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:: Volume 33, Issue 3 (12-2018) ::
geores 2018, 33(3): 73-87 Back to browse issues page
Prediction of Land Cover Changes in Horizon of 2028 through a Hybrid Model of Markov Chain and Cellular Automata;Catchment Area around Bazangan Lake Case Study
Marzieh Alikhah Asl * 1, Farzaneh Rezvani 1
1- Department of Natural Resources And Enviromental Engineering,Payame Noor University, Tehran,Iran
Abstract:   (250 Views)
Introduction and Background Detection and prediction of changes are necessary for maintenance of an ecosystem particularly in rapidly-changing and often unplanned regions in developing countries.
Aims This study predicts the land use changes in catchment area around Bazangan Lake for the year of 2028 with the aim of investigating the evelopment in Bazangan Lake ecosystem based on the observeddegradation from 2002 to 2015.
Methodology The classification of studied area was carried out based on five categories of irrigated agriculture, rainfed agriculture, rangeland, water zones and residential areas through TM, ETM and OLI sensors and utilization of independent component analysis (ICA) with an overall accuracy of 92.23% and
kappa coefficient of 0.89% for the years of 1999, 2002 and 2015. Afterwards, the land use changes were predicted by a hybrid model of Markov chain and cellular automata. The overall accuracy and kappa coefficient were determined in IDRISI software by the help of ERRMAT Module to verify mode.
Conclusion According to error matrix, the overall accuracy of performance was 71 percent and kappa coefficient 0.87 percent which proved Markov chain and cellular automaton (CA-Markov) for predicting the land use classes in upcoming 13 years. According to results, the continued current process of land
use changes in this region will change Bazangan Lake area to 12.81 hectares, the irrigated agriculture land area to 495.91 hectares, rainfed agriculture land area to 5764.42 hectares, rangelands to 4592.15 hectares, and residential land area to 94.74 hectares in the next 13 years.
Keywords: Markov Chain Model, Cellular Automata, Bazangan Lake, Remote Sensing
Full-Text [PDF 1501 kb]   (157 Downloads)    
Type of Study: Research | Subject: GIS
Received: 2018/01/18 | Accepted: 2018/10/1 | Published: 2018/12/19
References
1. Anderson, J. R., Hady, E., Roach, E. J., & Wetter, T. (1976). A land use and land cover classification system for use with remote sensor data. Washington: US Government Printing Office. [DOI:10.3133/pp964]
2. Brown, D. G., Pijanowski, B. C., & Duh, J. D. (2000). Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59(4), 247-263. [DOI:10.1006/jema.2000.0369]
3. Dejkam, S. (2011). Studying the trend and pattern of urban development changes with the landscape ecology approach (case study: Rasht city). (Master's thesis ), University of Tehran, Tehran. (Persian)
4. Department of Natural Resources of Khorasan Razavi Province. (2008). Detailed executive studies of Shourlagh-e-Sarakhs watershed management. (Persian)
5. Eslah, M., Al-Modarresi, S. A., Mofidifar, M., & Malekzadeh Bafghi, S. (2014). Evaluation of Markov chain model efficiency in evaluating land use and land cover change using Lanssat satellite images. Paper presented at the National Conference on Application of Advanced Space Analysis Models (Remote Sensing and GIS in Land Use Planning). Yazd, Yazd Islamic Azad University, Yazd municipality. (Persian)
6. Falahtakar, S., Safiyanian, A., Khajeddin, J., & Ziaei, H. (2009). Investigating the ability of CA Markov model to predict land cover map (case study: Isfahan city). Paper presented at the Geomatics Conference, Mapping Organization, Tehran. (Persian)
7. Fan, F., Weng, Q., & Wang, Y. (2007). Land use and land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors, 7(7), 1323-1342. [DOI:10.3390/s7071323]
8. Fatahi, M. M., & Habibi Arbatani, V. (2011). Applied remote sensing. Tehran: Abangah Publication. (Persian)
9. Gobattoni, F., Pelorosso, R., & Leone, A. (2009). Assessing the effects of land use changes on soil erosion: a case study in Central Apennine (Italy). Paper presented at the XXXIII CIOSTA - CIGR V Conference 2009, Reggio Calabria (Italy).
