Modeling and predicting land use changes using Markov chain Model (Case study: Ghaleh Jogh, Torbat-e-Heydarieh City)

Document Type : Complete scientific research article

Authors

1 M.Sc. Graduate, Dept. of Nature Engineering and Medicinal Plants, Faculty of Agriculture, University of Torbat Heydarieh, Khorasan-Razavi, Iran.

2 Corresponding Author, Assistant Prof., Dept. of Nature Engineering and Medicinal Plants, Faculty of Agriculture, University of Torbat-Heydarieh, Khorasan-Razavi, Iran.

3 Assistant Prof., Soil Conservation and Watershed Management Research Department, Khorasan-Razavi, Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Khorasan-Razavi, Iran.

Abstract

Modeling and predicting land use changes using Markov chain Model (Case study: Ghaleh Jogh, Torbat-e-Heydarieh City)
Abstract:
Background and objectives: Prediction of land use changes in explaining the interactions between ecosystems and human activities is important for helping decision makers. A Land use map is considered an information resource in natural resource management. Optimal resource management needs to be investigated, as recognition of changes and resource degradation in the past, and proper and principled planning in order to control and control possible future degradation. The purpose of this study was to evaluate the performance of the Markov chain model (CA Markov) in determining and predicting land use changes for the future in the Ghaleh Jogh area located in the city of Torbat-e-Heydarieh.
Materials and Methods: Satellite imagery, remote sensing, and the Markov chain model were used for modeling and detecting land use change. The study area of the Ghaleh Jogh Watershed is from Torbat-e-Heydarieh province. A topographic map with a scale of 1: 25,000 and OLI, ETM +, and Landsat MSS images of 1987, 2002, and 2015 were used to prepare land use maps and the changes process. Satellite imagery with first order polynomial equation was corrected by the method of closest neighboring geometric correction and corrected using linear and histogram explanations. For the categorization of images, the controlled classification method and the maximum probability algorithm with acceptable accuracy are used. A Land use map was prepared in 4 ranges of pasture land, Bayer lands, crops, and gardens. In this research, using the CA Markov model, the 2015 forecast map was compared with the monitored map. The percentage of land use, garden, rangeland, lawn, and arable land use varies from 5, 54, 24, and 17 percent in 1987 to 7, 4, 7, 36, 51, and 7.6 percent in 2015, and the user's map Lands for 2025 and 2040 are foreseen. Model calibration was used for predicting the 2015 model.
Results and Conclusion: The results showed that according to the Kappa coefficient, the prepared map has high accuracy. Based on the results of the detection and simulation of the pasture and cultivating lands, they are among the most unstable classes. Garden lands due to more attention and economic significance, there have not been any significant changes in their extent. In general, the largest changes occurred in pasture and agricultural land. The corrective work done in the region has reduced the area of arable land and added to the rangelands.

