Temporal monitoring of land use/land cover change in Kahramanmaraş city

Year 2021, , 134 - 140, 01.07.2021

Kübra Aliyazıcıoğlu Fatmanur Beker R. Hale Topaloğlu B. Baha Bilgilioğlu Resul Çömert

https://doi.org/10.31127/tuje.707156

Cited By: 2

Abstract

Irregular urbanization causes problems such as decrease in fertile agricultural areas, irregular industrialization and urbanization. To provide a healthier life opportunity for future generations without disturbing nature, it is essential to determine the temporal changes in land use and to make land management plans accordingly. In this study, land use/land cover (LULC) change occurred in Kahramanmaras province within 30 years was investigated by remote sensing and integration of Geographic Information Systems. Landsat satellite images of 1988-1998-2008-2018 were obtained for the production of LULC maps. Each image was classified according to supervised classification approach using the support vector machines (SVMs) algorithms. The confusion matrix was created for each year to examine the accuracy of the LULC maps. The overall accuracy of the thematic maps was obtained as 91.76%, 93.56%, 86.89% and 88.29%, respectively. Also, Kappa values of thematic maps for each year were obtained as 0.88, 0.91, 0.81 and 0.84, respectively. When the results were examined, the development of industry in the city area and the construction of the airport contributed to the development of the social and economic structure of the city. The increase in the number of housing in the related regions has led to an increase in the amount of urban areas and a decrease in agricultural areas.

Keywords

Change detection, Support vector mechine, Land use, Land cover, Kahramanmaraş

Thanks

This article has been selected from the papers presented at the TUFUAB-2019 (10. Turkey National Photogrammetry and Remote Sensing Technical Symposium)

