Research Article
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Landsat Images Classification and Change Analysis of Land Cover/Use in Istanbul

Year 2016, Volume: 3 Issue: 2, 56 - 65, 02.08.2016

İsmail Büyüksalih

https://doi.org/10.30897/ijegeo.304484
Cited By: 2

Abstract

This paper describes the methodology and results of classifications of Landsat TM data of the Istanbul for the
years 1987 and 2007. Nine different land cover/use categories have been used, named built-up area, cropland,
barren ground, grassland, scrub/brush, water, ever green, deciduous, cloud and others uses. When the obtained
classification results are evaluated as a result of, the 1987 Landsat image overall accuracy of 79 % and a kappa
value of 0.76, the 2007 Landsat image overall accuracy of 83.50% and kappa value of 0.81. Thus, Istanbul's
change analysis was revealed that the 20-year period. The classifications have provided an economical and
accurate way to quantify, map and analyze changes over time in land cover. 

Keywords

: Remote sensing; Land cover/Land use GIS Temporal Analysis Landsat

References

  • Alpar, B., Gazioglu, C., Altinok, Y., Yücel, ZY., and Dengiz, Ş. (2004). Tsunami hazard assessment in Istanbul using by high resolution satellite data (Ikonos) and DTM. XXth Congress of the ISPRS, Istanbul, 2004.
  • Algan, O., M Namık Yalçın, Mehmet Özdoğan, Yücel Yılmaz, Erol Sarı, Elmas Kırcı-Elmas, İsak Yılmaz, Özlem Bulkan, Demet Ongan, Cem Gazioğlu, Atike Nazik, Mehmet Ali Polat, Engin Meriç (2011). Holocene coastal change in the ancient harbor of Yenikapı–İstanbul and its impact on cultural history. Quaternary Research, Vol .76 (1), pp.30-45.
  • Bauer, M.E., Yuan, F., Sawaya. K. E. (2003). Multi-temporal Landsat Image Classification and Change Analysis of Land Cover in The Twin Cities (minnesota) Metropolitan Area MutiTemp-2003, Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images. Ispra, Italy.
  • Cohen, W. B. and Goward, S. N. (2004). Landsat's role in ecological applications of remote sensing. Bioscience, 54(6), 535-545.
  • Conghe, S., Curtis, E., Woodcock, K. C. S., Lenney, M. P., and Scott, A. M. (2001). Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing Environment: 75:230–244.
  • Çoban, H. Oğuz., Koç, A. (2008). Sınıflandırma sonrası karsılastırma tekniği kullanılarak heterojen yapıya sahip ormanlardaZamansal Değişimlerin Belirlenmesi Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi, Seri: A, Sayı: 1, Yıl: 2008, ISSN: 1302-7085, S: 72-84.
  • Erdin, K., Koç, A., Yener, H. (2002). Remote sensing data in monitoring of relations between forest and settlements areas, 3rd International Symposium Remote Sensing of Urban Areas, Proceedings Volume II, Tüyap-Istanbul, Turkey, pp 600-608.
  • Gazioğlu C., Burak, S., Alpar, B., Türker, A. and Barut, IF. (2010) Foreseeable impacts of sea level rise on the southern coast of the Marmara Sea (Turkey), Vol.12(6), pp. 932-943, Water Policy.
  • Günlü, A., Keleş, S., Kadıoğulları, A., Başkent, E. Z. (2011). Landsat 7 ETM+ Uydu Görüntüsü Yardımıyla Arazi Kullanımı, Meşcere Gelişim Çağı ve Meşcere Kapalılığın Tahmin Edilmesi; Kastamonu-Kızılcasu İşletme Şefliği Örneği, Ulusal Akdeniz Orman ve Çevre Sempozyumu, Kahramanmaraş.
  • Günlü, A., Sivrikaya, F., Başkent, E. Z., Keleş, S., Çakir, G., Kadiogullari, A.İ. (2008). Estimation of Stand Type Parameters and Land Cover Using Landsat-7 ETM Image: A Case Study From Turkey, Sensors, 8: 2509-2525. Houghton.
  • Hoffer, RM. (1978). Biological and Physical Considerations in Applying Computer- aided analysis techniques to Remote Sensor data. In Remote Sensing: The quantitative approach (PH Swam, SM Davis Eds), McGraw- Hill, USA Ioannis, M., Meliadis M. (2011). Multi-temporal Landsat image classification and change analysis of land cover/use in the Prefecture of Thessaloiniki, Greece Proceedings of the International Academy of Ecology and Environmental Sciences. Kaya, Ş., Şeker, D. Z. (2013). Çok Zamanlı Landsat 5 TM Uydu Görüntü Verileri Kullanılarak Arazi Örtüsü/Kullanımı Arasındakı İlişkilerin normlaştırılarak değerlendirilmesi, TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara.
  • Kırtıloğlu, E. et al. (2014). Hotamış Gölü Çev. Arazi Kul. Uydu Görüntüleri Yardımıyla Zamansal Analizi, 5. Uzaktan Algilama-CBS Sempozyumu (UZAL-CBS 2014), İstanbul.
  • Lillesand, T.M., Kiefer, R.W. (2000). Remote Sensing and Image Interpratation, fourth edition, the Lehigh press, New York.
  • Kun, Jia., Shunlin, Liang., Xiangqin, Wei., Yunjun, Yao., Yingru, Su., Bo, Jiang and Xiaoxia, Wang. (2014). Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data, Remote Sens. 6, 11518-11532.
  • Lu, D., Weng, Q. (2006). Use of impervious surface in urban land use classification, Remote Sensing of Environment, 102(1-2), 146-160.
  • Maktav, D., Erbeka, F. S. (2005). Analysis of urban growth using multi‐temporal satellite data in Istanbul, Turkey, International Journal of Remote Sensing,26, Issue 4.
  • OECD. (2008). OECD territorial reviews. Turkey: Istanbul ISBN 9789264043718.
  • Ramachandra, T. and Kumar U. (2004). Geographic Resources Decision Support System for land use, land cover dynamics analysis. Proceedings of the FOSS/GRASS Users Conference. 12-14 September 2004, Bangkok, Thailand.
  • Rozenstein, O., Karnieli, A. (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography 31, 533-544.
  • Sağlam, B., Bilgili E., Durmaz, B., Kadıoğulları, A. İ., and Küçük, Ömer. (2008). Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery, MDPI sensors.
  • Singh, A., 1989. Digital change detection techniques using remotely sensed data, International Journal of Remote Sensing, 10(6): 989–1003.
  • Todd A. Schroeder, Warren B. Cohen, Conghe Song C, Morton J. Canty, Zhigiang Yang. (2006). Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sensing of Environment.
  • TUIK. (2012). Turkish Statistics Institute. http://www.tuik.gov.tr.
  • Uysal, C., Maktav, D. (2015). Landsat Verileri ve Lineer Spektral Ayriştirma (unmixing) Yöntemi Kullanilarak İzmit Körfezi Çevresinde Kentsel Değişim Alanlarinin Belirlenmesi, Havacilik ve Uzay Teknolojileri Dergisi, Cilt 8 Sayi 1, 47-53.

