Araştırma Makalesi
BibTex RIS Kaynak Göster

Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective

Yıl 2025, Cilt: 13 Sayı: 1, 286 - 298, 30.01.2025
https://doi.org/10.29130/dubited.1462304

Öz

As urbanization continues to increase, the concerns about sustainable land use and management are also growing. Thus, there has been an increasing number of scientific research studies conducted on this phenomenon. In many cases, these studies have been performed by using expensive remote sensing software with low-resolution (>30m) Landsat imagery. On the other hand, Google Earth Pro imageries have a geometric resolution of 1.5 meters to 2 meters for most of the covered areas in the world. The objective of this study is to investigate changes in land use in the Kemalöz District of Uşak, Türkiye, which is the fastest-growing district, by utilizing Google Earth Pro and GIS for rapid assessment of land use change between 2005 and 2024, offering an alternative to costly remote sensing software and low-resolution Landsat imagery. The study also aims to evaluate the relative benefits of integrating geoscience to analyze land-use changes to provide insights to policymakers and local officials to make informed decisions about the most effective way to manage land to mitigate the negative effects of urbanization. The findings of this study indicated that the build-up land use has increased from 1,734 to 2,755 km2 from 2005 to 2024. Vegetation land use has increased from 1,081 to 1,392 km2 between 2005 and 2024. Agricultural land use has decreased from 1,781 to 1,149 km2 between 2005 and 2024. Barren land use has decreased from 1,803 to 1,103 km2. This suggests significant urban development or infrastructure expansion occurred in the study area over the 19 years.

