Makro Yerşekillerinin Tanımlanmasında Ölçek ve Örneklem Pencere Boyutuna İlişkin Belirsizlikler

Bu çalışma makro yer şekillerinin tanımlanmasında temel alınan pencere örneklem boyutlarının istatistiksel önemi ve tanımlamalarda meydana getirdiği farklılıkların üzerinde durmaktadır. Yerşekillerinin otomatik olarak sınıflandırılmasında, optimum ölçeğin belirlenmesi sorunu önemini korumaktadır. Bu nedenle, ölçek faktörü ile örneklem pencere boyutu arasındaki ilişkiler yer şekillerinin tanımlanmasında dolayısıyla sınıflandırılmasındaki ilk aşamayı oluşturmaktadır. Yapılan değerlendirmeler, farklı çözünürlüklerde sayısal yükseklik modelleri Global Multi-resolution Terrain Elevation Data–GMTED2010 ve Multi-Error-Removed Improved–Terrain DEM kullanarak yapılmıştır. Dağ-plato ve dağ-ova arasındaki sınır belirsizliklerinin farklı ölçek ve analiz pencerelerinde tanımlamalarda getirdiği farklılıklar, UNEP-WCMC 2000 (K1) sınıflama algoritması kullanılarak Türkiye özelinde tartışılmıştır. Bu alanlara ilişkin yükseklik, eğim, topoğrafik röliyef gibi sayısal yükseklik modeli türevleri ve bunlara ait tanımsal istatistikler kullanılarak veri matrisleri oluşturulmuştur. Seçili alanlarda sahayı en iyi temsil eden ölçek ve pencere boyutlarının kombinasyonlarını içeren test sonuçları, pencere boyutunda yapılan değişikliklerle genelleştirme kapasitesi arttıkça tanımlanan makro yer şekli birliğinin farklı bir haritayla sonuçlanabileceğini göstermektedir. Buna göre makro yer şekillerinin tanımlanmasında, çalışmamızda değişen oranlarda yapılan pencere boyutu testlerinde belirlenen 2.5 km’lik komşuluk analiz penceresi boyutu üst sınırı ile daha anlamlı sonuçlar ortaya çıkmıştır. Yerşekli sınıflamasında dağ sınır ilişkilerinin, SYM çözünürlüğünden ziyade komşuluk analiz pencere boyutuna daha duyarlı olduğu görülmüştür.

Anahtar Kelimeler:

Jeomorfometri, Makro Yer Şekilleri, Dağ Sınıfları, Sayısal Yükseklik Modeli

Uncertainties Related to Scale and Sampling Window Size in Defining Macro Landforms

This study focuses on the statistical significance of sampling window sizes, which are used to define macro landforms and the differences they cause in definitions. In the automatic classification of landforms, the problem of determining the optimum scale remains important. Therefore, the relations between the scale factor and the window size constitute the first step, thus classifying landforms. The evaluations were carried out using GMTED2010 and MERIT DEM at different resolutions. The differences in the definitions of different scales and analysis windows caused by the border uncertainties between mountainplateau and mountain-plain that are specific to Türkiye were discussed using the UNEP-WCMC 2000 classification algorithm. Data matrices were created using DEM derivatives such as elevation, slope, and topographic relief for these areas and their descriptive statistics. The test results, which include the combinations of scale and window sizes that best represent the area in selected fields, indicate that the defined macro landform units can result in a more different map as the generalization capacity increases with the changes made in the window size. More meaningful results emerged with the upper limit of the 2.5 km NAW size determined in our study’s window size tests performed at varying rates. In landform classification, mountain boundary relationships were more sensitive to NAW size than DEM resolution.

Keywords:

Geomorphometry, Macro Landforms, Mountain Classes, Digital Elevation Model,

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