Journals / International Journal of Engineering and Geosciences / 2026 / Volume: 11 Issue: 2

Mapping Urban Growth Through Landscape Expansion Index and Land Use Analysis: Evidence from Western Turkey

Kemal Mert Cubukcu(DOKUZ EYLÜL ÜNİVERSİTESİ)0000-0003-3604-7014
Pages
—
Publication Date
2025-12-16
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal a significant increase in built-up areas, particularly after 2000, with a total expansion from 45682 hectares in 1984 to 68869 hectares in 2025.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study investigates the dynamics of Land Use and Land Cover (LULC) changes along the İzmir-Denizli Highway corridor in western Turkey from 1984 to 2025, utilizing remote sensing techniques and the Landscape Expansion Index (LEI) to analyze urban growth patterns.
📊
Key Findings & Data:The findings reveal a significant increase in built-up areas, particularly after 2000, with a total expansion from 45682 hectares in 1984 to 68869 hectares in 2025.
💡
Conclusion & Impact:The study underscores the importance of integrating spatial and temporal analyses in urban planning to promote more sustainable development patterns and mitigate the adverse effects of urbanization on the environment.
🔬Design:Research Article
📍Scope:2025 Verileri
⚙️Variables:Remote Sensing · Land Use Land Cover (LULC) · Landscape Expansion Index (LEI)

Abstract

This study investigates the dynamics of Land Use and Land Cover (LULC) changes along the İzmir-Denizli Highway corridor in western Turkey from 1984 to 2025, utilizing remote sensing techniques and the Landscape Expansion Index (LEI) to analyze urban growth patterns. Employing cloud-free Landsat satellite imagery and the Random Forest classification algorithm within Google Earth Engine, the research identifies and quantifies built-up area expansion over four decades. The findings reveal a significant increase in built-up areas, particularly after 2000, with a total expansion from 45682 hectares in 1984 to 68869 hectares in 2025. The analysis highlights a predominance of edge-expansion growth (71.3%), with outlying growth (27.4%) and minimal infilling growth (1.3%). This trend indicates a shift towards urban sprawl, raising concerns about the sustainability of land use practices. The study underscores the importance of integrating spatial and temporal analyses in urban planning to promote more sustainable development patterns and mitigate the adverse effects of urbanization on the environment.

🔖

Cite This Article

Demirdağ, G. E., Cubukcu, K. M. (2025). Mapping Urban Growth Through Landscape Expansion Index and Land Use Analysis: Evidence from Western Turkey. International Journal of Engineering and Geosciences, Volume: 11 Issue: 2. https://doi.org/10.26833/ijeg.1659422

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (65)

