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

Yıl 2022, Sayı: 45, 437 - 453, 25.01.2022
https://doi.org/10.32003/igge.1007780

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.

Kaynakça

  • Abass, K., Adanu, S. K., & Agyemang, S. (2018). Peri-urbanisation and loss of arable land in Kumasi Metropolis in three decades: Evidence from remote sensing image analysis. Land Use Policy, 72(1), 470-479.
  • Ahmad, A., & Quegan, S. (2012). Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences, 6(129), 6425-6436.
  • Akinyemi, F. O., & Mashame, G. (2018). Analysis of land change in the dryland agricultural landscapes of eastern Botswana. Land Use Policy, 76(1), 798-811.
  • Ankara Metropolitan Municipality. (2006). 2023 Capital Ankara Master Plan Disclosure Report, Ankara Metropolitan Municipality Directorate of Construction and Urbanization, Ankara.
  • Banerjee, R., & Srivastava, P. K. (2013). Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing. Land Use Policy, 34(1), 193-203.
  • Baraldi, A., Despini, F., & Teggi, S. (2017). Multi-spectral image panchromatic sharpening, outcome and process quality assessment protocol. Computer Vision and Pattern Recognition, 1-39.
  • Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114 (1), 24-31.
  • Bolstad, P., & Lillesand, T. M. (1991). Rapid maximum likelihood classification. Photogrammetric Engineering and Remote Sensing, 57(1), 67-74.
  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  • Caldas, M. M., Goodin, D., Sherwood, S., Campos Krauer, J. M., & Wisely, S. M. (2015). Land-cover change in the Paraguayan Chaco: 2000-2011. Journal of Land Use Science, 10(1), 1-18.
  • Canaz, S., Aliefendioğlu, Y., & Tanrıvermiş, H. (2017). Change detection using Landsat images and an analysis of the linkages between the change and property tax values in the Istanbul province of Turkey. Journal of Environmental Management, 200, 446-455.
  • Chamling, M., & Bera, B. (2020). Spatio-temporal patterns of land use/land cover change in the Bhutan-Bengal foothill region between 1987 and 2019: Study towards geospatial applications and policy making. Earth Systems and Environment, 4 (1), 1-14.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • Deng, X., Huang, J., Rozelle, S., & Uchida, E. (2008). Growth, population and industrialization and urban land expansion of China. Journal of Urban Economics, 63(1), 96-115.
  • ERDAS Field Guide (1999). University of Massachusetts, Amherst, MA, USA. Erdas Inc., Atlanta, Georgia. Technical Report 92-8. http://faculty.une.edu/cas/szeeman/rs/docs/FieldGuide.pdf.
  • Esri Online, (2019) Training manual, 19 March 2019 retrieved from https://www.esri.com/.
  • General Directorate of Cadastre and Land Registration, (2019) Classification of cadastral information and land resources, 19 March 2019 retrieved from https://www.tkgm.gov.tr/tr.
  • Gómez, D., & Montero, J. (2011). Determining the accuracy in image supervised classification problems. Advances in Intelligent Systems Research, 1(1), 342-349.
  • Heinl, M., & Tappeiner, U. (2012). The benefits of considering land cover seasonality in multi-spectral image classification. Journal of Land Use Science, 7(1), 1-19.
  • Ismail, M. H., & Jusoff, K. (2008). Satellite data classification accuracy assessment based from reference dataset. International Journal of Computer and Information Science and Engineering, 2(2), 96-102.
  • Jhonnerie, R., Siregar, V. P., Nababan, B., Prasetyo, L. B., & Wouthuyzen, S. (2015). Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24, 215-221.
  • Jiyuan, L., Mingliang, L., Xiangzheng, D., Dafang, Z., Zengxiang, Z., & Di, L. (2002). The land use and land cover change database and its relative studies in China. Journal of Geographical Sciences, 12(3), 275-282.
  • Korah, P. I., Nunbogu, A. M., & Akanbang, B. A. A. (2018). Spatio-temporal dynamics and livelihoods transformation in Wa, Ghana. Land Use policy, 77, 174-185.
  • Masek, J. G., Lindsay, F. E., & Goward, S. N. (2000). Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations. International Journal of Remote Sensing, 21(18), 3473-3486.
  • Matsuoka, M. (2012). The influence of spectral wavelength on the quality of pansharpened image simulated using hyperspectral data. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 297-302.
  • Mialhe, F., Gunnell, Y., Navratil, O., Choi, D., Sovann, C., Lejot, J., Gaudou, B., & Landon, N. (2019). Spatial growth of Phnom Penh, Cambodia (1973-2015): Patterns, rates, and socio-ecological consequences. Land Use Policy, 87, 104061.
  • Mundia, C. N., & Aniya, M. (2005). Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. International journal of Remote sensing, 26(13), 2831-2849.
  • Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
  • Salvati, L., & Sabbi, A. (2011). Exploring long-term land cover changes in an urban region of southern Europe. International Journal of Sustainable Development & World Ecology, 18(4), 273-282.
  • Schneider, A. (2012). Monitoring land cover change in urban and peri- urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124, 689-704.
  • Shi, G., Jiang, N., & Yao, L. (2018). Land use and cover change during the rapid economic growth period from 1990 to 2010: A case study of Shanghai. Sustainability, 10(2), 426-438.
  • Şerefoğlu, C. (2021). A new definition of ‘rural areas’ for the metropolitan city of Ankara, Turkey. International Journal of Geography and Geography Education (IGGE), 44, 272-281.
  • Tan, K. C., San Lim, H., MatJafri, M. Z., & Abdullah, K. (2010). Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60(7), 1509-1521.
  • Töre, E. Ö., & Som, S. K. (2009). Sosyo-mekânsal ayrışmada korunaklı konut yerleşmeleri: İstanbul örneği. Megaron Yıldız Teknik Üniversitesi Mimarlık Fakültesi E-Dergisi, 4(3), 121-130.
  • TurkStat, (2019) Turkish Statistical Institute Database. Retrived from http://www.tuik.gov.tr/Start.do.
  • Xian, G., Homer, C., & Fry, J. (2009). Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of Environment, 113(6), 1133-1147.
  • Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2006). Evaluating urban expansion and land use change in Shijiazhuang, China by using GIS and remote sensing. Landscape and Urban Planning, 75(1-2), 69-80.
  • Xu, J., Li, G., & Chen, G. (2012). Driving force analysis of land use change based on Logistic regression model in mining area. Transactions of the Chinese Society of Agricultural Engineering, 28(20), 247-255.
  • Yang, L., Xian, G., Klaver, J. M., & Deal, B. (2003). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering & Remote Sensing, 69(9), 1003-1010.
  • Yang, X. (2002). Satellite monitoring of urban spatial growth in the Atlanta metropolitan area. Photogrammetric Engineering and Remote Sensing, 68(7), 725-734.
  • Yang, X., & Lo, C. P. (2002). Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23(9), 1775-1798.
  • Yin, J., Yin, Z., Zhong, H., Xu, S., Hu, X., Wang, J., & Wu, J. (2011). Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environmental Monitoring and Assessment, 177(1), 609-621.
  • Yu, X., Zhang, A., Hou, X., Li, M., & Xia, Y. (2013). Multi-temporal remote sensing of land cover change and urban sprawl in the coastal city of Yantai, China. International Journal of Digital Earth, 6(2), 137-154.
  • Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the twin cities (Minnesota) Metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2-3), 317-328.

