TY  - JOUR
T1  - Modeling of Temporal and Spatial Changes of Land Cover and Land Use by Artificial Neural Networks: Kastamonu Sample
TT  - Arazi Örtüsü ve Kullanımının Zamansal ve Mekânsal Değişiminin Yapay Sinir Ağları ile Modellenmesi: Kastamonu Örneği
AU  - Buğday, Ender
AU  - Doğan, Samet
PY  - 2018
DA  - December
JF  - Bartın Orman Fakültesi Dergisi
PB  - Bartin University
WT  - DergiPark
SN  - 1302-0943
SP  - 653
EP  - 663
VL  - 20
IS  - 3
LA  - en
AB  - Currently, it is very important to identify anduse the most appropriate methods in the management of limited resources and toreach a conclusion in a short time period by using the technology in aneffective manner to fastly obtain information in high quality. Remote sensing (RS)techniques are used as a very effective tool for this purpose. Obtaininginformation about various parameters without direct contact with the objectsprovides advantages in terms of both time and cost. RS technologies are used invarious different disciplines. One of the most important application areaswhere these technologies are used is to monitor urban development by the helpof the satellite images. Determination of urban land use in detail is importantfor decision-makers, planners, practitioners and researchers to conducteffective planning activities. In this study the change in land cover and landuse between the years of 1999 and 2016 in the central district of Kastamonu wasinvestigated; land use and exchange groups were formed. First, satellite imagesof the study area were classified by controlled classification method and theiraccuracy was calculated. The classified satellite images are used to model theprobable land area, its usage and changes in 2033 by using Artificial NeuralNetworks (ANN) approach. According to this, changes in the field between theyears 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 itis a feasible and practical tool to determine the change of land cover and landuse to predict the course of the future. The ANN approach used in this study ispredicted to become an important decision support system for planners anddecision makers.
KW  - Geographic Information Systems
KW  - Remote Sensing
KW  - Land Cover/Land Use
KW  - Artificial Neural Networks
N2  - Sınırlı olan doğal kaynakların yönetiminde enuygun yöntemleri tespit etmek ve kullanmak, teknolojinin etkin kullanılmasıylakaliteli bilgiyle kısa zamanda sonuca ulaşmak günümüzde son derece önemlidir.Uzaktan algılama (UA) teknikleri bu bakımdan çok etkili bir araç olarakkullanılmaktadır. Objelerle doğrudan temas olmaksızın çeşitli parametrelerhakkında bilgiler edinmek hem zaman hem de maliyet açısından avantajlarsağlamaktadır. UA teknolojileri birbirinden farklı birçok disiplindekullanılmaktadır. Bu teknolojilerin kullanıldığı en önemli uygulamaalanlarından biri de uydu görüntüleri yardımıyla kentsel gelişiminizlenmesidir. Kentsel arazi kullanımının detaylı olarak belirlenmesi kararvericiler, planlayıcılar, uygulayıcılar ve araştırmacılar için etkili planlamafaaliyetleri yürütebilmeleri açısından önemlidir. Bu çalışmada Kastamonu ilimerkez ilçesine ait 1999 - 2016 yılları arasındaki arazi örtüsü ve arazi kullanımınındeğişimi incelenmiş; arazi kullanımı ve değişimi grupları oluşturulmuştur. Öncelikleçalışma alanına ait uydu görüntüleri kontrolsüz sınıflandırma metoduylasınıflandırılmış ve doğruluk dereceleri hesaplanmıştır. Sınıflandırılan uydugörüntüleri Yapay Sinir Ağları (YSA) yaklaşımı ile çalışma alanının 2033yılındaki muhtemel arazi örüsü, kullanımı ve değişimi modellenmiştir. Buna göreçalışma alanında 1999 yılı ile 2016 yılı arasında meydana gelen değişim;ormanlık alanlar için %7.8 azalma, su alanları için %10.8 artma, tarım alanlarıiçin %13.9 azalma ve yapılaşma alanları için %10.9 artma şeklinde gerçekleştiğitespit edilmiştir. Elde edilen sonuçlar ile arazi örtüsü ve arazi kullanımıdeğişiminin tespit edilmesi ve gelecekte nasıl bir seyir izleyeceğinin tahminedilebilmesi için uygulanabilir pratik bir araç olduğu düşüncesine varılmıştır.Bu çalışmada kullanılan YSA yaklaşımının planlayıcı ve karar vericiler içinönemli bir karar destek sistemi aracı olacağı öngörülmektedir.
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UR  - https://dergipark.org.tr/en/pub/barofd/issue//467974
L1  - https://dergipark.org.tr/en/download/article-file/599736
ER  -