  1. Liu, X., Li, X., Chen, Y., Tan, Z., Li, S., & Ai, B. (2010). A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology, 25(5), 671–682. https://doi.org/10.1007/s10980-010-9454-5
  2. Malaviya, S., Munsi, M., Oinam, G., & Joshi, P. K. (2010). Landscape approach for quantifying land use land cover change (1972-2006) and habitat diversity in a mining area in Central India (Bokaro, Jharkhand). Environmental Monitoring and Assessment, 170(1–4), 215–229. https://doi.org/10.1007/s10661-009-1227-8
  3. Ahady, A. B., & Kaplan, G. (2022). Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul. International Journal of Engineering and Geosciences, 7(1), 24–31. https://doi.org/10.26833/ijeg.860077
  4. Dapke, P. P., Quadri, S. A., Nagare, S. M., Bandal, S. B., & Baheti, M. R. (2025). A Comparative Analysis of Machine Learning Techniques for LULC Classification Using Landsat-8 Satellite Imagery. International Journal of Engineering and Geosciences, 10(1), 84–92. https://doi.org/10.26833/ijeg.1503104
  5. Cihat Basara, A., Emin Tabar, M., Gulsun, S., & Sisman, Y. (2022). Monitoring urban sprawl in Atakum district using CORINE data. Advanced Geomatics, 2(2), 49–56. Retrieved from http://publish.mersin.edu.tr/index.php/geomatics/index
  6. Öztürk, M. Y., & Çölkesen, İ. (2021). The impacts of vegetation indices from UAV-based RGB imagery on land cover classification using ensemble learning. Mersin Photogrammetry Journal, 3(2), 41–47. https://doi.org/10.53093/mephoj.943347
  7. Hermosilla, T., Ruiz, L. A., Recio, J. A., & Cambra-López, M. (2012). Assessing contextual descriptive features for plot-based classification of urban areas. Landscape and Urban Planning, 106(1), 124–137. https://doi.org/10.1016/j.landurbplan.2012.02.008
  8. Sun, C., Wu, Z. F., Lv, Z. Q., Yao, N., & Wei, J. B. (2013). Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 21(1), 409–417. https://doi.org/10.1016/j.jag.2011.12.012
  9. Demirel, Y., & Türk, T. (2024). Automatic detection of active fires and burnt areas in forest areas using optical satellite imagery and deep learning methods. Mersin Photogrammetry Journal, 6(2), 66–78. https://doi.org/10.53093/mephoj.1575877
  10. Sapena, M., & Ruiz, L. A. (2015). Analysis of urban development by means of multi-temporal fragmentation metrics from LULC data. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 40, pp. 1411–1418). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprsarchives-XL-7-W3-1411-2015
  11. Yakar, M., & Dogan, Y. (2018, November). 3D Reconstruction of residential areas with SfM photogrammetry. In Conference of the Arabian Journal of Geosciences (pp. 73-75). Cham: Springer International Publishing.
  12. Tian, Y., Yin, K., Lu, D., Hua, L., Zhao, Q., & Wen, M. (2014). Examining land use and land cover spatiotemporal change and driving forces in Beijing from 1978 to 2010. Remote Sensing, 6(11), 10593–10611. https://doi.org/10.3390/rs61110593
  13. EEA. (2006). Urban sprawl in Europe — The ignored challenge. Copenhagen.
  14. EEA. (2011). Analysing and managing urban growth.
  15. Harvey, R. O., & Clark, W. A. V. (1965). The Nature and Economics of Urban Sprawl. Land Economics, 41(1), 1–9.
  16. Atef, I., Ahmed, W., & Abdel-Maguid, R. H. (2023). Modelling of land use land cover changes using machine learning and GIS techniques: a case study in El-Fayoum Governorate, Egypt. Environmental Monitoring and Assessment, 195(6), 637. https://doi.org/10.1007/s10661-023-11224-7
  17. Chettry, V. (2022). Geospatial measurement of urban sprawl using multi-temporal datasets from 1991 to 2021: case studies of four Indian medium-sized cities. Environmental Monitoring and Assessment, 194(12). https://doi.org/10.1007/s10661-022-10542-6
  18. Elburz, Z., & Cubukcu, K. M. (2021). Spatial effects of transport infrastructure on regional growth: the case of Turkey. Spatial Information Research, 29(1), 19–30. https://doi.org/10.1007/s41324-020-00332-y
  19. Abelairas-Etxebarria, P., & Astorkiza, I. (2012). Farmland prices and land-use changes in periurban protected natural areas. Land Use Policy, 29(3), 674–683. https://doi.org/10.1016/j.landusepol.2011.11.003
  20. Fiedeń, Ł. (2019). Changes in land use in the communes crossed by the A4 motorway in Poland. Land Use Policy, 85, 397–406. https://doi.org/10.1016/j.landusepol.2019.04.025
  21. Liang, Y., Zeng, J., Sun, W., Zhou, K., & Zhou, Z. (2021). Expansion of construction land along the motorway in rapidly developing areas in Cambodia. Land Use Policy, 109, 1–15. https://doi.org/10.1016/j.landusepol.2021.105691
