Skip to main content

Advertisement

SpringerLink
Log in

Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Land use and land cover (LULC) changes affect several natural environmental factors, including soil erosion, hydrological balance, biodiversity, and the climate, which ultimately impact societal well-being. Therefore, LULC changes are an important aspect of land management. One method used to analyze LULC changes is the mathematical modeling approach. In this study, Cellular Automata and Markov Chain (CA-MC) models were used to predict the LULC changes in the Seyhan Basin in Turkey that are likely to occur by 2036. Satellite multispectral imagery acquired in the years 1995, 2006, and 2016 were classified using the object-based classification method and used as the input data for the CA-MC model. Subsequently, the post-classification comparison technique was used to determine the parameters of the model to be simulated. The Markov Chain analyses and the multi-criteria evaluation (MCE) method were used to produce a transition probability matrix and land suitability maps, respectively. The model was validated using the Kappa index, which reached an overall level of 77%. Finally, the LULC changes were mapped for the year 2036 based on transition rules and a transition area matrix. The LULC prediction for the year 2036 showed a 50% increase in the built-up area class and a 7% decrease in the open spaces class compared to the LULC status of the reference year 2016. About an 8% increase in agricultural land is also likely to occur in 2036. About a 4% increase in shrub land and a 5% decrease in forest areas are also predicted.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Agarwal, C., Green, G. M., Grove, J. M., Evans, T. P., & Schweik, C. M. (2002). A review and assessment of land-use change models: dynamics of space, time, and human choice.

  • Alphan, H., Doygun, H., & Unlukaplan, Y. I. (2009). Post-classification comparison of land cover using multitemporal Landsat and ASTER imagery: the case of Kahramanmaraş, Turkey. Environmental Monitoring and Assessment, 151(1), 327–336.

    Article  Google Scholar 

  • Al-sharif, A. A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), 4291–4301.

    Article  Google Scholar 

  • Araya, Y. H. (2009). Urban land use change analysis and modelling: a case study of Setubal-Sesimbra, Portugal.

  • Barsi, J. A., Lee, K., Kvaran, G., Markham, B. L., & Pedelty, J. A. (2014). The spectral response of the Landsat-8 operational land imager. Remote Sensing, 6(10), 10232–10251.

    Article  Google Scholar 

  • Berberoğlu, S., Akın, A., & Clarke, K. C. (2016). Cellular automata modeling approaches to forecast urban growth for Adana, Turkey: A comparative approach. Landscape and Urban Planning, 153, 11–27.

    Article  Google Scholar 

  • Bunruamkaew, K., & Murayam, Y. (2011). Site suitability evaluation for ecotourism using GIS & AHP: a case study of Surat Thani province, Thailand. Procedia-Social and Behavioral Sciences, 21, 269–278.

    Article  Google Scholar 

  • Chen, J., Mao, Z., Philpot, B., Li, J., & Pan, D. (2013). Detecting changes in high-resolution satellite coastal imagery using an image object detection approach. International Journal of Remote Sensing, 34(7), 2454–2469.

    Article  Google Scholar 

  • DeFries, R. S., Foley, J. A., & Asner, G. P. (2004). Land-use choices: balancing human needs and ecosystem function. Frontiers in Ecology and the Environment, 2(5), 249–257.

    Article  Google Scholar 

  • Eastman, J. R. (2001). Guide to GIS and image processing volume. USA: Clark University.

    Google Scholar 

  • Eastman, J. (2006). IDRISI Andes manual. Worcester, MA, USA: Clark Labs, Clark University.

    Google Scholar 

  • Eastman, J. R. (2009). IDRISI Taiga guide to GIS and image processing. Worcester, MA: Clark Labs Clark University.

    Google Scholar 

  • Eswaran, H., Berberoğlu, S., Cangir, C., Boyraz, D., Zucca, C., Özevren, E., et al. (2010). The anthroscape approach in sustainable land use. In Sustainable Land Management (pp. 1-50): Springer.

