Document Type : Research Paper


1 Master student of Land Assessment and Spatial Planning, Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

2 Associate Professor, Department of Environment, Faculty of Natural Resources, University of Tehran, Karaj, Iran.

3 Assistant Professor, Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Arak University of Technology, Arak, Iran.



Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study has aimed to introduce Google Earth Engine to evaluate the pattern of land changes during 2006- 2021 and predict the pattern of future changes by using an integrated model based on Cellular automata and Markov chain using Google Earth Engine system. Three Landsat images (2006, 2014 and 2021) were classified using the support vector machine classifier method, and were simulated using the integrated model of cellular automata and Markov chain. In order to evaluate the accuracy of the predicted map of 2021, the classified map of the same year was applied. The accuracy of classified and simulated maps was Kno=0.812, Klocation=0.816, Kstandard=0.786 respectively. Evaluation of the land use/cover changes shows that between 2006 and 2035, the buildup areas will reach from 4839.01 hectares to 7199.76 hectares with increasing of 2360.75 hectares. These results indicate the necessity of land use planning principles. Simulation models can reduce the risks of long-term decision-making in land use management and Google Earth Engine can reduce the time and cost for classification and satellite image processing.


 [1] Ali Mohammadi Sarab, A., Motakan, A.A. and Mirbagheri, B. (2008). Evaluating the efficiency of the cellular automata model in simulating the expansion of urban land in the southwest suburbs of Tehran. Journal of Space Planning and Preparation, 2(14), 81-102. (In Persian)
[2] Azadi, H., Barati, A. A., Rafiaani, P., Taheri, F., Gebrehiwot, K., Witlox, F. and Lebailly, P. (2017). Evolution of land use-change modeling: routes of different schools of knowledge. Landscape and Ecological Engineering, 13(2), 319-332.
[3] Bazoband, M. (2015).Modeling urban land use change using automatic cells model (case study: Tabriz city). Master's thesis. Faculty of Geography, Khwarazmi University. (In Persian)
[4] Bennear, L. S. and Olmstead, S. M. (2008). The impacts of the “right to know”: Information disclosure and the violation of drinking water standards. Journal of Environmental Economics and Management, 56(2), 117-130.
[5] Biswas, M., Banerji, S. and Mitra, D. (2020). Land-use–land-cover change detection and application of Markov model: A case study of Eastern part of Kolkata. Environment, Development and Sustainability, 22(5), 4341-4360.
[6] Christensen, M. and Jokar Arsanjani, J. (2020). Stimulating implementation of sustainable development goals and conservation action: predicting future land use/cover change in Virunga National Park, Congo. Sustainability, 12(4), 1570.
[7] Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1), 37–46.
[8] Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.
[9] De Groot, R. S., Alkemade, R., Braat, L., Hein, L. and Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological complexity, 7(3), 260-272.
[10] Deng, X., Huang, J., Rozelle, S. and Uchida, E. (2006). Cultivated land conversion and potential agricultural productivity in China. Land use policy, 23(4), 372-384.
[11]de Noronha Vaz, E., Nijkamp, P., Painho, M. and Caetano, M. (2012). A multi-scenario forecast of urban change: A study on urban growth in the Algarve. Landscape and Urban Planning, 104(2), 201-211.
[12] Eastman, J. R. (2003). IDRISI Kilimanjaro: guide to GIS and image processing.
[13] Eastman, J. R. (2012). IDRISI Selva manual. Clark labs-Clark University. Worcester, Mass. USA.
[14] Esmaeili, H. and Negahban, S. (2021). Detection and prediction of land use changes using Markov chain model and cellular automata case study: (Darab plain). Journal of Geographical Studies of Dry Areas,11(43), 41-61. (In Persian)
[15] Fereshtehkhou, M. (2013). Dynamic modeling of urban growth using the combined model of cellular automata with genetic algorithm and artificial neural networks (study area: Kerman city). Master's thesis. Faculty of Civil Engineering and Mapping. Kerman Graduate University of Technology. (In Persian)
[16] Fu, F., Deng, S., Wu, D., Liu, W. and Bai, Z. (2022). Research on the spatiotemporal evolution of land use landscape pattern in a county area based on CA-Markov model. Sustainable Cities and Society, 80, 103760.