Kaynakça

  • [1] United Nations, Department of Economic and Social Affairs, and Population Division. "World Urbanization Prospects 2018: Highlights (ST/ESA/SER. A/421)." (2019).
  • [2] Fenoglio, M. S., Rossetti, M. R., & Videla, M. (2020). Negative effects of urbanization on terrestrial arthropod communities: A meta‐analysis. Global Ecology and Biogeography, 29(8), 1412-1429.
  • [3] Herrero-Jáuregui, C., & Concepción, E. D. (2023). Effects of counter-urbanization on Mediterranean rural landscapes. Landscape Ecology, 38(12), 3695-3711.
  • [4] Cheng, Z., & Hu, X. (2023). The effects of urbanization and urban sprawl on CO2 emissions in China. Environment, Development and Sustainability, 25(2), 1792-1808.
  • [5] Menashe‐Oren, A., & Bocquier, P. (2021). Urbanization is no longer driven by migration in low‐and middle‐income countries (1985–2015). Population and Development Review, 47(3), 639-663.
  • [6] Asadzadeh, A., Kötter, T., Fekete, A., Moghadas, M., Alizadeh, M., Zebardast, E., ... & Hutter, G. (2022). Urbanization, migration, and the challenges of resilience thinking in urban planning: Insights from two contrasting planning systems in Germany and Iran. Cities, 125, 103642.
  • [7] Golding, S. A., & Winkler, R. L. (2020). Tracking urbanization and exurbs: Migration across the rural–urban continuum, 1990–2016. Population research and policy review, 39(5), 835-859.
  • [8] Antrop, M. (2004). Landscape change and the urbanization process in Europe. Landscape and Urban Planning, 67(1-4), 9-26.
  • [9] Melchiorri, M., Florczyk, A. J., Freire, S., Schiavina, M., Pesaresi, M., & Kemper, T. (2018). Unveiling 25 years of planetary urbanization with remote sensing: Perspectives from the global human settlement layer. Remote Sensing, 10(5), 768.
  • [10] Song, X., Feng, Q., Xia, F., Li, X., & Scheffran, J. (2021). Impacts of changing urban land-use structure on sustainable city growth in China: A population-density dynamics perspective. Habitat International, 107, 102296.
  • [11] Haase, D., Kabisch, N., & Haase, A. (2013). Endless urban growth? On the mismatch of population, household and urban land area growth and its effects on the urban debate. PloS One, 8(6), e66531.
  • [12] Wolff, M., Haase, D., & Haase, A. (2018). Compact or spread? A quantitative spatial model of urban areas in Europe since 1990. PloS One, 13(2), e0192326.
  • [13] MohanRajan, S. N., Loganathan, A., & Manoharan, P. (2020). Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges. Environmental Science and Pollution Research, 27(24), 29900-29926.
  • [14] Zengin, E. (2023). A Combined Assessment of Sea Level Rise (SLR) Effect on Antalya Gulf (Türkiye) and Future Predictions on Land Loss. Journal of the Indian Society of Remote Sensing, 51(5), 1121-1133.
  • [15] Acar, R. U., & Zengin, E. (2023). Performance Assessment of Landsat 8 and Sentinel-2 Satellite Images for the Production of Time Series Land Use/Land Cover (Lulc) Maps. Journal of Scientific Reports-A, (053), 1-15.
  • [16] Yildiz, U., & Ozkul, C. (2024). Heavy metals contamination and ecological risks in agricultural soils of Uşak, western Türkiye: a geostatistical and multivariate analysis. Environmental Geochemistry and Health, 46(2), 58.
  • [17] Yildiz, U., & Ozkul, C. (2022). Spatial distribution and ecological risk assessment of heavy metals contamination of urban soils within Uşak, western Turkiye. International Journal of Environmental Analytical Chemistry, 1-23.
  • [18] Acar, R. U., & Özkul, C. (2020). Investigation of heavy metal pollution in roadside soils and road dusts along the Kütahya–Eskişehir Highway. Arabian Journal of Geosciences, 13(5), 216.
  • [19] Zengin, E. (2023). Inundation risk assessment of Eastern Mediterranean Coastal archaeological and historical sites of Türkiye and Greece. Environmental Monitoring and Assessment, 195(8), 968.
  • [20] Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote sensing of Environment, 175, 205-214.
  • [21] Ha, T. V., Tuohy, M., Irwin, M., & Tuan, P. V. (2020). Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 11-19.
  • [22] Frimpong, B. F., & Molkenthin, F. (2021). Tracking urban expansion using random forests for the classification of landsat imagery (1986–2015) and predicting urban/built-up areas for 2025: A Study of the Kumasi Metropolis, Ghana. Land, 10(1), 44.
  • [23] Desktop, Google Earth Pro, Release 7.3.6, Google L.L.C., Mountain View, California 94043 USA, 2022. [24] Wu, H., Lin, A., Xing, X., Song, D., & Li, Y. (2021). Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method. International Journal of Applied Earth Observation and Geoinformation, 103, 102475.
  • [25] Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., & Mi, J. (2020). GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data Discussions, 2020, 1-31. [26] Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of high-resolution Google Earth satellite imagery in land-use map preparation for urban-related applications. Procedia Technology, 24, 1835-1842.
  • [27] Leachtenauer, J.C., Malila, W., Irvine, J., Colburn, L. and Salvaggio, N. (1997). General image-quality equation: GIQE. Applied Optics, 36(32), pp.8322-8328.
  • [28] Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., & Xue, P. (2020). Land use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on Geographical Detectors and Google Earth Engine: A case study in Gannan Prefecture. Remote Sensing, 12(19), 3139.
  • [29] Floreano, I. X., & de Moraes, L. A. F. (2021). Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment, 193(4), 239.
  • [30] Cui, J., Zhu, M., Liang, Y., Qin, G., Li, J., & Liu, Y. (2022). Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on Google Earth Engine from 2000 to 2020. ISPRS International Journal of Geo-Information, 11(3), 163.
  • [31] Feizizadeh, B., Omarzadeh, D., Kazemi Garajeh, M., Lakes, T., & Blaschke, T. (2023). Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine. Journal of Environmental Planning and Management, 66(3), 665-697.
  • [32] Zhao, Z., Islam, F., Waseem, L.A., Tariq, A., Nawaz, M., Islam, I.U., Bibi, T., Rehman, N.U., Ahmad, W., Aslam, R.W. and Raza, D., 2024. Comparison of three machine learning algorithms using Google Earth engine for land use land cover classification. Rangeland Ecology & Management, 92, pp.129-137.
  • [33] Chen, H., Li, D., Chen, Y., & Zhao, Z. (2023). Spatial-temporal evolution monitoring and ecological risk assessment of coastal wetlands on Hainan island, China. Remote Sensing, 15(4), 1035.
  • [34] Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated.
  • [35] Ercan, T., Dincel, A., Metin, S., Turkecan, A., & Gunay, E. (1978). Geology of Usak. Bulletin of Geological Society of Turkiye. 21, 97. [36] Desktop, ESRI ArcGIS, Release 10.7. 1, Environmental Systems Research Institute, Redlands, CA, USA, 2019.