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

Yıl 2022, Sayı: 45, 437 - 453, 25.01.2022
https://doi.org/10.32003/igge.1007780

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.

Kaynakça

  • Abass, K., Adanu, S. K., & Agyemang, S. (2018). Peri-urbanisation and loss of arable land in Kumasi Metropolis in three decades: Evidence from remote sensing image analysis. Land Use Policy, 72(1), 470-479.
  • Ahmad, A., & Quegan, S. (2012). Analysis of maximum likelihood classification on multispectral data. Applied Mathematical Sciences, 6(129), 6425-6436.
  • Akinyemi, F. O., & Mashame, G. (2018). Analysis of land change in the dryland agricultural landscapes of eastern Botswana. Land Use Policy, 76(1), 798-811.
  • Ankara Metropolitan Municipality. (2006). 2023 Capital Ankara Master Plan Disclosure Report, Ankara Metropolitan Municipality Directorate of Construction and Urbanization, Ankara.
  • Banerjee, R., & Srivastava, P. K. (2013). Reconstruction of contested landscape: Detecting land cover transformation hosting cultural heritage sites from Central India using remote sensing. Land Use Policy, 34(1), 193-203.
  • Baraldi, A., Despini, F., & Teggi, S. (2017). Multi-spectral image panchromatic sharpening, outcome and process quality assessment protocol. Computer Vision and Pattern Recognition, 1-39.
  • Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114 (1), 24-31.
  • Bolstad, P., & Lillesand, T. M. (1991). Rapid maximum likelihood classification. Photogrammetric Engineering and Remote Sensing, 57(1), 67-74.
  • Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  • Caldas, M. M., Goodin, D., Sherwood, S., Campos Krauer, J. M., & Wisely, S. M. (2015). Land-cover change in the Paraguayan Chaco: 2000-2011. Journal of Land Use Science, 10(1), 1-18.
  • Canaz, S., Aliefendioğlu, Y., & Tanrıvermiş, H. (2017). Change detection using Landsat images and an analysis of the linkages between the change and property tax values in the Istanbul province of Turkey. Journal of Environmental Management, 200, 446-455.
  • Chamling, M., & Bera, B. (2020). Spatio-temporal patterns of land use/land cover change in the Bhutan-Bengal foothill region between 1987 and 2019: Study towards geospatial applications and policy making. Earth Systems and Environment, 4 (1), 1-14.
  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46.
  • Deng, X., Huang, J., Rozelle, S., & Uchida, E. (2008). Growth, population and industrialization and urban land expansion of China. Journal of Urban Economics, 63(1), 96-115.
  • ERDAS Field Guide (1999). University of Massachusetts, Amherst, MA, USA. Erdas Inc., Atlanta, Georgia. Technical Report 92-8. http://faculty.une.edu/cas/szeeman/rs/docs/FieldGuide.pdf.
  • Esri Online, (2019) Training manual, 19 March 2019 retrieved from https://www.esri.com/.
  • General Directorate of Cadastre and Land Registration, (2019) Classification of cadastral information and land resources, 19 March 2019 retrieved from https://www.tkgm.gov.tr/tr.
  • Gómez, D., & Montero, J. (2011). Determining the accuracy in image supervised classification problems. Advances in Intelligent Systems Research, 1(1), 342-349.
  • Heinl, M., & Tappeiner, U. (2012). The benefits of considering land cover seasonality in multi-spectral image classification. Journal of Land Use Science, 7(1), 1-19.
  • Ismail, M. H., & Jusoff, K. (2008). Satellite data classification accuracy assessment based from reference dataset. International Journal of Computer and Information Science and Engineering, 2(2), 96-102.
  • Jhonnerie, R., Siregar, V. P., Nababan, B., Prasetyo, L. B., & Wouthuyzen, S. (2015). Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, 24, 215-221.
  • Jiyuan, L., Mingliang, L., Xiangzheng, D., Dafang, Z., Zengxiang, Z., & Di, L. (2002). The land use and land cover change database and its relative studies in China. Journal of Geographical Sciences, 12(3), 275-282.
