Arazi Örtüsü ve Kullanımının Zamansal ve Mekânsal Değişiminin Yapay Sinir Ağları ile Modellenmesi: Kastamonu Örneği

Sınırlı olan doğal kaynakların yönetiminde en uygun yöntemleri tespit etmek ve kullanmak, teknolojinin etkin kullanılmasıyla kaliteli bilgiyle kısa zamanda sonuca ulaşmak günümüzde son derece önemlidir. Uzaktan algılama (UA) teknikleri bu bakımdan çok etkili bir araç olarak kullanılmaktadır. Objelerle doğrudan temas olmaksızın çeşitli parametreler hakkında bilgiler edinmek hem zaman hem de maliyet açısından avantajlar sağlamaktadır. UA teknolojileri birbirinden farklı birçok disiplinde kullanılmaktadır. Bu teknolojilerin kullanıldığı en önemli uygulama alanlarından biri de uydu görüntüleri yardımıyla kentsel gelişimin izlenmesidir. Kentsel arazi kullanımının detaylı olarak belirlenmesi karar vericiler, planlayıcılar, uygulayıcılar ve araştırmacılar için etkili planlama faaliyetleri yürütebilmeleri açısından önemlidir. Bu çalışmada Kastamonu ili merkez ilçesine ait 1999 - 2016 yılları arasındaki arazi örtüsü ve arazi kullanımının değişimi incelenmiş; arazi kullanımı ve değişimi grupları oluşturulmuştur. Öncelikle çalışma alanına ait uydu görüntüleri kontrolsüz sınıflandırma metoduyla sınıflandırılmış ve doğruluk dereceleri hesaplanmıştır. Sınıflandırılan uydu görüntüleri Yapay Sinir Ağları (YSA) yaklaşımı ile çalışma alanının 2033 yılındaki muhtemel arazi örüsü, kullanımı ve değişimi modellenmiştir. Buna göre çalışma alanında 1999 yılı ile 2016 yılı arasında meydana gelen değişim; ormanlık alanlar için %7.8 azalma, su alanları için %10.8 artma, tarım alanları için %13.9 azalma ve yapılaşma alanları için %10.9 artma şeklinde gerçekleştiği tespit edilmiştir. Elde edilen sonuçlar ile arazi örtüsü ve arazi kullanımı değişiminin tespit edilmesi ve gelecekte nasıl bir seyir izleyeceğinin tahmin edilebilmesi için uygulanabilir pratik bir araç olduğu düşüncesine varılmıştır. Bu çalışmada kullanılan YSA yaklaşımının planlayıcı ve karar vericiler için önemli bir karar destek sistemi aracı olacağı öngörülmektedir.

Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample

Currently, it is very important to identify and use the most appropriate methods in the management of limited resources and to reach a conclusion in a short time period by using the technology in an effective manner to fastly obtain information in high quality. Remote sensing (RS) techniques are used as a very effective tool for this purpose. Obtaining information about various parameters without direct contact with the objects provides advantages in terms of both time and cost. RS technologies are used in various different disciplines. One of the most important application areas where these technologies are used is to monitor urban development by the help of the satellite images. Determination of urban land use in detail is important for decision-makers, planners, practitioners and researchers to conduct effective planning activities. In this study the change in land cover and land use between the years of 1999 and 2016 in the central district of Kastamonu was investigated; land use and exchange groups were formed. First, satellite images of the study area were classified by controlled classification method and their accuracy was calculated. The classified satellite images are used to model the probable land area, its usage and changes in 2033 by using Artificial Neural Networks (ANN) approach. According to this, changes in the field between the years of 1999 and 2016 are given as follows; 7.8% decrease for forest areas, 10.8% increase for water areas, 13.9% decrease for agricultural areas and 10.9% increase for construction areas. Based on the results, it was thought that it is a feasible and practical tool to determine the change of land cover and land use to predict the course of the future. The ANN approach used in this study is predicted to become an important decision support system for planners and decision makers.

Keywords:

Geographic Information Systems, Remote Sensing Land Cover/Land Use, Artificial Neural Networks,

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