10. Goldsmith, F. B. (1991). Monitoring for conservation and ecology. Berlin: Springer Science & Business Media. [DOI:10.1007/978-94-011-3086-8]
11. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20), 3761-3772. [DOI:10.1016/j.ecolmodel.2011.09.009]
12. Hashemi Tangestani, M., Beiranvand, S., & Tayyebi, M. H. (2013). Change detection of Bakhtegan Fars lake during the period from 1956 to 2007. Journal of Environmental Studies, 39(3), 189-199. (Persian)
13. Islam Bonyad, A., & Haji Ghaderi, T. (2007). Preparation of Zanjan forests natural map using ETM + Landsat 7 data. Journal of Agricultural Science and Technology, 11(42), 627-638. (Persian)
14. Jensen, J. R. (2007). Remote sensing of the environment: An earth resource perspective. New Jersey: Pearson Prentice Hall. https://doi.org/10.1016/j.rse.2007.03.022 [DOI:10.1016/j.rse.2007.02.014]
15. Kifer, L. (2000). Fundamentals of remote sensing and interpretation of aerial and satellite images (H. Malmeryan, Trans.). Tehran: Geographic Organization publications, Ministry of Defense and Armed Forces Support. (Persian)
16. Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International journal of Remote sensing, 28(5), 823-870. [DOI:10.1080/01431160600746456]
17. Majidian, M. (2015). Evaluating and modeling land use changes using remote sensing and Markov chain model (case study: Yasouj city). (Master's thesis), Payame Noor University, Tehran. (Persian)
18. Namdar, M., Adamowski, J., Saadat, H., Sharifi, F., & Khiri, A. (2014). Land-use and land-cover classification in semi-arid regions using independent component analysis (ICA) and expert classification. International journal of Remote sensing, 35(24), 8057-8073. [DOI:10.1080/01431161.2014.978035]
19. Piquer-Rodríguez, M., Kuemmerle, T., Alcaraz-Segura, D., Zurita-Milla, R., & Cabello, J. (2012). Future land use effects on the connectivity of protected area networks in southeastern Spain. Journal for Nature Conservation, 20(6), 326-336. [DOI:10.1016/j.jnc.2012.07.001]
20. Pontius Jr, R. G., & Chen, H. (2006). Geomod modeling. USA: Clark University.
21. Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric engineering and remote sensing, 66(8), 1011-1016.
22. Ramezani, N., & Jafari, R. (2015). Land use/cover change detection in 2025 with CA-Markov chain model (case study: Esfarayen). Geographical Researches Quarterly Journal, 29(4), 83-96. (Persian)
23. Safyanian, A., & Khodakarmi, L. (2011). Preparation of land use map using Fuzzy classification method. Town and Country Planning Journal, 3(4), 95-114. (Persian)
24. Samii, A., Aghazadeh, V., & Khodadadi, A. (2007). Environmental impact assessment of Soungoun copper mine. Paper presented at the Proceedings of the 26th Earth Science Forum, Geological Survey and Mineral Exploration of Iran, Tehran. (Persian)
25. Schulz, J. J., Cayuela, L., Echeverria, C., Salas, J., & Rey Benayas, J. M. (2010). Monitoring land cover change of the dryland forest landscape of Central Chile (1975–2008). Applied Geography, 30(3), 436-447. [DOI:10.1016/j.apgeog.2009.12.003]
26. Stéphenne, N., & Lambin, E. F. (2001). A dynamic simulation model of land-use changes in Sudano-sahelian countries of Africa (SALU). Agriculture, Ecosystems & Environment, 85(1), 145-161. [DOI:10.1016/S0167-8809(01)00181-5]
27. Wang, S. Q., Zheng, X. Q., & Zang, X. B. (2012). Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environmental Sciences, 13, 1238-1245. [DOI:10.1016/j.proenv.2012.01.117]
28. Whitford, W. G. (2008). Ecology of desert systems (H. Azarnivand & A. Malekian, Trans.). Tehran: University of Tehran. (Persian)
29. Wu, Q., Li, H.-q., Wang, R.-s., Paulussen, J., He, Y., Wang, M., . . . Wang, Z. (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78(4), 322-333. [DOI:10.1016/j.landurbplan.2005.10.002]
30. Zare Garizi, A., Bardi Sheikh, V., Saedoddin, A., & Mahini, A. (2012). Simulating the spatiotemporal changes of forest extent for the Chehelchay watershed (Golestan province), using integrated CA-Markov model. Forest and Poplar Research, 20(2), 273-285. (Persian)
31. Ziaian Firoozabadi, P., Shakiba, A., Matkan, A. A., & Sadeghi, A. (2009). Remote sensing, geographic information system and cellular automata model as a tool for simulation of urban land use change (case Study: Shahre kord). Environmental Sciences Journal, 7(1), 133-148. (Persian)
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Alikhah Asl M, Rezvani F. Prediction of Land Cover Changes in Horizon of 2028 through a Hybrid Model of Markov Chain and Cellular Automata;Catchment Area around Bazangan Lake Case Study. geores. 2018; 33 (3) :73-87
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Volume 33, Issue 3 (12-2018) Back to browse issues page
فصلنامه تحقیقات جغرافیایی Geographical Researches Quarterly Journal
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