Monitoring land-use changes and predicting their spatio-temporal trends in Hamedan City

Document Type : Original Article


Department of Geography and Spatial Planning, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran


Introduction: Land use change is spreading in developing countries and has negative environmental-ecological consequences. Therefore, access to up-to-date information on land-use change is necessary to analyze the needs of human settlements and adopt appropriate policies that are essential to ensure the future. The study of past, present, and future land use has a fundamental role in the decisions and policies of land-use planners. This study aimed to study land use and its changes during 1993, 2004, and 2019. The CA-MARKOV model also identifies how land-use changes in Hamedan and simulates and predicts land use and its changes in 2050.
Material and methods: In this study, after obtaining satellite images of TM, ETM, and OLI sensors, preprocessing steps including various radiometric and geometric corrections were performed on the images.Then, the classification of satellite images was done using Google Earth software and the vector support algorithm. Based on this, the land uses of the region were divided into four classes: residential and non-residential use, barren lands and poor rangeland, garden lands and irrigated agriculture, and mountainous and rangeland. After land use detection and its changes, the trend of these changes was predicted in 2050 using the automatic cell model and Markov chain due to its high ability to detect spatial-spatial component changes.
Results and discussion: Results indicated that the growth and development of urbanization in this metropolis have led to the city's expansion in this area. So that residential and non-residential land use increased from 0.8% of the total area in 1993 to 2.1% in 2019. The study of land-use changes showed that from 1993 to 2004, 0.2% was added to the rate of residential and non-residential land uses. The next largest increase in land use was in the very poor barren lands and rangeland, which reached 49.8% in 2004. Horticultural and irrigated agriculture land use and mountainous land use decreased by 1.2 and 1.5%, respectively, in 2004 compared to 1993. The land-use area of residential and non-residential construction and barren lands have continued to increase in 2019. The area of these land uses increased by 1.1 and 2.4 %, respectively. Finally, it can be said that from 1993 to 2019, horticultural, agricultural, mountainous, and rangeland uses have been transformed into residential and non-residential construction uses and barren lands. This land conversion has negative consequences for the region's future.
Conclusion: In this study, it was found that the automatic cell model and Markov chain have a high ability to predict future land-use changes. Also, the largest increase in land use was related to residential and non-residential construction and barren lands, and other land uses such as garden lands and irrigated and mountainous agriculture. Rangeland experienced a decrease in area in the region. Therefore, planners should consider this extensive urban growth and development to carry out their plans more efficiently.