Hızlı Kentselleşmenin Arazi Kullanım Dinamikleri Üzerindeki Değerlendirmesi: Kemalöz Mahallesi, Uşak, Türkiye'de Bir Google Earth ve CBS Yaklaşımı - Bir Yer Bilimleri Perspektifi

Yıl 2025, Cilt: 13 Sayı: 1, 286 - 298, 30.01.2025
https://doi.org/10.29130/dubited.1462304

Öz

Kentselleşmenin artmasıyla birlikte, sürdürülebilir arazi kullanımı ve yönetimine yönelik endişeler de artmaktadır. Bu nedenle, bu fenomen üzerine yapılan bilimsel araştırmaların sayısı artmaktadır. Birçok durumda, bu çalışmalar, maliyetli uzaktan algılama yazılımları ve düşük çözünürlüklü (>30m) Landsat görüntüleri kullanılarak gerçekleştirilmiştir. Öte yandan, Google Earth Pro görüntüleri, dünya genelindeki çoğu alan için 1,5 metreden 2 metreye kadar geometrik çözünürlüğe sahiptir. Bu çalışmanın amacı, Türkiye'nin Uşak şehrinin en hızlı büyüyen mahallesi olan Kemalöz’ün arazi kullanımındaki değişiklikleri incelemektir. Bu inceleme, 2005 ile 2024 arasındaki arazi kullanım değişikliklerini hızlı bir şekilde değerlendirmek için Google Earth Pro ve Coğrafi Bilgi Sistemi (GIS) kullanarak, maliyetli uzaktan algılama yazılımlarına ve düşük çözünürlüklü Landsat görüntülerine alternatif sunmaktadır. Çalışma ayrıca, arazi kullanımı değişikliklerini analiz etmek için yer bilimlerini entegre etmenin göreli faydalarını değerlendirmeyi amaçlamaktadır, böylece karar vericilere ve yerel yetkililere kentselleşmenin negatif etkilerini azaltmak için araziyi en etkili şekilde yönetme konusunda bilgi vermektedir. Bu çalışmanın bulguları, inceleme alanında 19 yıl boyunca önemli bir kentsel gelişme veya altyapı genişlemesi olduğunu göstermektedir. 2005 ile 2024 arasında yapılan inceleme, yapılaşma alanının 1.734'ten 2.755 km2'ye, bitki örtüsü kullanım alanının ise 1.081'den 1.392 km2'ye arttığını göstermiştir. Tarım alanı kullanımının ise 2005 ile 2024 arasında 1.781'den 1.149 km2'ye azaldığı tespit edilmiştir. Kullanılmayan arazi alanının ise 1.803'ten 1.103 km2'ye azaldığı görülmektedir. Bu sonuçlar, inceleme alanında önemli bir kentsel gelişme veya altyapı genişlemesi olduğunu göstermektedir.

Teşekkür

The author expresses his sincere gratitude to the South Dakota School of Mines for generously providing access to their ArcGIS license, which was instrumental in conducting this study.