  • Fakioğlu, M. (2005). Seyhan Barajı Hidrografik Harita Alımı Değerlendirmesi ve Sonuçları, 2. Ulusal Mühendislik Ölçmeleri Sempozyumu, İstanbul(s 225), 236.

  • Geist, H. J., & Lambin, E. F. (2002). Proximate causes and underlying driving forces of tropical deforestation: Tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. BioScience, 52(2), 143–150.

    Article  Google Scholar 

  • Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Applied Geography, 63, 101–112.

    Article  Google Scholar 

  • Kityuttachai, K., Tripathi, N. K., Tipdecho, T., & Shrestha, R. (2013). CA-Markov analysis of constrained coastal urban growth modeling: Hua Hin seaside city, Thailand. Sustainability, 5(4), 1480–1500.

    Article  Google Scholar 

  • Kumar, M., & Rajan, K. S. (2014). Modelling land-use changes in Godavari River basin : a comparison of two districts in Andhra Pradesh. Proceedings of 7th Intl. Congress on Env. Modelling and Software. International Environmental Modelling and Software Society (IEMSS), San Diego, California, USA.

  • Mandal, S. N., Choudhury, J. P., & Chaudhuri, S. (2012). In search of suitable fuzzy membership function in prediction of time series data. International Journal of Computer Science Issues, 9(3), 293–302.

    Google Scholar 

  • Mehdi, B., Ludwig, R., & Lehner, B. (2012). Determining agricultural land use scenarios in a mesoscale Bavarian watershed for modelling future water quality. Advances in Geosciences, 31, 9–14.

    Article  Google Scholar 

  • Meyer, W. B., & Turner, B. (1996). Land-use/land-cover change: challenges for geographers. GeoJournal, 39(3), 237–240.

    Article  Google Scholar 

  • Modi, M., Kumar, R., Shankar, G. R., & Martha, T. R. (2014). Land cover change detection using object-based classification technique: a case study along the Kosi River, Bihar. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 839–843.

    Article  Google Scholar 

  • Muller, M. R., & Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151–157.

    Google Scholar 

  • Nouri, J., Gharagozlou, A., Arjmandi, R., Faryadi, S., & Adl, M. (2014). Predicting urban land use changes using a CA-Markov model. Arabian Journal for Science & Engineering (Springer Science & Business Media BV), 39(7), 5565–5573.

    Article  Google Scholar 

  • Pathak, S. (2014). New change detection techniques to monitor land cover dynamics in mine environment. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(8), 875–879.

    Article  Google Scholar 

  • Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing, 66(8), 1011–1016.

    Google Scholar 

  • Pontius Jr., R. G., & Chen, H. (2006). Land change modeling with GEOMOD. Clark University.

  • Pramanik, M. K. (2016). Site suitability analysis for agricultural land use of Darjeeling district using AHP and GIS techniques. Modeling Earth Systems and Environment, 2(2), 1–22.

    Article  Google Scholar 

  • Prieto-Amparán, J. A., Pinedo-Alvarez, A., Villarreal-Guerrero, F., Pinedo-Alvarez, C., Morales-Nieto, C., & Manjarrez-Domínguez, C. (2016). Past and future spatial growth dynamics of Chihuahua City, Mexico: pressures for land use. ISPRS International Journal of Geo-Information, 5(12), 235.

    Article  Google Scholar 

  • Raddad, S. (2016). Integrated a GIS and multi criteria evaluation approach for suitability analysis of urban expansion in southeastern Jerusalem region-Palestine. American Journal of Geographic Information System, 5(1), 24–31.

    Google Scholar 

  • Rahman, M. T. (2016). Detection of land use/land cover changes and urban sprawl in Al-Khobar, Saudi Arabia: an analysis of multi-temporal remote sensing data. ISPRS International Journal of Geo-Information, 5(2), 15.

    Article  Google Scholar 

  • Reis, S. (2008). Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors, 8(10), 6188–6202.

