LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY

Öz

After the declaration of Ankara as the capital city of Turkey in 1923, the size of the city was identified to be insufficient to cope with the developmental and spatial needs of the city. In this study, the analysis and detection of land cover changes were conducted for the last three decades with ten-year time interval by using remotely sensed satellite data in Ankara to monitor the change in land cover, and growth and development of the city. Four classes; manmade area, land area, green area, and water area were created for each year images to assess change in land cover in central neighborhoods of Ankara. Maximum Likelihood Classifier (MLC) and Random Forest (RF) algorithms were performed and classification results were compared. Overall classification accuracy and overall kappa statistics computed as 85%-92% and between 0.78-0.87 for MLC algorithm, respectively. Comparing with MLC algorithm, RF algorithm’s performance was unsatisfied. As a second step of this study, administrative data of Ankara such as population, land use types, number of buildings and flats, and spatial development relationships were analyzed in integration with remote sensing data results to analyses land development in Ankara.

Anahtar Kelimeler

• Land Cover
• Change Detection
• Satellite Image Classification
• Urban Growth

ARAZİ ÖRTÜSÜ DEĞİŞİMİ İZLEME TEKNİKLERİ VE MEKÂNSAL GELİŞİM: TÜRKİYE'NİN BAŞKENTİ ÖRNEĞİ

Öz

Ankara'nın 1923'te Türkiye'nin başkenti olarak ilan edilmesinden sonra, kentin büyüklüğünün kentin gelişimsel ve mekânsal ihtiyaçlarını karşılamak için yetersiz olduğu tespit edilmiştir. Bu çalışmada, Ankara ilinde uzaktan algıma uydu verileri kullanılarak, kentin büyüme ve gelişiminin izlenmesi amacıyla son otuz yılda on yıllık zaman aralığı ile arazi örtüsü değişimlerinin analizi ve tespiti yapılmıştır. Ankara merkez mahallelerinde arazi örtüsündeki değişimi değerlendirmek için 5 farklı yılda insan yapımı alan, arazi alanı, yeşil alan ve su alanı olarak görüntüler sınıflandırılmıştır. Maksimum Olabilirlik Sınıflandırıcısı (MLC) ve Rastgele Orman (RF) algoritmaları ile sınıflandırma gerçekleştirilmiş ve sınıflandırma sonuçları karşılaştırılmıştır. MLC algoritması için genel sınıflandırma doğruluğu ve genel kappa istatistikleri sırasıyla %85-92 ve 0.78-0,87 arasında hesaplanmıştır. MLC algoritması ile karşılaştırıldığında, RF algoritmasının performansı daha kötü çıktığı görülmüştür. Bu çalışmanın ikinci adımı olarak Ankara'nın nüfus, arazi kullanım tipleri, bina ve daire sayısı ve mekânsal gelişim ilişkileri gibi idari verileri, Ankara'daki arazi gelişimini analiz etmek için uzaktan algılama veri sonuçları ile entegre olarak analiz edilmiştir.

Anahtar Kelimeler

• Arazi Örtüsü
• Değişim Yakalama
• Uydu Görüntüsü Sınıflandırma
• Kentsel Büyüme

Kaynakça

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