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

Year 2026, Volume: 6 Issue: 1, 119 - 132, 31.01.2026

Alperen Meral

https://doi.org/10.61112/jiens.1692029

https://izlik.org/JA89EP95KP

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

https://izlik.org/JA89EP95KP

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

References

• Farda NM (2017) Multi-temporal Land Use Mapping of Coastal Wetlands Area using Machine Learning in Google Earth Engine. IOP Conf Ser Earth Environ Sci 98:012042.
• Yesmin R, Mohiuddin ASM, Uddin MJ, Shahid MA (2014) Land use and land cover change detection at Mirzapur Union of Gazipur District of Bangladesh using remote sensing and GIS technology. IOP Conf Ser Earth Environ Sci 20:012055.
• Tariq A, Shu H, Siddiqui S, Imran M, Farhan M (2021) Monitoring Land Use And Land Cover Changes Using Geospatial Techniques, A Case Study Of Fateh Jang, Attock, Pakistan. Geography, Environment, Sustainability 14(1):41–52.
• Chughtai AH, Abbasi H, Karas IR (2021) A review on change detection method and accuracy assessment for land use land cover. Remote Sens Appl 22:100482.
• Ayenikafo OM, Wang YF (2021) LULC changes analysis in Sudano Guinean Region of Benin. Appl Ecol Environ Res 19(1):715–26.
• Bulut İ, Yüksel A, Yıldız E, Meral A, Kolak MN, Kocademir D, Akkuş H, Mohabbi M, Varolgüneş S (2024) Türkiye’de çığ kontrol projelerinin hazırlanma süreçleri: Bingöl ili Adaklı ilçesi Aktaş köyü örneği. Bingöl Üniversitesi Teknik Bilimler Dergisi 5(2):13–27.
• Meral A, Yüksel A (2024) Çığ Kontrolü Projelendirme Çalışmalarında Risk Analizi Değerlendirmesi. In: Ege 11th Uluslararası Uygulamalı Bilimler Kongresi. İzmir: Academy Global Publishing House; p. 842–55.
• Rwanga SS, Ndambuki JM (2017) Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences 08(04):611–22.
• Akbar TA, Hassan QK, Ishaq S, Batool M, Butt HJ, Jabbar H (2019) Investigative Spatial Distribution and Modelling of Existing and Future Urban Land Changes and Its Impact on Urbanization and Economy. Remote Sens (Basel) 11(2):105.
• Dome T, Gayane F, Guilgane F, Mouhamadou MMN, Mbagnick F (2022) Detection and predictive modeling of land use changes by CA-Markov in the northern part of the Southern rivers: From Lower Casamance to Gba river (Guinea Bissau). Journal of Ecology and The Natural Environment 14(1):1–14.
• Rawat JS, Kumar M (2015) Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science 18(1):77–84.
• Ntakirutimana A, Vansarochana C (2021) Assessment and Prediction of Land Use/Land Cover Change in the National Capital of Burundi Using Multi-temporary Landsat Data and Cellular Automata-Markov Chain Model. Environ Nat Resour J 19(5):1–14.
• Ramanamurthy BV, Vijayasaradhi B (2021) Change detection analysis in LULC of the upstream Thandava reservoir using RS and GIS applications. IOP Conf Ser Mater Sci Eng 1025(1):012034.
• Kamaraj M, Rangarajan S (2022) Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environmental Science and Pollution Research 29(57):86337–48.
• Schultz M, Voss J, Auer M, Carter S, Zipf A (2017) Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation 63:206–13.
• Rahmi KIN, Ali A, Maghribi AA, Aldiansyah S, Atiqi R (2022) Monitoring of land use land cover change using google earth engine in urban area: Kendari city 2000-2021. IOP Conf Ser Earth Environ Sci 950(1):012081.
• Abose YM, Begeno T (2020) Evaluations of Stream Flow Response to Land use and Land Cover Changes in Wabe Watershed, Omo-Gibe Basin, Ethiopia. International Journal of Civil, Mechanical and Energy Science 6(6):24–36.
• Aryal J, Sitaula C, Frery AC (2023) Land use and land cover (LULC) performance modeling using machine learning algorithms: a case study of the city of Melbourne, Australia. Sci Rep 13(1):13510.
• Mondal MS, Sharma N, Kappas M, Garg PK (2020) Ca markov modeling of land use land cover change predictions and effect of numerical iterations, image interval (time steps) on prediction results. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020:713–20.
• Mondal MS, Sharma N, Kappas M, Garg PK (2013) Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques. Geocarto Int 28(7):632–56.
• Gündüz HI (2025) Land-use land-cover dynamics and future projections using GEE, ML, and QGIS-MOLUSCE: A case study in Manisa. Sustainability 17(4):1363.
• Kamaraj M, Rangarajan S (2022) Predicting the future land use and land cover changes for Bhavani basin, Tamil Nadu, India, using QGIS MOLUSCE plugin. Environmental Science and Pollution Research 29(57):86337–48.
• Iskandar B, Kurnia AA, Jauhari A, Zannah F (2024) Modeling land cover change using MOLUSCE in Kahayan Tengah forest management unit, Kalimantan Tengah. Jurnal Sylva Lestari 12(2):242–57.
• Alshari EA, Gawali BW (2022) Modeling land use change in Sana’a City of Yemen with MOLUSCE. J Sens 2022:1–15.