  • Korah, P. I., Nunbogu, A. M., & Akanbang, B. A. A. (2018). Spatio-temporal dynamics and livelihoods transformation in Wa, Ghana. Land Use policy, 77, 174-185.
  • Masek, J. G., Lindsay, F. E., & Goward, S. N. (2000). Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations. International Journal of Remote Sensing, 21(18), 3473-3486.
  • Matsuoka, M. (2012). The influence of spectral wavelength on the quality of pansharpened image simulated using hyperspectral data. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 297-302.
  • Mialhe, F., Gunnell, Y., Navratil, O., Choi, D., Sovann, C., Lejot, J., Gaudou, B., & Landon, N. (2019). Spatial growth of Phnom Penh, Cambodia (1973-2015): Patterns, rates, and socio-ecological consequences. Land Use Policy, 87, 104061.
  • Mundia, C. N., & Aniya, M. (2005). Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. International journal of Remote sensing, 26(13), 2831-2849.
  • Otukei, J. R., & Blaschke, T. (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, S27-S31.
  • Salvati, L., & Sabbi, A. (2011). Exploring long-term land cover changes in an urban region of southern Europe. International Journal of Sustainable Development & World Ecology, 18(4), 273-282.
  • Schneider, A. (2012). Monitoring land cover change in urban and peri- urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sensing of Environment, 124, 689-704.
  • Shi, G., Jiang, N., & Yao, L. (2018). Land use and cover change during the rapid economic growth period from 1990 to 2010: A case study of Shanghai. Sustainability, 10(2), 426-438.
  • Şerefoğlu, C. (2021). A new definition of ‘rural areas’ for the metropolitan city of Ankara, Turkey. International Journal of Geography and Geography Education (IGGE), 44, 272-281.
  • Tan, K. C., San Lim, H., MatJafri, M. Z., & Abdullah, K. (2010). Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60(7), 1509-1521.
  • Töre, E. Ö., & Som, S. K. (2009). Sosyo-mekânsal ayrışmada korunaklı konut yerleşmeleri: İstanbul örneği. Megaron Yıldız Teknik Üniversitesi Mimarlık Fakültesi E-Dergisi, 4(3), 121-130.
  • TurkStat, (2019) Turkish Statistical Institute Database. Retrived from http://www.tuik.gov.tr/Start.do.
  • Xian, G., Homer, C., & Fry, J. (2009). Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods. Remote Sensing of Environment, 113(6), 1133-1147.
  • Xiao, J., Shen, Y., Ge, J., Tateishi, R., Tang, C., Liang, Y., & Huang, Z. (2006). Evaluating urban expansion and land use change in Shijiazhuang, China by using GIS and remote sensing. Landscape and Urban Planning, 75(1-2), 69-80.
  • Xu, J., Li, G., & Chen, G. (2012). Driving force analysis of land use change based on Logistic regression model in mining area. Transactions of the Chinese Society of Agricultural Engineering, 28(20), 247-255.
  • Yang, L., Xian, G., Klaver, J. M., & Deal, B. (2003). Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering & Remote Sensing, 69(9), 1003-1010.
  • Yang, X. (2002). Satellite monitoring of urban spatial growth in the Atlanta metropolitan area. Photogrammetric Engineering and Remote Sensing, 68(7), 725-734.
  • Yang, X., & Lo, C. P. (2002). Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23(9), 1775-1798.
  • Yin, J., Yin, Z., Zhong, H., Xu, S., Hu, X., Wang, J., & Wu, J. (2011). Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979–2009) in China. Environmental Monitoring and Assessment, 177(1), 609-621.
  • Yu, X., Zhang, A., Hou, X., Li, M., & Xia, Y. (2013). Multi-temporal remote sensing of land cover change and urban sprawl in the coastal city of Yantai, China. International Journal of Digital Earth, 6(2), 137-154.
  • Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer, M. E. (2005). Land cover classification and change analysis of the twin cities (Minnesota) Metropolitan area by multitemporal Landsat remote sensing. Remote Sensing of Environment, 98(2-3), 317-328.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Beşeri Coğrafya
Bölüm ARAŞTIRMA MAKALESİ
Yazarlar