[17] Gao, J., Li, F., Gao, H., Zhou, C. and Zhang, X. (2017). The impact of land-use changes on water-related ecosystem services: a study of the Guishui River Basin, Beijing, China. Journal of Cleaner Production, 163, S148-S155.
[18] Gamboa, A. M. and Galicia, L. (2011). Differential influence of land use/cover change on topsoil carbon and microbial activity in low-latitude temperate forests. Agriculture, ecosystems & environment, 142(3-4), 280-290.
[19] Ghaderi, Sh., Zare Chahouki, M.A., Azarnivand, H., Tavili. And Rayghani, B. (2020). Prediction of land use changes using CA-Markov model (case study: Eshtehard). Journal of Rangeland, 4(1), 147-160. (In Persian)
[20] Gharaibeh, A., Shaamala, A., Obeidat, R. and Al-Kofahi, S. (2020). Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model. Heliyon, 6(9), e05092.
[21] Groeneveld, J., Müller, B., Buchmann, C. M., Dressler, G., Guo, C., Hase, N. and Schwarz, N. (2017). Theoretical foundations of human decision-making in agent-based land use models–A review. Environmental modelling & software, 87, 39-48.
[22] Gualtieri, J. A. and Cromp, R. F. (1999). Support vector machines for hyperspectral remote sensing classification. In 27th AIPR workshop: advances in computer-assisted recognition (Vol. 3584, pp. 221-232). SPIE.
[23] Guan, D., Gao, W., Watari, K. and Fukahori, H. (2008). Land use change of Kitakyushu based on landscape ecology and Markov model. Journal of Geographical Sciences, 18(4), 455-468.
[24] Guzman, L. A., Escobar, F., Peña, J. and Cardona, R. (2020). A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region. Land use policy, 92, 104445.
[25] Haines-Young, R., Potschin, M. and Kienast, F. (2012). Indicators of ecosystem service potential at European scales: mapping marginal changes and trade-offs. Ecological Indicators, 21, 39-53.
[26] Hasan, S. S., Zhen, L., Miah, M. G., Ahamed, T. and Samie, A. (2020). Impact of land use change on ecosystem services: A review. Environmental Development, 34, 100527.
[27] Hishe, S., Bewket, W., Nyssen, J. and Lyimo, J. (2020). Analysing past land use land cover change and CA-Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia. Geocarto International, 35(3), 225-255.
[28] Houet, T. and Hubert-Moy, L. (2006). Modeling and projecting land-use and land-cover changes with Cellular Automaton in considering landscape trajectories. EARSeL eProceedings, 5(1), 63-76.
[29] Huang, C., Davis, L. S. and Townshend, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, 23(4), 725-749.
[30] Hyandye, C. and Martz, L. W. (2017). A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. International journal of remote sensing, 38(1), 64-81.
[31] Irwin, E. G., Jayaprakash, C. and Munroe, D. K. (2009). Towards a comprehensive framework for modeling urban spatial dynamics. Landscape ecology, 24(9), 1223-1236.
[32] Karimi, F., Sultana, S., Babakan, A. S. and Suthaharan, S. (2019). An enhanced support vector machine model for urban expansion prediction. Computers, Environment and Urban Systems, 75, 61-75.
[33] Kamusoko, C., Aniya, M., Adi, B. and Manjoro, M. (2009). Rural sustainability under threat in Zimbabwe–simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model. Applied Geography, 29(3), 435-447.
[34] Kolios, S. and Stylios, C. D. (2013). Identification of land cover/land use changes in the greater area of the Preveza peninsula in Greece using Landsat satellite data. Applied Geography, 40, 150-160.
[35]Koomen, E., Stillwell, J., Bakema, A. and Scholten, H. J. (Eds.). (2007). Modelling land-use change: Progress and applications (Vol. 90). Springer Science & Business Media.