Year 2016, Volume: 3 Issue: 2, 56 - 65, 02.08.2016

İsmail Büyüksalih

https://doi.org/10.30897/ijegeo.304484
Cited By: 2

Abstract

References

  • Alpar, B., Gazioglu, C., Altinok, Y., Yücel, ZY., and Dengiz, Ş. (2004). Tsunami hazard assessment in Istanbul using by high resolution satellite data (Ikonos) and DTM. XXth Congress of the ISPRS, Istanbul, 2004.
  • Algan, O., M Namık Yalçın, Mehmet Özdoğan, Yücel Yılmaz, Erol Sarı, Elmas Kırcı-Elmas, İsak Yılmaz, Özlem Bulkan, Demet Ongan, Cem Gazioğlu, Atike Nazik, Mehmet Ali Polat, Engin Meriç (2011). Holocene coastal change in the ancient harbor of Yenikapı–İstanbul and its impact on cultural history. Quaternary Research, Vol .76 (1), pp.30-45.
  • Bauer, M.E., Yuan, F., Sawaya. K. E. (2003). Multi-temporal Landsat Image Classification and Change Analysis of Land Cover in The Twin Cities (minnesota) Metropolitan Area MutiTemp-2003, Second International Workshop on the Analysis of Multi-Temporal Remote Sensing Images. Ispra, Italy.
  • Cohen, W. B. and Goward, S. N. (2004). Landsat's role in ecological applications of remote sensing. Bioscience, 54(6), 535-545.
  • Conghe, S., Curtis, E., Woodcock, K. C. S., Lenney, M. P., and Scott, A. M. (2001). Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? Remote Sensing Environment: 75:230–244.
  • Çoban, H. Oğuz., Koç, A. (2008). Sınıflandırma sonrası karsılastırma tekniği kullanılarak heterojen yapıya sahip ormanlardaZamansal Değişimlerin Belirlenmesi Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi, Seri: A, Sayı: 1, Yıl: 2008, ISSN: 1302-7085, S: 72-84.
  • Erdin, K., Koç, A., Yener, H. (2002). Remote sensing data in monitoring of relations between forest and settlements areas, 3rd International Symposium Remote Sensing of Urban Areas, Proceedings Volume II, Tüyap-Istanbul, Turkey, pp 600-608.
  • Gazioğlu C., Burak, S., Alpar, B., Türker, A. and Barut, IF. (2010) Foreseeable impacts of sea level rise on the southern coast of the Marmara Sea (Turkey), Vol.12(6), pp. 932-943, Water Policy.
  • Günlü, A., Keleş, S., Kadıoğulları, A., Başkent, E. Z. (2011). Landsat 7 ETM+ Uydu Görüntüsü Yardımıyla Arazi Kullanımı, Meşcere Gelişim Çağı ve Meşcere Kapalılığın Tahmin Edilmesi; Kastamonu-Kızılcasu İşletme Şefliği Örneği, Ulusal Akdeniz Orman ve Çevre Sempozyumu, Kahramanmaraş.
  • Günlü, A., Sivrikaya, F., Başkent, E. Z., Keleş, S., Çakir, G., Kadiogullari, A.İ. (2008). Estimation of Stand Type Parameters and Land Cover Using Landsat-7 ETM Image: A Case Study From Turkey, Sensors, 8: 2509-2525. Houghton.
  • Hoffer, RM. (1978). Biological and Physical Considerations in Applying Computer- aided analysis techniques to Remote Sensor data. In Remote Sensing: The quantitative approach (PH Swam, SM Davis Eds), McGraw- Hill, USA Ioannis, M., Meliadis M. (2011). Multi-temporal Landsat image classification and change analysis of land cover/use in the Prefecture of Thessaloiniki, Greece Proceedings of the International Academy of Ecology and Environmental Sciences. Kaya, Ş., Şeker, D. Z. (2013). Çok Zamanlı Landsat 5 TM Uydu Görüntü Verileri Kullanılarak Arazi Örtüsü/Kullanımı Arasındakı İlişkilerin normlaştırılarak değerlendirilmesi, TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara.
