Department of Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran
Abstract
Introduction:
Principal land use management requires accurate and timely information in the form of maps. Considering the widespread and unsustainable changes in land use, including the destruction of natural resources in recent years, it is essential to study the changes in land covers over the time using satellite imagery. Because the conservation of natural resources requires continuous monitoring of an area, land-use change models are now used to identify and predict land-change trends and land degradation. One of the most widely used models in predicting land use change is the automated Markov chain model. The purpose of this study is to monitor land use changes in Abu Ghovair Plain in the past years and predict their status in the next 13 years. Material and methods:
In this study, in order to detect the changes in the study, TM, ETM+, and OLI images of Landsat satellite were used in the years 1990, 2003 and 2016, respectively. After applying geometric and atmospheric corrections to images, the land use map was created for each year. Then, to predict the changes in 2029 using the Markov chain in the Idrisi Selva software, the mapping of the years 1990 and 2003 were selected as the input to the model. Then, 13 years of forecasting changes were considered until 2016 to get the matrix of the likelihood of user changes. Then, data from the Markov chain method and the map of 2016 were used as input data for the CA-Markov cell method. Results and discussion:
The results of monitoring satellite images from 1969 to 2016 indicated a gradual increase in sandy areas by 62 km2 and its movement towards poor rangelands and shrubs. The agricultural lands were increased so that at the end of the period their size has increased by 67.68 km2. Residential land has also been expanded over the years, and the size of the shrubland has been reduced. After tracking the changes, the 2016 map was simulated by the model. Evaluating the accordance between the simulated map and the actual map with the Kappa index confirmed the accuracy of the model. Then, the 2029 map was prepared to predict the changes over the coming years. The discovery of changes in 2029 indicated that if the current trend continues, the area of the sand zones will increase to the extent of covering 15% of the area. In this period, the most changes will occur in the middle part of the southeast to the south of the area. The size of the shrubland will decrease by 13 km. The changes in agricultural lands continue to grow and will encompass 10% of the whole region in 2029. Conclusion:
Comparison between the simulated map of 2016 generated by the model and actual map with Kappa index showed that Auto-Markov model is a suitable model for predicting land use change and can be used to accurately assess the future status of land use and vegetation.
Abdul Alizadeh, Z. and Ebrahimi, A., 2015. Prediction of Future Changes in Landscape Conservation in Sabzkouh Protected Area Using the CA Markov Model. Journal of Raneland and Desert Research. 22, 30-21.
Arekhi, S., 2014, Prediction of the land use changes using the LCM model in the GIS (Case study: Saraboleh Area)". Journal of Research in Protection of Forests and Rangelands of Iran. 12, 1-19.
Dong, Z., Wang, X. and Chen, G., 2000. Monitoring sand dune advance in the Taklimakan Desert. Geomorphology. 35, 219-231.
Fathizad, H., Rostami, N. and Faramarzi, M., 2015. Detection and prediction of land cover changes using Markov chain model in semi-arid rangeland in western Iran. Journal of Environmental Monitoring and Assessment. 187(10), 629. 1-12.
Garcia, A., Sante, I., Crecente, R. and Miranda, D., 2011. An analysis of the effect of the stochastic component of urban cellular automata models. Computers, Environment and Urban Systems. 35, 289-296.
Ghorbani, R., Pourmohammadi, M. and Mahmoodzadeh, H., 2013. Environmental approach in modeling land use change in Tabriz Metropolitan area using Multi-Temporal satellite images, multivariate evaluation and Markov Markets Automatic Cells (1994-2038). Journal of Urban Studies. 8, 30-13
Jafari, M., Majedi, H., Monavari, S.M., Alesheikh, A.A. and Zarkesh, M.K., 2016. Dynamic simulation of urban expansion through a CA-Markov model Case study: Hyrcanian region, Gilan, Iran. European Journal of Remote Sensing. 49, 513-529.
Heidari Zadi, Z. and Mohammadi, A., 2017. Prediction of land use change in Mehran plain using the Auto-Markov Cell model. Journal of the Ecosystem of Desert Engineering. 10, 57-68.