References

• Başel H (2007). The causes of population mobility and internal migratıon in Turkey. Journal of Social Policy Conferences, 0 (53), 515-542 (in Turkish).
• Cohen J (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37-46. DOI: 10.1177/001316446002000104
• Çölkesen İ (2009). Comparing and analyzing of advanced classifier techniques in remote sensing. MS Thesis, Gebze İleri Teknoloji Enstitüsü, Turkey (in Turkish).
• Congalton R G & Green K (1999). Assessing the accuracy of remotely sensed data: Principles and practices. CRC Press.
• Demirci D A (2007). Character recognition by support vector machines. MS Thesis, Yıldız Teknik University, İstanbul, Turkey (in Turkish).
• Demirkan D Ç (2017). Hierarchical land use and land cover classification of Sentinel2-A images and its use for corine system. MS Thesis, Middle East Technical University, Ankara, Turkey.
• Denizdurduran M (2012). Remote sensing methods of land use and land cover characteristics of the province Kahramanmaraş. MS Thesis, Kahramanmaraş Sütçü İmam Üniversity, Kahramanmaraş, Turkey (in Turkish).
• Ekercin S (2007). Multitemporal change detection on the Salt lake and surroundings by integrating remote sensing and geographic information systems. PhD Thesis, İstanbul Technical University, İstanbul, Turkey
• Eray O (2008). Speech recognition application with support vector machines. MS Thesis, Pamukkale University, Denizli, Turkey (in Turkish).
• Ercan O (2020). Essentials of a sustainable land use planning approach for rural areas and a model proposal to be applied under Turkish Conditions. Turkish Journal of Engineering, 4 (3), 154-163. DOI: 10.31127/tuje.650238
• Esetlili M T, Balcik F B, Sanli F B, Ustuner M, Kalkan K, Goksel C, Gazioğlu C & Kurucu Y (2018). Comparison of object and pixel-based classifications for mapping crops using Rapideye Imagery: A case study of Menemen Plain, Turkey. International Journal of Environment and Geoinformatics, 5 (2), 231-243. DOI: 10.30897/ijegeo
• Geography of Kahramanmaraş, URL1: https://kahramanmaras.bel.tr/kesfedin/kahramanmarasin-cografyasi (Accessed Date: 29 April 2020).
• Göksel Ç, David R M, Dogru A Ö. (2018). Environmental monitoring of spatio-temporal changes in Northern Istanbul using remote sensing and GIS. International Journal of Environment and Geoinformatics, 5(1), 94-103. DOI: 10.30897/ijegeo.410943
• Göksel C, Doğru A Ö (2019). Analyzing the urbanization in the protection area of the Bosphorus. International Journal of Engineering and Geosciences, 4 (2), 52-57. DOI: 10.26833/ijeg.446912
• Hegazy I R & Kaloop M R (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), 117-124. DOI: 10.1016/j.ijsbe.2015.02.005
• İderman E (2006). Investigation of the ancient and present land use of Salamis ancient city and it's environment by using remote sensing and geographic information systems. MS Thesis, Çukurova Üniversity, Adana, Turkey (in Turkish).
• Işık Ş (2005). Urbanisation and Urbanisation Models in Turkey. Aegean Geographical Journal, 14 (1-2), 57-71 (in Turkish).
• Jenness J & Wynne J J (2005). Cohen's Kappa and classification table metrics 2.0: An ArcView 3.x extension for accuracy assessment of spatially explicit models. Open-File Report OF 2005-1363. Flagstaff, AZ: US Geological Survey, Southwest Biological Science Center. 86 p.
• Jimoh R, Afonja Y, Albert C & Amoo N (2018). Spatio-temporal urban expansion analysis in a growing city of Oyo Town, Oyo State, Nigeria using remote sensing and geographic information system (GIS) tools. International Journal of Environment and Geoinformatics, 5(2), 104-113. DOI: 10.30897/ijegeo.354627
• Karakuş P, Karabork H, Kaya S (2017). A comparison of the classification accuracies in determining the land cover of Kadirli Region of Turkey by using the pixel based and object based classification algorithms. International Journal of Engineering and Geosciences, 2 (2), 52-60. DOI: 10.26833/ijeg.298951
• Kavzoglu T & Colkesen I (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359. DOI: 10.1016/j.jag.2009.06.002
• Kavzoğlu T & Çölkesen İ (2010). Investigation of the effects of Kernel functions in satellite image classification using support vector machines. Harita Dergisi, 144(7), 73-82 (in Turkish).
• Kumar P, Gupta D K, Mishra V N & Prasad R (2015). Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data. International Journal of Remote Sensing, 36(6), 1604-1617. DOI: 10.1080/2150704X.2015.1019015
• Leichtle T, Geiß C, Wurm M, Lakes T & Taubenböck H (2017). Unsupervised change detection in VHR remote sensing imagery–an object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54, 15-27. DOI: 10.1016/j.jag.2016.08.010
• Mondal A, Kundu S, Chandniha S K, Shukla R & Mishra P K (2012). Comparison of support vector machine and maximum likelihood classification technique using satellite imagery. International Journal of Remote Sensing and GIS, 1(2), 116-123.
• Noi P T & Kappas M (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18. DOI: 10.3390/s18010018
• Orhan O, Dadaser-Celik F, Ekercin S (2019). Investigating land surface temperature changes using Landsat-5 data and real-time infrared thermometer measurements at Konya Closed Basin in Turkey. International Journal of Engineering and Geosciences, 4(1), 16-27. DOI: 10.26833/ijeg.417151
• Oruç M, Marangoz A M & Karakış S (2007). Comparıson of pixel-based and object-oriented classification approaches using pan-sharpened landsat 7 etm+ image, 11. Türkiye Harita Bilimsel ve Teknik Kurultayı (in Turkish).
• 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. DOI: 10.1016/j.jag.2009.11.002
• Sandal E K & Karademir N (2013). Determination of people's expectations and consciousness with adequacy of green spaces in Kahramanmaraş. Eastern Geographical Review, 18(29), 155-176 (in Turkish).
• Tok E (2006). Monitoring the urbanization through V-I-S model using remote sensing data. İstanbul Technical University, İstanbul, Turkey (in Turkish).
• TUIK URL2: http://tuik.gov.tr/UstMenu.do?metod=temelist (Accessed Date: 29 April 2020).
• Üstüner M, Şanli F B, Abdikan S, Esetlili M T & Kurucu Y (2013). Investigation the impact of rededge and NIR bands on crop type classıfıcatıon: A case study of RAPIDEYE. Türkiye Ulusal Fotogrametri ve Uzaktan Algılama Birliği VII. Teknik Sempozyumu (TUFUAB’2013), Trabzon, Turkey (in Turkish).
• Vapnik V N (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York.
• Yang C, Everitt J H & Murden D (2011). Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 75(2), 347-354. DOI: 10.1016/j.compag.2010.12.012
• 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. DOI: 10.1016/j.rse.2005.08.006