  22. Müller, K., Steinmeier, C., & Küchler, M. (2010). Urban growth along motorways in Switzerland. Landscape and Urban Planning, 98(1), 3–12. https://doi.org/10.1016/j.landurbplan.2010.07.004
  23. Villarroya, A., & Puig, J. (2012). Urban and industrial land-use changes alongside motorways within the Pyrenean area of Navarre, Spain. Environmental Engineering and Management Journal, 11(5), 1213–1220. https://doi.org/10.30638/eemj.2012.145
  24. Keken, Z., Sebkova, M., & Skalos, J. (2014). Analyzing land cover change—The impact of the motorway construction and their operation on landscape structure. Journal of Geographic Information System, 06(05), 559–571. https://doi.org/10.4236/jgis.2014.65046
  25. Zheng, Q., He, S., Huang, L., Zheng, X., Pan, Y., Shahtahmassebi, A. R., … Wang, K. (2016). Assessing the impacts of Chinese sustainable ground transportation on the dynamics of urban growth: A case study of the Hangzhou Bay Bridge. Sustainability, 8(666), 1–20. https://doi.org/10.3390/su8070666
  26. Demirel, H., Sertel, E., Kaya, S., & Zafer Seker, D. (2008). Exploring impacts of road transportation on environment: a spatial approach. Desalination, 226(1–3), 279–288. https://doi.org/10.1016/j.desal.2007.02.111
  27. Mothorpe, C., Hanson, A., & Schnier, K. (2013). The impact of interstate highways on land use conversion. Annals of Regional Science, 51(3), 833–870. https://doi.org/10.1007/s00168-013-0564-2
  28. Chi, G. (2010). The impacts of highway expansion on population change: An integrated spatial approach. Rural Sociology, 75(1), 58–89. https://doi.org/10.1111/j.1549-0831.2009.00003.x
  29. Garcia-López, M. àngel. (2012). Urban spatial structure, suburbanization and transportation in Barcelona. Journal of Urban Economics, 72(2–3), 176–190. https://doi.org/10.1016/j.jue.2012.05.003
  30. Duranton, G., & Turner, M. A. (2012). Urban growth and transportation. Review of Economic Studies, 79(4), 1407–1440. https://doi.org/10.1093/restud/rds010
  31. Fan, F., & Fan, W. (2014). Understanding spatial-temporal urban expansion pattern (1990–2009) using impervious surface data and landscape indexes: a case study in Guangzhou (China). Journal of Applied Remote Sensing, 8(1), 083609. https://doi.org/10.1117/1.jrs.8.083609
  32. Godone, D., Garbarino, M., Sibona, E., Garnero, G., & Godone, F. (2014). Progressive fragmentation of a traditional Mediterranean landscape by hazelnut plantations: The impact of CAP over time in the Langhe region (NW Italy). Land Use Policy, 36, 259–266. https://doi.org/10.1016/J.LANDUSEPOL.2013.08.018
  33. Lausch, A., & Herzog, F. (2002). Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecological Indicators, 2(1–2), 3–15. https://doi.org/10.1016/S1470-160X(02)00053-5
  34. Zhou, X., & Wang, Y. C. (2011). Spatial–temporal dynamics of urban green space in response to rapid urbanization and greening policies. Landscape and Urban Planning, 100(3), 268–277. https://doi.org/10.1016/J.LANDURBPLAN.2010.12.013
  35. Xu, C., Liu, M., Zhang, C., An, S., Yu, W., & Chen, J. M. (2007). The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China. Landscape Ecology, 22(6), 925–937. https://doi.org/10.1007/s10980-007-9079-5
  36. Nduwayezu, G. (2015). Modeling Urban Growth in Kigali City Rwanda. University of Twente.
  37. Nas, İ. (2016). Kentleşmenin tarım alanlarına ttkisinin yasal ve yönetsel açıdan irdelenmesi: Denizli örneği. Bartın University.
  38. Shands, W. E. (1991). Problems and prospects at the urban-forest interface. Land uses and expectations are in transition. Journal of Forestry, 89(6), 23–26.
  39. Barnes, K. B., Morgan, J., & Roberge, M. (2002). Sprawl Development: Its Patterns, Consequences, and Measurement.
  40. Wu, J., & David, J. L. (2002). A spatially explicit hierarchical approach to modeling complex ecological systems: theory and applications. Ecological Modelling (Vol. 153).
  41. McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure.
  42. O’neill, R. V, Krummel, J. R., Gardner, R. H., Sugihara, G., Jackson, B., Deangelis, D. L., … Graham, R. L. (1988). Indices of landscape pattern. Landscape Ecology (Vol. 1). SPB Academic Publishing.
  43. Riitters, K. H., O’neill, R. V, Hunsaker, C. T., Wickham, J. D., Yankee, D. H., Timmins, S. P., … Jackson, B. L. (1995). A factor analysis of landscape pattern and structure metrics. Landscape Ecology (Vol. 10).