Aburas, M.M., Ho, Y.M., Ramli, M.F. and Ash’Aari, Z.H., 2016. The simulation and prediction of Spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation. 52, 380-389.
Al-shalabi, M., Billa, L., Pradhan, B., Mansor, S. and Al-Sharif, A.A., 2013. Modelling urban growth evolution and land-use changes using GIS-based cellular automata and SLEUTH models: the case of Sana'a metropolitan city, Yemen. Environmental Earth Sciences. 70(1), 425-437.
Alsharif, A.A. and Pradhan, B., 2014. Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing. 42(1), 149-163.
Anan, HS, 2019. Contribution to the paleontology, stratigraphy and paleo-biogeography of some diagnostic Pakistanian Paleogene foraminifer in the Middle East. Earth Sciences Pakistan . 3(1), 23-28.
Camara, M., Jamil, N.R.B., Abdullah, A.F.B. and Hashim, R.B., 2020. Integrating cellular automata Markov model to simulate future land-use change of a tropical basin. Global Journal of Environmental Science and Management. 6(3), 403-414.
Farajollahi, F., Asgari H., Ownagk, M., Mahboubi, M. and Salman Mahini, A., 2016. Monitoring and forecasting the trend of spatial and temporal changes in land use/cover (case study: Region Marava Tappeh, Golestan). Remote Sensing and GIS in Natural Resources . 6(4), 1-14. (In Persian with English abstract).
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T. and Hokao, K., 2011. Modeling urban land-use change by the integration of cellular automaton and Markov model. Ecological Modelling . 222(20-22), 3761-3772. Hagen, A., 2003. Fuzzy set approach to assessing similarity of categorical maps. International Journal of Geographical Information Science. 17(3), 235-249.
Hamad, R., Balzter, H. and Kolo, K., 2018. Predicting land use/land cover changes using a CA-Markov model under two different scenarios. Sustainability . 10(10), 3421.
Hathout, S., 2002. The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. Journal of Environmental Management. 66(3), 229-238.
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.
Khawaldah, H. A., Farhan, I. and Alzboun, N.M., 2020. Simulation and prediction of land use and land cover change using GIS, remote sensing and CA-Markov model. Global Journal of Environmental Science and Management. 6(2), 215-232.
Landis, J.R. and Koch, G.G., 1977. The measurement of observer agreement for categorical data. Biometrics . 33(1), 159-174.
Mas, J.F., Kolb, M., Paegelow, M., Olmedo, M.T.C. and Houet, T., 2014. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environmental Modelling and Software. 51, 94-111.
Mehrabi, A., Khabazi, M., Almodaresi, S.A., Nohesara, M. and Derakhshani, R., 2019. Land-use changes monitoring over 30 years and prediction of future changes using multi-temporal Landsat imagery and the land change modeler tools in Rafsanjan city (Iran). Sustainable Development of Mountain Territories . 11(1), 26-35.
Muller, M.R. and Middleton, J., 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, and Canada. Landscape Ecology . 9(2), 151-157.
Omar, N. Q., Ahamad, M. S. S., Hussin, W. M. A. W., Samat, N., and Ahmad, S. Z. B., (2014). Markov CA, multi regression, and multiple decision making for modeling historical changes in Kirkuk City, Iraq. Indian Society of Remote Sensing. 42(1), 165-178.
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.
Qian, J., Zhou, Q. and Hou, Q., 2007. Comparison of pixel-based and object-oriented classification methods for extracting built-up areas in arid zone. In Proceedings Fifth Isprs Workshop on Updating Geo-spatial Databases with Imagery and the 5th Isprs Workshop on DMGISs, 28th-29th August, Urumchi, Xingjizng, China. P.163-171 .
Ruben, G.B., Zhang, K., Dong, Z., and Xia, J., 2020. Analysis and projection of land-use/land-cover dynamics through scenario-based simulations using the CA-Markov model: A case study in guanting reservoir basin, China. Sustainability . 12(9), 3747.
Rimal, B., Zhang, L., Keshtkar, H., Haack, B.N., Rijal, S. and Zhang, P., 2018. Land use/land cover dynamics and modeling of urban land expansion by the integration of cellular automata and markov chain. International Journal of Geo-Information . 7(4), 154.
Rimal, B., Zhang, L., Keshtkar, H., Sun, X. and Rijal, S., 2018. Quantifying the spatiotemporal pattern of urban expansion and hazard and risk area identification in the Kaski District of Nepal. Land. 7(1), 37.
Shafiei Sabet, N., Shakiba A. and Mohammadi, A., 2019. Detection and prediction of land-use changes using the Ca-Markov model (case study: Damavand metropolitan area). Journal of Geographical Information. 28(1),1-16. (In Persian with English abstract).
Shen, S., Chen, L., Fan, C. and GAO, Y., 2019. Dynamic simulation of urban green space evolution based on Ca-Markov model-a case study of Hexi new town of Nanjing city, china. Applied Ecology and Environmental Research. 17(4), 8569-8581.
Stehman, SV. 2004. A critical evaluation of the normalized error matrix in map accuracy assessment. Photogrammetric Engineering and Remote Sensing . 70(6), 743-751.
Subedi, P., Subedi, K. and Thapa, B., 2013. Application of a hybrid cellular automaton–Markov (CA-Markov) model in land-use change prediction: a case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences . 1(6), 126-132.
Torrens, P.M., 2003. Automata-based models of urban systems. In: Longley, P. and Batty, M., (Eds.), Advanced Spatial Analysis. ESRI Press, Redlands, FL, pp. 61-79 .
White, R. and Engelen, G., 2000. High-resolution integrated modeling of the spatial dynamics of urban and regional systems. Computers, Environment, and Urban Systems. 24(5), 383-400.
Wolfram, S., 1983. Statistical mechanics of cellular automata. Reviews of Modern Physics .
55(3), 601-644.