Land Use Change Prediction using a Hybrid (CA-Markov) Model

Authors
A. Ildoromi 1
M. Safari Shad 2
1 Associate Professor, Department of Range and Watershed Management, Malayer University, Malayer, Iran
2 Ph.D. Student, Department Watershed Management, Faculty of Natural Resources, Sari University of Agriculture and Natural Resource, Sari, Iran
Abstract
Landsat data for 1992, 2000, and 2013 land use changes for Ekbatan Dam watershed was simulated through CA-Markov” model. Two classification methods were initially used, viz. the maximum likelihood (MAL) and support vector machine (SVM). Although both methods showed high overall accuracy and Kappa coefficient, visually MAL failed in separating land uses, particularly built up and dry lands.Therefore, the results of SVM were used for Markov Chain Model and “CA” filter to predict land use map for 2034. In order to assess the ability of “CA Markov” model, simulation for 2013was performed. Results showed that simulated map was in agreement with the existing map for2013 at 84% level. The land use map prediction showed that built up area of 0.8298 km2 in 2013 will increase to 1.02113 km2 in 2034. In contrast, irrigated agriculture will decrease from 17.33 km2 to 17.16 km2, and rain fed agriculture from 45.07 km2 to 44.49 km2. Results of this research proved the application of “CA Markov” model in simulating the land use changes.
Keywords
Cellular Automata model
MAL
SVM
Braimoh, A.K. and Onishi, T. Geostatistical techniques for incorporating spatial correlation into land use change models. Int J. App. Earth Obser. Geoinfo., 2007; 9(1): 438-446.
Breckling, B., Pe'er, G. and Matisons, G. Modeling Complex Ecological Dynamics. Springer., 2011; 397 P.
Camps-Valls, G., Gomez-Chova, L., Munoz-Mari, J. and Rojo-Alvarez, J.L. Martine Ramon M.Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE T. Geosci. Remote., 2008; 46(6): 1822-1835.
Devadas, R., Denhama, R.J. and Pringle, M. Support vector machine classification of object-based data for crop mapping, using multi-temporal land sat imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS Congress, 25 Melbourne, Australia, August – 01 September 2012; 185-190.
Dezhkam, S., Jabarian Amiri, B. and Darvishsefat, A.A. Anticipated changes in land use and cover in the Rasht city using Markov chain and CA model. Environ. Rese. J., 2015; 6(11): 104-139. (In Persian).
Debolini, M., Schoorl, J.M., Temme, A., Galli, M. and Bonari, E. Changes in Agricultural Land use Affecting Future Soil Redistribution Patterns: A Case Study in Southern Tuscany (Italy). Land Degrad Dev., 2015; 26(6): 574-586.
Falahatkar, S., Safianian, A., Khojedin, S.J. and Ziai, H. The ability of CA Markov model to predict land cover map (cas study: Esfahan Province). Geomatics Conferrence, Tehran, 2009; 1-9. (In Persian).
Huang, T.M., Kecman, V. and Kopriva, I. Kernel Based Algorithms for Mining Huge Data Sets, Supervised,Semi-supervised and unsupervised learning. Springer Verlag, Berlin, Heidelberg. 2006; 260 P.
Hamadan Regional Water Authority., Feasibility studies water and potential utilization of surface water Hamadan province, 2008; 69 P. (In Persian).
Haibo, Y., Longjiang, D., Hengliang, G. and Jie, Z. Tai'an land use Analysis and Prediction Based on RS and Markov Model. Procedia Environ. Sci., 2011; 10(4): 2625-2630.
Jafarian Jeloudar, Z., Shabanzadeh, S., Kavian, A. and Shokri M. Spatial Variability of Soil Features Affected by Landuse Type using Geostatistics. ECOPERSIA. 2014; 2 (3): 667-679.
Mirzaei Moosivand, A. Assesmentthe changes in rangeland at different times using satellite imagery and GIS in Khalkhalcity, Rangeland and Watershed Master's thesis, Mohaghegh Ardabili University. 2011; 117 P. (In Persian).
Mantero, P., Moser, G. and Serpico, S.B. Partially supervised classification of remote sensing images through SVM-based probability density estimation, IEEE T. Geosci. Remote Sens.2005; 43(3): 559-570.
Mountrakis, G., Im, J. and Ogole C. Support vector machines in remote sensing. J Photoger Remote Sens., 2011; 66(1): 247-259.
Muñoz-Rojas, M., Jordán. A. and Zavala, L.M. De la Rosa D, Abd-Elmabod S.K, Anaya Romero M. Impact of Land Use and Land Cover Changes on Organic Carbon Stocks in Mediterranean Soils (1956–2007). Land Degrade. Dev. 2015; 26(2): 168-179.
Rezaii Makhdoom, M.H., Valizade Kamran, Kh. and Andaryani, S. Almas Poor J. Geo. Plan., 2016; 19(52): 163-183. (In Persian).
Singh, A.K. Modeling Land use/ Land cover Changes Using Cellular Automata in Geo-Spatial Environment, MSC Theses, Netherland.2003; 58 P.
Sharma, A., Kamlesh, N., Tiwari, P. and Bhadoria, S. Effect of land use land cover change on soil erosion potential in an agricultural watershed. Environ. Monit. Assess., 2011; 173(1): 789-801.
Salazar, A., Baldi, G., Hirota, M. and Syktus, J. McAlpine C. Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review Global Planet Change., 2015; 128: 103-119.
Saiful Bahari, N.I. and Ahmad, A. Aboobaider B.M. Application of support vector machine for classification of multispectral data. 7th IGRSM International Remote Sensing & GIS Conference and Exhibition. IOP Conf. Series: Earth Environ. Sci., Tomsk, Russian, 2016; July11-16; 1-8.
Slam, M.R, MiahM, G. and Inoue, Y. Analysis of Land use and Land Cover Changes in the Coastal Area of Bangladesh using Landsat Imagery. Land Degrad. Dev., 2016; 27(4): 899-909.
Torrens, P.M. and O'Sullivan, D. Cellular automata and urban simulation: where do we go from here? Environ. Plan B., 2001; 28(1): 163-168.
Vapnik, V.N. An overview of statistical learning theory.IEEE Trans. Neural Network.1999; 10(5):988–1000.
Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R. and Espaldon, V. Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ. Manage., 2002; 30(1): 391-405.
Yalew, S.G., Mu, M.L, Griensven, A.V., Teferi, E., Priess, J., Schweitzer, Ch. and Zaag, P. Land-Use Change Modeling in the Upper Blue Nile Watershed. Environ., 2016; 3(3):1-16.
ECOPERSIA
Volume 5, Issue 1 - Serial Number 19
March 2017
Pages 1631-1640