Koolhoven, E., Hendrikse, W., Nieuwenhuis, W., Retsios, B., Schouernburg, M., Wang, L., Buelde, P. and Nijmeijer, R., 2005. ILWIS 3.3 Academic, TC, Netherland.
Norris, J.R., 1997. Markov Chains. Cambridge University Press, UK.
Pakparvar, M., Gabriels, D., Aarabi, K., Edraki, M., Raes, D. and Cornelis, W., 2012. Incorporating legacy soil data to minimize errors in salinity change detection: a case study of Darab plain, Iran. International Journal of Remote Sensing. 33(19), 6215-6238.
Rafiee, R., Mahiny, A. and Khorasani, N., 2009. Assessment of changes in urban green spaces of Mashad city using satellite data. International Journal of Applied Earth Observation and Geoinformation. 11(6) 431-438.
Rajabzadeh, F., 2016. Land use changes in Southwest of Tehran using remote sensing and Markov chain technique. Journal of Soil and Water Resources Conservation. 6(2), 59-72.
Sanjari, S. and Boroumand, N., 2013. Monitoring of land use / vegetation changes in the last three decades using remote sensing technique (Case study: Zarand, Kerman). Journal of Remote Sensing and GIS in Natural Resources. 1, 67-57.
Sante, I., Garcia, A., Miranda, D. and Crecente, R., 2010. Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning. 96, 108-122.
Shahriat Panahi, M., Mohammadi, SH., Moshiri, S.R., Fotouhi, J. and Steelagi, A., 2010. Methods for studying and analyzing land use changes with emphasis on measuring and analyzing applied data on Gorgan area. Sepehr Journal. 19(73): 48-52.
Sorodi, M. and Jozi, S.A., 2011. Predicting of vegetation changes using Markov model (case study: district 4 of Tehran municipality). Application of Remote Sensing and GIS in Environmental Sciences. 2(2), 83-95.
Subedi, P., Subedi, K. and Thapa, B., 2013. Application of a hybrid cellular automaton â Markov (CA-Markov) model in land-use change prediction: A case study of Saddle Creek Drainage Basin, Florida. Applied Ecology and Environmental Sciences. 6, 126-132.
Veldkamp, A. and Lambin E.F., 2001. Predicting land-use change. Agriculture, Acosystems and Environment. 85 (1), 1-6.
Wolfram, S., 1984. Cellular automata as models of complexity. Nature. 311, 61-67.
Yao, Z.Y., Wang, T., Han, Z.W., Zhang, W.M. and Zhao, A.G., 2007. Migration of sand dunes on the Northern Alxa Plateau, Inner Mongolia, China. Journal of Arid Environments. 70, 80-93.
Zobairi, M. and Majd, A., 2006. Familiar with the Technique of Measuring and Using in Natural Resources (Satellite Data, Aerial Photos, Space). Tehran University Press, Iran.
Mohamadian Behbahani, A., & Heidarizadi, Z. (2018). Monitoring and predicting the trend of sand zone changes using the CA-Markov model (case study: Abu Ghovair plain, Dehloran, Ilam province). Environmental Sciences, 16(4), 153-166.
MLA
Ali Mohamadian Behbahani; Zahedeh Heidarizadi. "Monitoring and predicting the trend of sand zone changes using the CA-Markov model (case study: Abu Ghovair plain, Dehloran, Ilam province)". Environmental Sciences, 16, 4, 2018, 153-166.
HARVARD
Mohamadian Behbahani, A., Heidarizadi, Z. (2018). 'Monitoring and predicting the trend of sand zone changes using the CA-Markov model (case study: Abu Ghovair plain, Dehloran, Ilam province)', Environmental Sciences, 16(4), pp. 153-166.
VANCOUVER
Mohamadian Behbahani, A., Heidarizadi, Z. Monitoring and predicting the trend of sand zone changes using the CA-Markov model (case study: Abu Ghovair plain, Dehloran, Ilam province). Environmental Sciences, 2018; 16(4): 153-166.