    Article  Google Scholar 

  • Riebsame, W., Parton, W., Galvin, K., Burke, I., Bohren, L., Young, R., et al. (1994). Integrated modeling of land use and cover change. BioScience, 44(5), 350–356.

    Article  Google Scholar 

  • Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical modelling, 9(3–5), 161–176.

    Article  Google Scholar 

  • Sang, L., Zhang, C., Yang, J., Zhu, D., & Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3), 938–943.

    Article  Google Scholar 

  • Seçkin, N., & Topçu, E. (2016). Drought analysis of the Seyhan Basin by using standardized precipitation index (SPI) and L-moments. Tarım Bilimleri Dergisi, 22(2), 196–215.

    Article  Google Scholar 

  • Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., & Iwamoto, H. (2014). Precise global DEM generation by ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2(4), 71.

    Article  Google Scholar 

  • Talu, N., & Özüt, H. (2011). Strategic steps to adapt to climate change in Seyhan River basin, October. 2011, Ankara. http://www.mdgfund.org/sites/default/files/ENV_CASE_STUDY_Turkey_StrategicStepstoAdapttoClimateChangeinSeyhanRiverBasin.pdf. Accessed 4 July 2018.

  • Turner, B., Meyer, W. B., & Skole, D. L. (1994). Global land-use/land-cover change: towards an integrated study. Ambio. Stockholm, 23(1), 91–95.

    Google Scholar 

  • Vázquez-Quintero, G., Solís-Moreno, R., Pompa-García, M., Villarreal-Guerrero, F., Pinedo-Alvarez, C., & Pinedo-Alvarez, A. (2016). Detection and projection of forest changes by using the Markov chain model and cellular automata. Sustainability, 8(3), 236.

    Article  Google Scholar 

  • Veldkamp, A., & Lambin, E. F. (2001). Predicting land-use change. Agriculture Ecosystems and Environment, 85(1–3), 1–6.

    Article  Google Scholar 

  • Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. (2002). Modeling the spatial dynamics of regional land use: the CLUE-S model. Environmental Management, 30(3), 391–405.

    Article  Google Scholar 

  • Verburg, P. H., Schot, P. P., Dijst, M. J., & Veldkamp, A. (2004). Land use change modelling: current practice and research priorities. GeoJournal, 61(4), 309–324.

    Article  Google Scholar 

  • Wen, W. (2008). Wetland change prediction using Markov cellular automata model in Lore Lindu National Park Central Sulawesi Province, Indonesia (seminar).

  • Xu, L., Li, Z., Song, H., & Yin, H. (2013). Land-use planning for urban sprawl based on the clue-s model: a case study of Guangzhou, China. Entropy, 15(9), 3490–3506.

    Article  Google Scholar 

  • Yang, Y., Zhang, S., Yang, J., Xing, X., & Wang, D. (2015). Using a cellular automata-Markov model to reconstruct spatial land-use patterns in Zhenlai County, Northeast China. Energies, 8(5), 3882–3902.

    Article  Google Scholar 

  • Yu, W., Zang, S., Wu, C., Liu, W., & Na, X. (2011). Analyzing and modeling land use land cover change (LUCC) in the Daqing City, China. Applied Geography, 31(2), 600–608.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of RS&GIS, Cukurova University, Adana, Turkey

    Elaheh Zadbagher & Suha Berberoglu

  2. Department of Geomatics Engineering, Bulent Ecevit University, Zonguldak, Turkey

    Elaheh Zadbagher & Kazimierz Becek

  3. Department of Geoengineering, Mine and Geology, Wroclaw University of Science and Technology, Wroclaw, Poland

    Kazimierz Becek

Authors
  1. Elaheh Zadbagher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kazimierz Becek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suha Berberoglu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Elaheh Zadbagher.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zadbagher, E., Becek, K. & Berberoglu, S. Modeling land use/land cover change using remote sensing and geographic information systems: case study of the Seyhan Basin, Turkey. Environ Monit Assess 190, 494 (2018). https://doi.org/10.1007/s10661-018-6877-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-018-6877-y

Keywords

Search

Navigation