Kaynakça

  • [1] United Nations, Department of Economic and Social Affairs, and Population Division. "World Urbanization Prospects 2018: Highlights (ST/ESA/SER. A/421)." (2019).
  • [2] Fenoglio, M. S., Rossetti, M. R., & Videla, M. (2020). Negative effects of urbanization on terrestrial arthropod communities: A meta‐analysis. Global Ecology and Biogeography, 29(8), 1412-1429.
  • [3] Herrero-Jáuregui, C., & Concepción, E. D. (2023). Effects of counter-urbanization on Mediterranean rural landscapes. Landscape Ecology, 38(12), 3695-3711.
  • [4] Cheng, Z., & Hu, X. (2023). The effects of urbanization and urban sprawl on CO2 emissions in China. Environment, Development and Sustainability, 25(2), 1792-1808.
  • [5] Menashe‐Oren, A., & Bocquier, P. (2021). Urbanization is no longer driven by migration in low‐and middle‐income countries (1985–2015). Population and Development Review, 47(3), 639-663.
  • [6] Asadzadeh, A., Kötter, T., Fekete, A., Moghadas, M., Alizadeh, M., Zebardast, E., ... & Hutter, G. (2022). Urbanization, migration, and the challenges of resilience thinking in urban planning: Insights from two contrasting planning systems in Germany and Iran. Cities, 125, 103642.
  • [7] Golding, S. A., & Winkler, R. L. (2020). Tracking urbanization and exurbs: Migration across the rural–urban continuum, 1990–2016. Population research and policy review, 39(5), 835-859.
  • [8] Antrop, M. (2004). Landscape change and the urbanization process in Europe. Landscape and Urban Planning, 67(1-4), 9-26.
  • [9] Melchiorri, M., Florczyk, A. J., Freire, S., Schiavina, M., Pesaresi, M., & Kemper, T. (2018). Unveiling 25 years of planetary urbanization with remote sensing: Perspectives from the global human settlement layer. Remote Sensing, 10(5), 768.
  • [10] Song, X., Feng, Q., Xia, F., Li, X., & Scheffran, J. (2021). Impacts of changing urban land-use structure on sustainable city growth in China: A population-density dynamics perspective. Habitat International, 107, 102296.
  • [11] Haase, D., Kabisch, N., & Haase, A. (2013). Endless urban growth? On the mismatch of population, household and urban land area growth and its effects on the urban debate. PloS One, 8(6), e66531.
  • [12] Wolff, M., Haase, D., & Haase, A. (2018). Compact or spread? A quantitative spatial model of urban areas in Europe since 1990. PloS One, 13(2), e0192326.
  • [13] MohanRajan, S. N., Loganathan, A., & Manoharan, P. (2020). Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges. Environmental Science and Pollution Research, 27(24), 29900-29926.
  • [14] Zengin, E. (2023). A Combined Assessment of Sea Level Rise (SLR) Effect on Antalya Gulf (Türkiye) and Future Predictions on Land Loss. Journal of the Indian Society of Remote Sensing, 51(5), 1121-1133.
  • [15] Acar, R. U., & Zengin, E. (2023). Performance Assessment of Landsat 8 and Sentinel-2 Satellite Images for the Production of Time Series Land Use/Land Cover (Lulc) Maps. Journal of Scientific Reports-A, (053), 1-15.
  • [16] Yildiz, U., & Ozkul, C. (2024). Heavy metals contamination and ecological risks in agricultural soils of Uşak, western Türkiye: a geostatistical and multivariate analysis. Environmental Geochemistry and Health, 46(2), 58.
  • [17] Yildiz, U., & Ozkul, C. (2022). Spatial distribution and ecological risk assessment of heavy metals contamination of urban soils within Uşak, western Turkiye. International Journal of Environmental Analytical Chemistry, 1-23.
  • [18] Acar, R. U., & Özkul, C. (2020). Investigation of heavy metal pollution in roadside soils and road dusts along the Kütahya–Eskişehir Highway. Arabian Journal of Geosciences, 13(5), 216.
  • [19] Zengin, E. (2023). Inundation risk assessment of Eastern Mediterranean Coastal archaeological and historical sites of Türkiye and Greece. Environmental Monitoring and Assessment, 195(8), 968.
  • [20] Fu, P., & Weng, Q. (2016). A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery. Remote sensing of Environment, 175, 205-214.
  • [21] Ha, T. V., Tuohy, M., Irwin, M., & Tuan, P. V. (2020). Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam. The Egyptian Journal of Remote Sensing and Space Science, 23(1), 11-19.
  • [22] Frimpong, B. F., & Molkenthin, F. (2021). Tracking urban expansion using random forests for the classification of landsat imagery (1986–2015) and predicting urban/built-up areas for 2025: A Study of the Kumasi Metropolis, Ghana. Land, 10(1), 44.
  • [23] Desktop, Google Earth Pro, Release 7.3.6, Google L.L.C., Mountain View, California 94043 USA, 2022. [24] Wu, H., Lin, A., Xing, X., Song, D., & Li, Y. (2021). Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method. International Journal of Applied Earth Observation and Geoinformation, 103, 102475.
  • [25] Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., & Mi, J. (2020). GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data Discussions, 2020, 1-31. [26] Malarvizhi, K., Kumar, S. V., & Porchelvan, P. (2016). Use of high-resolution Google Earth satellite imagery in land-use map preparation for urban-related applications. Procedia Technology, 24, 1835-1842.
  • [27] Leachtenauer, J.C., Malila, W., Irvine, J., Colburn, L. and Salvaggio, N. (1997). General image-quality equation: GIQE. Applied Optics, 36(32), pp.8322-8328.
  • [28] Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., & Xue, P. (2020). Land use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on Geographical Detectors and Google Earth Engine: A case study in Gannan Prefecture. Remote Sensing, 12(19), 3139.
  • [29] Floreano, I. X., & de Moraes, L. A. F. (2021). Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment, 193(4), 239.
  • [30] Cui, J., Zhu, M., Liang, Y., Qin, G., Li, J., & Liu, Y. (2022). Land use/land cover change and their driving factors in the Yellow River Basin of Shandong Province based on Google Earth Engine from 2000 to 2020. ISPRS International Journal of Geo-Information, 11(3), 163.
  • [31] Feizizadeh, B., Omarzadeh, D., Kazemi Garajeh, M., Lakes, T., & Blaschke, T. (2023). Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine. Journal of Environmental Planning and Management, 66(3), 665-697.
  • [32] Zhao, Z., Islam, F., Waseem, L.A., Tariq, A., Nawaz, M., Islam, I.U., Bibi, T., Rehman, N.U., Ahmad, W., Aslam, R.W. and Raza, D., 2024. Comparison of three machine learning algorithms using Google Earth engine for land use land cover classification. Rangeland Ecology & Management, 92, pp.129-137.
  • [33] Chen, H., Li, D., Chen, Y., & Zhao, Z. (2023). Spatial-temporal evolution monitoring and ecological risk assessment of coastal wetlands on Hainan island, China. Remote Sensing, 15(4), 1035.
  • [34] Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World map of the Köppen-Geiger climate classification updated.
  • [35] Ercan, T., Dincel, A., Metin, S., Turkecan, A., & Gunay, E. (1978). Geology of Usak. Bulletin of Geological Society of Turkiye. 21, 97. [36] Desktop, ESRI ArcGIS, Release 10.7. 1, Environmental Systems Research Institute, Redlands, CA, USA, 2019.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Klasik Fizik (Diğer)
Bölüm Makaleler
Yazarlar