  44. Uğur Tona, A., Maraş, E. E., & Demir, V. (2023). Determination of densities of existing intersection by buffer analysis (Samsun-Atakum intersections). Advanced GIS (Vol. 3). Retrieved from http://publish.mersin.edu.tr/index.php/agis/index
  45. Noi Phan, T., Kuch, V., & Lehnert, L. W. (2020). Land cover classification using google earth engine and random forest classifier-the role of image composition. Remote Sensing, 12(15), 1–22. https://doi.org/10.3390/RS12152411
  46. Satılmış, H., Çorumluoğlu, Ö., & Akyel, E. (2023). Temporal Change of Göksu River. Advanced Geomatics, 3(2), 72–81. Retrieved from http://publish.mersin.edu.tr/index.php/geomatics/index
  47. Orhan, O., & Yakar, M. (2016). Investigating land surface temperature changes using Landsat data in Konya, Turkey. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41, 285-289.
  48. Şenol, H. İ., Kaya, Y., Yiğit, A. Y., & Yakar, M. (2024). Extraction and geospatial analysis of the Hersek Lagoon shoreline with Sentinel-2 satellite data. Survey Review, 56(397), 367-382.
  49. Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mirzadeh, S. M. J., … Hopkinson, C. (2019). Canadian wetland inventory using Google Earth Engine: The first map and preliminary results. Remote Sensing, 11(7). https://doi.org/10.3390/RS11070842
  50. Akar, Ö., & Tunç Görmüş, E. (2019). Göktürk-2 ve Hyperion EO-1 Uydu Görüntülerinden Rastgele Orman Sınıflandırıcısı ve Destek Vektör Makineleri ile Arazi Kullanım Haritalarının Üretilmesi. Geomatik, 4(1), 68–81. https://doi.org/10.29128/geomatik.476668
  51. Yakar, M. (2009). Digital elevation model generation by robotic total station instrument. Experimental Techniques, 33(2), 52-59.
  52. Shafiq, M., & Mahmood, S. (2022). Spatial assessment of forest cover change in Azad Kashmir, Pakistan. Advanced GIS, 2(2), 62–69.
  53. Ayalke, Z., & Şişman, A. (2024). Google Earth Engine kullanılarak makine öğrenmesi tabanlı iyileştirilmiş arazi örtüsü sınıflandırması: Atakum, Samsun örneği. Geomatik, 9(3), 375–390. https://doi.org/10.29128/geomatik.1472160
  54. Efe, E., & Algancı, U. (2023). Çok zamanlı Sentinel 2 uydu görüntüleri ve makine öğrenmesi tabanlı algoritmalar ile arazi örtüsü değişiminin belirlenmesi. Geomatik, 8(1), 27–34. https://doi.org/10.29128/geomatik.1092838
  55. Cihlar, J. (2000). Land cover mapping of large areas from satellites: Status and research priorities. International Journal of Remote Sensing, 21(6–7), 1093–1114. https://doi.org/10.1080/014311600210092
  56. Jin, Y., Liu, X., Chen, Y., & Liang, X. (2018). Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: a case study of central Shandong. International Journal of Remote Sensing, 39(23), 8703–8723. https://doi.org/10.1080/01431161.2018.1490976
  57. Li, X., Chen, W., Cheng, X., & Wang, L. (2016). A comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery. Remote Sensing, 8(6). https://doi.org/10.3390/rs8060514
  58. Matarira, D., Mutanga, O., & Naidu, M. (2022). Google Earth Engine for Informal Settlement Mapping: A Random Forest Classification Using Spectral and Textural Information. Remote Sensing, 14(20). https://doi.org/10.3390/rs14205130
  59. Millard, K., & Richardson, M. (2015). On the importance of training data sample selection in Random Forest image classification: A case study in peatland ecosystem mapping. Remote Sensing, 7(7), 8489–8515. https://doi.org/10.3390/rs70708489
  60. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
  61. Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., … Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 325–340. https://doi.org/10.1016/j.isprsjprs.2018.07.017
  62. El-Bouhali, A., Amyay, M., & Ech-Chahdi, K. E. O. (2025). Detection of irrigated crops using Landsat 8 images: A methodology based on fieldwork and spectral reflectance analysis. International Journal of Engineering and Geosciences, 10(1), 1–13. https://doi.org/10.26833/ijeg.1483206
  63. Paul, S. (2022). Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study. International Journal of Engineering and Geosciences, 7(2), 191–207. https://doi.org/10.26833/ijeg.975222
  64. Kaya, Y., Şenol, H. İ., Yiğit, A. Y., & Yakar, M. (2023). Car detection from very high-resolution UAV images using deep learning algorithms. Photogrammetric Engineering & Remote Sensing, 89(2), 117-123.
  65. Kasraian, D., Maat, K., Stead, D., & Van Wee, B. (2016). Long-term impacts of transport infrastructure networks on land-use change: an international review of empirical studies. Transport Reviews, 36(6), 772–792. https://doi.org/10.1080/01441647.2016.1168887