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1.Karimzadeh Motlagh, Z., Lotfi, A., Pourmanafi, S., & Ahmadizadeh, S. (2022). Evaluation and Prediction of Land-Use Changes using the CA-Markov Model, Geography and Environmental Planning, 33 (2), 63-80.
2.Fathizadeh, H., Karimi, H., Tazeh, M., & Tavakoli, M. (2013). Projection of Land Use Change and Land Coverage Using Satellite Data and Markov Chain Model (Case Study: Dawarge Plain, Ilam Province). Desert management. 2 (3), 61-76. [In Persian]
3.Esmaeili, F., & Ilanloo, M. (2021). Modeling Land Use Changes Based on Markov Chain in LCM (A Case Study of Ramhormoz), Quarterly Journal of Geography Environment Preparation, 14 (54), 147-166. [In Persian]
4.Guan, D., Gao, W., Watari, K., & Fukahori, H. (2008). Land use change of Kitakyushu based on landscape ecology and Markov model. Journal of Geographical Sciences. 18 (4), 455-468.
5.Zare, M., Nazari Samani, A.A., Khalighi Sigarodi, Sh., Jori, M.H., & Bazrafshan, J. (2016). Prognosis of Land Use Land Use Change Process in Kasaliyan Basin Basin Using the Auto-Markov Cell Model. Iranian Journal of Natural Resources. 70 (2), 373-383. [In Persian]
6.Khazaei, A., Abaspour, M., Babaei Kafaky, S., Taghavi, L., & Rashidi, Y. (2022). Investigating and predicting land use changes in Tehran metropolis using remote sensing technology. Environmental Sciences, Online press. [In Persian]
7.Nikpour, A., Amounia, H., & Nourpasandi, E. (2021). Monitoring and predicting land use changes using landsat satellite images by Cellular Automata and Markov model (Case study: Abbasabad area, Mazandaran province). Journal of RS and GIS for Natural Resources, 12 (2), 35-53. [In Persian]
8.Kianpoor Kal Khajh, M., Pajouhesh, M., & Emamgholizadeh, S. (2022). Evaluation of Markov Chain and Automated Cell Integrated Model in Simulation of Land Use Change and Land Cover of Gotvand Dam. Journal of Water and Sustainable Development, 9 (2), 47-56. [In Persian]
9.Imani Hersini, J., Kaboli, M., Faghihi, J., & Taherzadeh, A. (2016). Modeling the process of land cover change / land use using Markov chain and automated network (Case study of Hamedan province). Quarterly Journal of Environmental Science and Technology. 19 (1), 119-129. [In Persian]
10.Borana, S., & Yadav, S. (2017). Prediction of Land Cover Changes of Jodhpur City Using Cellular Automata Markov Modelling Techniques. International Journal of Engineering Science. 17 (11), 15402-15406.
11.FU, X., Wang, X., & Yang, J. (2018). Deriving suitability factors for CA-Markov land use simulation model based on local historical data. Journal of Environmental Management. 206, 10-19.
12.Naghibi, J. Habibian, H., & Habibbian, M.R. (2010). Determination of vegetation optimum indices for modeling of rangeland vegetation percentage using spectral reflection of satellite images. Journal of Plant Ecophysiology.
1 (3), 63-73. [In Persian]
13.Nickho, N., Iildoromi, A., & Nori, H. (2015). Changes in land use in Malair city using remote sensing. Quarterly Journal of Geography Environment Preparation, 30 (8), 63-86.
14.Singh, V. G., Singh, S. K., Kumar, N., & Singh, R. P. (2022). Simulation of land use/land cover change at a basin scale using satellite data and markov chain model. Geocarto International, pp. 1-26. [In Persian]
15.Mir Alizadehfard, S. R., & Alibakhshi, S. M. (2016). Monitoring and forecasting of land use change by applying Markov chain model and land change modeler (Case study: Dehloran Bartash plains, Ilam). Journal of RS and GIS for Natural Resources, 7 (2), 33-46. [In Persian]
16.Rahnama, M. R. (2021). Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustainable Cities and Society, 64, p. 102548.
17.Koohestani, N., Rastgar, S., Heidari, G., Shataei Joybari, S., & Amirnejad, H. (2020). Monitoring and predicting the trend of changing rangelands using Satelite images and CA-Markov model (Case study: Noor-rood basin, Mazandaran proince). Journal of RS and GIS for Natural Resources, 11 (3), 1-21.
18.Alavi Panah, S. K., Ehsani, A. H., & Omidi, P. (2004). Investigation of desertification and land degradation in Damghan using multi-spectral satellite data. Wildlife Magazine. University of Tehran. 9, 154-143. [In Persian]
19.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.
20.Binh, T. N. K. D., Vormant, N., Hung, N., Hens, L., & Boon, E. K. (2005). Land cover changes between 1968 and 2003 in CIA Nuoc, CA MAU Peninsula, Vietnam. Environment Development and Sustainability. 7, 591-536.
21.Behraveshian, H. (2016). Investigation of Landsat 8 Satellite Image Capabilities in Estimating Rangeland Forage Production (Case Study: Torbatehidiriyah). Master thesis, Torbat-e-Jadeirieh University. [In Persian]
22.Yuan, F., Sawaya, K. E., Loeffelholz, B. C., & Bauer. M. M. (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan. Remote Sensing of the Environment. 98, 317-328
23.Kamusoko, C., & Aniya, M. (2006). Land use/cover change and landscape fragmentation analysis in the bandura district Zimbabwe, Land degradation and Development. 18 (2), 221-233.
24.Shalaby, A., & Tateishi, R. (2007). Remot sensing and for mapping and monitoring land cover and land use changes in the Northwestern coastal zone of Egypt. Applied Geography. 27, 28-41.
25.Salehi, N., Ekhtesasi, M. R., & Talebi, A. (2019). Predicting locational trend of land use changes using CA-Markov model (Case study: Safarod Ramsar watershed), Journal of RS and GIS for Natural Resources, 10 (1), 106-120.