April 2016, namely, MODIStop-of-atmospheric reflectance values of bands 1-7, normalized difference snowindex, normalized difference vegetation index and land cover class. Higherresolution Landsat 7/8 images are used to generate the corresponding referenceFSC maps. Accuracy of SVM models are assessed with respect to the size of thetraining data and the sampling type. The impact of the kernel type on theaccuracy of the SVM models is also investigated. According to the results, allSVM models trained with linear, 2nd, 3rd and 4thorder polynomials as well as radial basis function (RBF) kernels give highcorrelation rates with the associated reference FSC maps (R ≥ 0,91). On the other hand, MOD10A1, the standard FSC product ofMODIS, exhibits slightly poorer performance with average R = 0,77. In terms of computational efficiency with respect to CPUtimes spent during the training stage, RBF kernel is found to be superior withaverage model building times of 279, 2300 and 8457 seconds for small-, medium-and large-sized training data sets, respectively.
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UR  - https://doi.org/10.24011/barofd.595462
L1  - https://dergipark.org.tr/en/download/article-file/893856
ER  -