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

Year 2019, , 911 - 926, 15.12.2019

Bora Berkay Çiftçi Semih Kuter

https://doi.org/10.24011/barofd.595462

Abstract

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, 2^nd, 3^rd and 4^th
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

Project Number

OF090316L04

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

Abstract

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.

Keywords

Karın uzaktan algılanması, MODIS, Landsat, destek vektör regresyonu

Supporting Institution

ÇANKIRI KARATEKİN ÜNİVERSİTESİ BAP BİRİMİ

Project Number

OF090316L04

Thanks

Bu çalışma, Bora Berkay ÇİFTÇİ’nin Çankırı Karatekin Üniversitesi BAP biriminin OF090316L04 nolu projesince desteklenen yüksek lisans tezinden üretilmiştir.

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