  • Kırtıloğlu, E. et al. (2014). Hotamış Gölü Çev. Arazi Kul. Uydu Görüntüleri Yardımıyla Zamansal Analizi, 5. Uzaktan Algilama-CBS Sempozyumu (UZAL-CBS 2014), İstanbul.
  • Lillesand, T.M., Kiefer, R.W. (2000). Remote Sensing and Image Interpratation, fourth edition, the Lehigh press, New York.
  • Kun, Jia., Shunlin, Liang., Xiangqin, Wei., Yunjun, Yao., Yingru, Su., Bo, Jiang and Xiaoxia, Wang. (2014). Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data, Remote Sens. 6, 11518-11532.
  • Lu, D., Weng, Q. (2006). Use of impervious surface in urban land use classification, Remote Sensing of Environment, 102(1-2), 146-160.
  • Maktav, D., Erbeka, F. S. (2005). Analysis of urban growth using multi‐temporal satellite data in Istanbul, Turkey, International Journal of Remote Sensing,26, Issue 4.
  • OECD. (2008). OECD territorial reviews. Turkey: Istanbul ISBN 9789264043718.
  • Ramachandra, T. and Kumar U. (2004). Geographic Resources Decision Support System for land use, land cover dynamics analysis. Proceedings of the FOSS/GRASS Users Conference. 12-14 September 2004, Bangkok, Thailand.
  • Rozenstein, O., Karnieli, A. (2011). Comparison of methods for land-use classification incorporating remote sensing and GIS inputs. Applied Geography 31, 533-544.
  • Sağlam, B., Bilgili E., Durmaz, B., Kadıoğulları, A. İ., and Küçük, Ömer. (2008). Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery, MDPI sensors.
  • Singh, A., 1989. Digital change detection techniques using remotely sensed data, International Journal of Remote Sensing, 10(6): 989–1003.
  • Todd A. Schroeder, Warren B. Cohen, Conghe Song C, Morton J. Canty, Zhigiang Yang. (2006). Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sensing of Environment.
  • TUIK. (2012). Turkish Statistics Institute. http://www.tuik.gov.tr.
  • Uysal, C., Maktav, D. (2015). Landsat Verileri ve Lineer Spektral Ayriştirma (unmixing) Yöntemi Kullanilarak İzmit Körfezi Çevresinde Kentsel Değişim Alanlarinin Belirlenmesi, Havacilik ve Uzay Teknolojileri Dergisi, Cilt 8 Sayi 1, 47-53.
There are 24 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

İsmail Büyüksalih

Publication Date August 2, 2016
Published in Issue Year 2016 Volume: 3 Issue: 2

Cite

APA Büyüksalih, İ. (2016). Landsat Images Classification and Change Analysis of Land Cover/Use in Istanbul. International Journal of Environment and Geoinformatics, 3(2), 56-65. https://doi.org/10.30897/ijegeo.304484

Cited By

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Bartın Orman Fakültesi Dergisi
Alkan GÜNLÜ
https://doi.org/10.24011/barofd.882471
Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul
International Journal of Environment and Geoinformatics
Hatice ÇATAL REİS
https://doi.org/10.30897/ijegeo.688826

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