• Muhammad R, Zhang W, Abbas Z, Guo F, Gwiazdzinski L (2022) Spatiotemporal change analysis and prediction of future land use and land cover changes using QGIS MOLUSCE plugin and remote sensing big data: A case study of Linyi, China. Land (Basel) 11(3):419.
• Bathe KD, Patil NS (2024) Assessment of land use-land cover dynamics and its future projection through Google Earth Engine, machine learning and QGIS-MOLUSCE: A case study in Jagatsinghpur district, Odisha, India. Journal of Earth System Science 133(2):111.
• Baghel S, Kothari MK, Tripathi MP, Singh PK, Bhakar SR, Dave V, Jain SK (2024) Spatiotemporal LULC change detection and future prediction for the Mand catchment using MOLUSCE tool. Environ Earth Sci 83(2):66.
• Amgoth A, Rani HP, Jayakumar KV (2023) Exploring LULC changes in Pakhal Lake area, Telangana, India using QGIS MOLUSCE plugin. Spatial Information Research 31(4):429–38.
• Isnain Z, Said SJ (2019) Using the geographical information system (GIS) methods in determination of landuse changes at Batu Sapi, Sandakan, Sabah, Malaysia. J Phys Conf Ser 1358(1):012069.
• Schultz M, Voss J, Auer M, Carter S, Zipf A (2017) Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation 63:206–13.
• Kafy AA, Faisal AA, Shuvo RM, Naim MdNH, Sikdar MdS, Chowdhury RR, Islam MA, Sarker HS, Khan HH, Kona MA (2021) Remote sensing approach to simulate the land use/land cover and seasonal land surface temperature change using machine learning algorithms in a fastest-growing megacity of Bangladesh. Remote Sens Appl 21:100463.
• Google Aerth Engine (2025) https://earthengine.google.com/ Accessed 2 January 2025
• USGS Earth Explorer (2025) https://earthexplorer.usgs.gov/ Accessed 05 January 2025
• ASF (2024) https://search.asf.alaska.edu/#/?zoom=9.322¢er=39.889,38.093&dataset= ALOS&polygon=POLYGON((38.5125%2038.0636,40.0474%2038.0636,40.0474%2039.1546,38.5125%2039.1546,38.5125%2038.0636))&resultsLoaded=true&granule=ALPSRS106522850-L1.5. Accessed 30 December 2024.
• Open Street Map (2024). https://www.openstreetmap.org/export#map=12/40.9153/40.4714. Erişim 30 Aralık 2024
• QGIS (2025). QGIS 3.40.4. qgis.org
• Bolat S, Doğan M (2022) Uzun dönemli (1984-2020) arazi kullanımı değişiminin tespiti ve modellemesi (2035): Gölcük İlçesi’nin analizi. Coğrafya Dergisi 2022(44):169–81.
• Alrubkhi A (2017) Land use change analysis and modeling using open source (QGIS) case study: Boasher Willayat. Sultan Qaboos University College Of Arts and Social Science.
• Rahman MTU, Tabassum F, Rasheduzzaman M, Saba H, Sarkar L, Ferdous J, Uddin SZ, Islam AZMZ (2017) Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh. Environ Monit Assess 189(11):565.
• Guidigan MLG, Sanou CL, Ragatoa DS, Fafa CO, Mishra VN (2019) Assessing land use/land cover dynamic and its impact in Benin Republic using land change model and CCI-LC products. Earth Systems and Environment 3(1):127–37.
• Aneesha Satya B, Shashi M, Deva P (2020) Future land use land cover scenario simulation using open source GIS for the city of Warangal, Telangana, India. Applied Geomatics 12(3):281–90.
• Sisay G, Gessesse B, Fürst C, Kassie M, Kebede B (2023) Modeling of land use/land cover dynamics using artificial neural network and cellular automata Markov chain algorithms in Goang watershed, Ethiopia. Heliyon 9(9):e20088.
• Heaton, JT (2018) Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning: The MIT Press, 2016, 800 pp, ISBN: 0262035618. Genetic Programming and Evolvable Machines 19, 305-307.
• Jokar Arsanjani J, Helbich M, Kainz W, Darvishi Boloorani A (2013) Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation 21:265–75.
• Aburas MM, Ho YM, Ramli MF, Ash’aari ZH (2016) The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation 52:380–9.
• Qiang Y (2015) Lam NSN Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata. Environ Monit Assess 187(3):57.
• Pontius RG, Peethambaram S, Castella JC (2011) Comparison of three maps at multiple resolutions: A case study of land change simulation in Cho Don District, Vietnam. Annals of the Association of American Geographers 101(1):45–62.
• Pontius RG, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 32(15):4407–29.
• Herold M, Couclelis H, Clarke KC (2005) The role of spatial metrics in the analysis and modeling of urban land use change. Comput Environ Urban Syst 29(4):369–99.
• Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–74.
• IPCC (2019) Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
• IPCC (2022) Sixth Assessment Report (AR6): Climate Change 2022 – Impacts, Adaptation and Vulnerability.
• Kabisch N, Korn H, Stadler J, Bonn A (2017) Nature-Based Solutions to Climate Change Adaptation in Urban Areas. Cham: Springer International Publishing.
• Meerow S, Newell JP, Stults M (2016) Defining urban resilience: A review. Landsc Urban Plan 147:38–49.