Urbanization under constraint: predicting land use futures in the geographically fragile city of Rize

Abstract

This study explores land use and land cover (LULC) dynamics in the geographically constrained and ecologically fragile city center of Rize, Turkey. Using remote sensing-based classification from 1990 to 2025, future LULC changes for 2060 were simulated through the QGIS MOLUSCE module, integrating Cellular Automata-Markov Chain (CA-Markov) and Artificial Neural Network (ANN) models. Results indicate a substantial increase in urban areas, primarily at the expense of agricultural and natural lands, with urban expansion concentrated in low-lying, flood-prone zones such as riverbeds and coastal plains. The ANN-based framework demonstrated high predictive accuracy (93.53% overall accuracy; spatial Kappa: 1.00), confirming its applicability for complex terrain conditions. The findings underscore the critical need for zoning regulations informed by spatial modelling to mitigate potential implications. The proposed methodology provides a scalable and transferable decision-support tool for sustainable land management and climate-resilient urban planning in mountainous and rapidly urbanizing regions.

Keywords

• LULC
• MOLUSCE
• Ecological fragility
• CA_Markov model
• ANN

Kentleşmenin Sınırları: Rize'de Gelecekteki Arazi Kullanımının Öngörülmesi

Abstract

Bu çalışma, Türkiye'nin coğrafi olarak kısıtlı ve ekolojik olarak kırılgan Rize şehir merkezindeki arazi kullanımı ve arazi örtüsü (AKÖ) dinamiklerini araştırmaktadır. 1990'dan 2025'e kadar Landsat tabanlı sınıflandırmalar kullanılarak, 2060 yılı için gelecekteki LULC değişiklikleri, Hücresel Otomata-Markov Zinciri (CA-Markov) ve Yapay Sinir Ağı (ANN) modellerini entegre eden QGIS MOLUSCE modülü aracılığıyla simüle edilmiştir. Sonuçlar, kentsel genişlemenin nehir yatakları ve kıyı ovaları gibi alçakta kalan, sele eğilimli bölgelerde yoğunlaşmasıyla birlikte, öncelikle tarımsal ve doğal araziler pahasına kentsel alanlarda önemli bir artış olduğunu göstermektedir. YSA tabanlı çerçeve yüksek tahmin doğruluğu (%93,53 genel doğruluk; uzamsal Kappa: 1,00) göstererek karmaşık arazi koşullarında uygulanabilirliğini teyit etmiştir. Bulgular, çevresel riskleri azaltmak ve karbon yutak kapasitesini korumak için mekânsal modelleme ile bilgilendirilmiş imar düzenlemelerine duyulan kritik ihtiyacın altını çizmektedir. Önerilen metodoloji, dağlık ve hızla kentleşen bölgelerde sürdürülebilir arazi yönetimi ve iklime dirençli kentsel planlama için ölçeklenebilir ve aktarılabilir bir karar destek aracı sunmaktadır.

Keywords

• LULC
• MOLUSCE
• Ekolojik Kırılganlık
• CA-Markov Modeli
• YSA

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