[36] Li, K., Feng, M., Biswas, A., Su, H., Niu, Y. and Cao, J. (2020). Driving factors and future prediction of land use and cover change based on satellite remote sensing data by the LCM model: a case study from Gansu province, China. Sensors, 20(10), p.2757.
[37] Li, X., Zhang, J., Li, Z., Hu, T., Wu, Q., Yang, J. and Wang, X. (2021). Critical role of temporal contexts in evaluating urban cellular automata models. GIScience & Remote Sensing, 58(6), 799-811.
[38] Lin, D. and Lin, Y. (2015). Stakeholders of voluntary forest carbon offset projects in China: An empirical analysis. Advances in meteorology.
[39] Liu, J. and Deng, X. (2010). Progress of the research methodologies on the temporal and spatial process of LUCC. Chinese Science Bulletin, 55(14), 1354-1362.
[40] Liu, J., Zhang, Q. and Hu, Y. (2012). Regional differences of China’s urban expansion from late 20th to early 21st century based on remote sensing information. Chinese Geographical Science, 22(1), 1-14.
[41] Maitima, J. M., Olson, J. M., Mugatha, S. M., Mugisha, S. and Mutie, I. T. (2010). Land use changes, impacts and options for sustaining productivity and livelihoods in the basin of Lake Victoria. Journal of sustainable development in Africa, 12(3), 1520-5509.
[42] Mansour, S., Al-Belushi, M. and Al-Awadhi, T. (2020). Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Policy, 91, 104414.
[43] Mantero, P., Moser, G. and Serpico, S. B. (2005). Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 559-570.
[44] Mendoza, M. E., Granados, E. L., Geneletti, D., Pérez-Salicrup, D. R. and Salinas, V. (2011). Analysing land cover and land use change processes at watershed level: a multitemporal study in the Lake Cuitzeo Watershed, Mexico (1975–2003). Applied Geography, 31(1), 237-250.
[45] Midekisa, A., Holl, F., Savory, D. J., Andrade-Pacheco, R., Gething, P. W., Bennett, A. and Sturrock, H. J. (2017). Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing. PloS one, 12(9), e0184926.
[46] Mountrakis, G., Im, J. and Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247-259.
[47] Muller, M. R. and Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology, 9(2), 151-157.
[48] Nahavandya, S. K., Kumar, L. and Ghamisi, P. (2017). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran. arXiv preprint arXiv:1708.01089.
[49] Nguyen, V.T., Le, T.T.H. and La, P.H. (2017). Predicting land use change affected by population growth by integrating logistic regression, Markov chain and cellular automata models. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 35(4), pp.221-230.
[50] Nitze, I., Schulthess, U. and Asche, H. (2012). Comparison of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, 79, 3540.
[51] Parker, D. C., Manson, S. M., Janssen, M. A., Hoffmann, M. J. and Deadman, P. (2003). Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the association of American Geographers, 93(2), 314-337.
[52]Parvar,Z., Shayesteh, k.(2019). Integration of Cellular Automata -Markov (CA-Markov) Model and Logistic Regression to Land-Use Change Prediction: (A Case Study of Gamasiab Basin). Journal of Natural Environment,72(1),1-14. (In Persian)
[53] Pinto, N., Antunes, A. P. and Roca, J. (2021). A cellular automata model for integrated simulation of land use and transport interactions. ISPRS International Journal of Geo-Information, 10(3), 149.
[54] Pontius Jr, R. G. (2000). Comparison of categorical maps. Photogramm. Eng. Remote Sens, 66, 1011-1016.
[55] Pontius Jr, R. G. (2002). Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric engineering and remote sensing, 68(10), 1041-1050.
[56] Pontius, G. R. and Malanson, J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2), 243-265.
[57] Pontius Jr, R. G. and Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), 4407-4429.
[58] Rai, R., Zhang, Y., Paudel, B., Acharya, B. K. and Basnet, L. (2018). Land use and land cover dynamics and assessing the ecosystem service values in the trans-boundary Gandaki River Basin, Central Himalayas. Sustainability, 10(9), 3052.