Ümit Yıldız 0000-0002-3843-7203

Yayımlanma Tarihi 30 Ocak 2025
Gönderilme Tarihi 31 Mart 2024
Kabul Tarihi 11 Ekim 2024
Yayımlandığı Sayı Yıl 2025 Cilt: 13 Sayı: 1

Kaynak Göster

APA Yıldız, Ü. (2025). Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective. Duzce University Journal of Science and Technology, 13(1), 286-298. https://doi.org/10.29130/dubited.1462304
AMA Yıldız Ü. Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective. DÜBİTED. Ocak 2025;13(1):286-298. doi:10.29130/dubited.1462304
Chicago Yıldız, Ümit. “Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective”. Duzce University Journal of Science and Technology 13, sy. 1 (Ocak 2025): 286-98. https://doi.org/10.29130/dubited.1462304.
EndNote Yıldız Ü (01 Ocak 2025) Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective. Duzce University Journal of Science and Technology 13 1 286–298.
IEEE Ü. Yıldız, “Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective”, DÜBİTED, c. 13, sy. 1, ss. 286–298, 2025, doi: 10.29130/dubited.1462304.
ISNAD Yıldız, Ümit. “Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective”. Duzce University Journal of Science and Technology 13/1 (Ocak 2025), 286-298. https://doi.org/10.29130/dubited.1462304.
JAMA Yıldız Ü. Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective. DÜBİTED. 2025;13:286–298.
MLA Yıldız, Ümit. “Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective”. Duzce University Journal of Science and Technology, c. 13, sy. 1, 2025, ss. 286-98, doi:10.29130/dubited.1462304.
Vancouver Yıldız Ü. Assessment of Rapid Urbanization Effects on Land Use Dynamics: A Google Earth and GIS Approach in Kemalöz District, Uşak, Türkiye - An Earth Science Perspective. DÜBİTED. 2025;13(1):286-98.