Yeşim Aliefendioğlu 0000-0002-0859-7150

Sibel Canaz Sevgen 0000-0001-5552-6067

Harun Tanrıvermiş 0000-0002-0765-5347

Yayımlanma Tarihi 25 Ocak 2022
Yayımlandığı Sayı Yıl 2022 Sayı: 45

Kaynak Göster

APA Aliefendioğlu, Y., Canaz Sevgen, S., & Tanrıvermiş, H. (2022). LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY. Lnternational Journal of Geography and Geography Education(45), 437-453. https://doi.org/10.32003/igge.1007780
AMA Aliefendioğlu Y, Canaz Sevgen S, Tanrıvermiş H. LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY. IGGE. Ocak 2022;(45):437-453. doi:10.32003/igge.1007780
Chicago Aliefendioğlu, Yeşim, Sibel Canaz Sevgen, ve Harun Tanrıvermiş. “LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY”. Lnternational Journal of Geography and Geography Education, sy. 45 (Ocak 2022): 437-53. https://doi.org/10.32003/igge.1007780.
EndNote Aliefendioğlu Y, Canaz Sevgen S, Tanrıvermiş H (01 Ocak 2022) LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY. lnternational Journal of Geography and Geography Education 45 437–453.
IEEE Y. Aliefendioğlu, S. Canaz Sevgen, ve H. Tanrıvermiş, “LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY”, IGGE, sy. 45, ss. 437–453, Ocak 2022, doi: 10.32003/igge.1007780.
ISNAD Aliefendioğlu, Yeşim vd. “LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY”. lnternational Journal of Geography and Geography Education 45 (Ocak 2022), 437-453. https://doi.org/10.32003/igge.1007780.
JAMA Aliefendioğlu Y, Canaz Sevgen S, Tanrıvermiş H. LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY. IGGE. 2022;:437–453.
MLA Aliefendioğlu, Yeşim vd. “LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY”. Lnternational Journal of Geography and Geography Education, sy. 45, 2022, ss. 437-53, doi:10.32003/igge.1007780.
Vancouver Aliefendioğlu Y, Canaz Sevgen S, Tanrıvermiş H. LAND COVER MONITORING TECHNIQUES AND SPATIAL DEVELOPMENT: THE CASE OF CAPITAL OF TURKEY. IGGE. 2022(45):437-53.