[59] Sakieh, Y. (2013). Determining the axes of sustainable development of Karaj city based on the simulation of city development and environmental capacity. Master's thesis. Faculty of Natural Resources, University of Tehran. (In Persian)
[60] Salehi, N., Ekhtesasi, M.R. and Talebi, A. (2019). Predicting the trend of land use changes using the Markov chain model (case study: Safaroud Ramsar watershed). Journal of Remote Sensing and Geographic Information System in Natural Resources, 10(1), 106-120. (In Persian)
[61] Sang, L., Zhang, C., Yang, J., Zhu, D. and Yun, W. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3-4), 938-943.
[62] Sampaio, G., Nobre, C., Costa, M. H., Satyamurty, P., Soares‐Filho, B. S. and Cardoso, M. (2007). Regional climate change over eastern Amazonia caused by pasture and soybean cropland expansion. Geophysical Research Letters, 34(17).
[63] Shafizadeh-Moghadam, H., Tayyebi, A., Ahmadlou, M., Delavar, M. R. and Hasanlou, M. (2017). Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth. Computers, Environment and Urban Systems, 65, 28-40.
[64] Shafizadeh-Moghadam, H., Tayyebi, A. and Helbich, M. (2017). Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation. Environmental monitoring and assessment, 189(6), 1-14.
[65] Sidhu, N., Pebesma, E. and Câmara, G. (2018). Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing, 51(1), 486-500.
[66] Singh, S. K., Mustak, S., Srivastava, P. K., Szabó, S. and Islam, T. (2015). Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environmental Processes, 2(1), 61-78.
[67] Steffen, W., Crutzen, P. J. and McNeill, J. R. (2007). The Anthropocene: are humans now overwhelming the great forces of nature. AMBIO: A Journal of the Human Environment, 36(8), 614-621.
[68] Strategic plan and structural (comprehensive) plan of Karaj city, no date in: Ministry of Housing and Urban Development. Unknown place of publication: Housing and urban development organization of Tehran province.194. (In Persian)
[69] Takada, T., Miyamoto, A. and Hasegawa, S. F. (2010). Derivation of a yearly transition probability matrix for land-use dynamics and its applications. Landscape ecology, 25(4), 561-572.
[70] Tsai, Y. H., Stow, D., Chen, H. L., Lewison, R., An, L. and Shi, L. (2018). Mapping vegetation and land use types in Fanjingshan National Nature Reserve using google earth engine. Remote Sensing, 10(6), 927.
[71] Van der Linden, S., Rabe, A., Okujeni, A. and Hostert, P. (2009). Image SVM classification. Application Manual: image SVM version, 2.
[72] Wolfram, S. (2018). Cellular automata and complexity: collected papers. crc Press.
[73] Wu, H., Zhou, L., Chi, X., Li, Y. and Sun, Y. (2012). Quantifying and analyzing neighborhood configuration characteristics to cellular automata for land use simulation considering data source error. Earth Science Informatics, 5(2), 77-86.
[74] Xing, W., Qian, Y., Guan, X., Yang, T. and Wu, H. (2020). A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation. Computers & Geosciences, 137, 104430.
[75] Yang, J., Guo, A., Li, Y., Zhang, Y. and Li, X.) 2019(. Simulation of landscape spatial layout evolution in rural-urban fringe areas: a case study of Ganjingzi District. GIScience & remote sensing. 56, 388-405.
[76] Yang, Q., Li, X. and Shi, X. (2008). Cellular automata for simulating land use changes based on support vector machines. Computers & geosciences, 34(6), 592-602.
[77] Yu, L., Porwal, A., Holden, E. J. and Dentith, M. C. (2012). Towards automatic lithological classification from remote sensing data using support vector machines. Computers & Geosciences, 45, 229-239.
[78] Yu, R., Deng, X., Yan, Z. and Shi, C. (2013). Dynamic evaluation of land productivity in China. Chinese Journal of Population Resources and Environment, 11(3), 253-260.
[79] Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A. and Sharp, J. L. (2018). Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International journal of applied earth observation and geoinformation, 69, 175-185.
[80] Zebardast, L. and Jafari, H.(2011). Evaluating the change process of Anzali wetland using remote sensing and providing a management solution. Journal of Environmental Study, 57(37), 57-64. (In Persian)