Land use changes predicition in Zagros forest areas based on Markov Chain Model
Subject Areas :
natural resorces
Ali Qaderian
1
,
Naser Ahmadi Sani
2
1 - M.Sc. of Agroecology, Mahabad Branch, Islamic Azad University, Mahabad, Iran.
2 - Associate. Prof., Faculty of Agriculture and Natural Resources, Mahabad Branch, Islamic Azad University, Mahabad, Iran. *(Corresponding Author)
Received: 2018-08-22
Accepted : 2019-01-16
Published : 2023-09-23
Keywords:
Sardasht Forests,
Change detection,
Simulation,
Landsat,
Cellular Automata,
Abstract :
Background and Objective: Today, due to increasing population growth and land use changes and its impact on the environment, monitoring and modeling land use changes is one of the main prerequisites for optimum use of land and achieving sustainable development. The purpose of this study was to evaluate and predict the spatial and temporal dynamics of land use in the county of Sardasht, in order to obtain basic information for planning in line with sustainable forest management.Material and Methodology: Data from the Landsat 7 images (+ETM) 2003, and Landsat 8 (OLI) 2015 were analyzed. The Maximum Likelihood algorithm has been used to mapping the land use for the years. The analysis of the change dynamics using traditional Markov Chain and Cellular Automata was predicted for the next 25 years.Findings: The overall accuracy of classified images in 2003 and 2015 was 89% and 94%, respectively, and the Kappa coefficient was 0.87 and 0.92. The results showed that during the period, bout 7% of the forest area has decreased and the agricultural lands has increased by 72%. According to the Markov transmission probability matrix the classes most affected by these changes is the forests and rangelands that changed to agricultural and residential.Discussion and Conclusion: Changes in the extent of forests until 2040 show that the area of forests will decrease continuously. The results of current study could provide quantitative information, which represents a base for assessing the sustainability in the management of Zagros forest ecosystems and for taking actions to mitigate degradation.
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Safianian, A., Khodakarami, L., 2011. Land use mapping using Fuzzy classification. Town and Country Planning, Vol. 3. No. 4. Pp. 95-114. (In Persian)
Rendana, , Abdul-Rahim, S., Mohd-Razi, I.W., Lihan, T., Ali, R.Z., 2015. CA-Markov for Predicting Land Use Changes in Tropical Catchment Area: A Case Study in Cameron Highland, Malaysia. Journal of Applied Sciences, No.15. Pp. 689-695.
Zare-Garizi, A., Bardi-Sheikh, V., Saadaddin, A.R., Salman-Mahiny, A., 2012. Simulating the spatiotemporal changes of forest extent for the Chehelchay watershed (Golestan province), using integrated CA-Markov model. Iranian journal of Forests and Poplar Research, Vol. 20. No. 2. Pp. 273-285. (In Persian)
Yousefi, M., Ashrafi, A., 2016. Urban growth modeling in Bojnurd by using Remote Sensing data (based on neural network and Markov modeling). Regional Planning, Vol. 6. No. 21. Pp. 179-192. (In Persian)
Amiraslani, F., Dragovich, D., 2011. Combating desertification in Iran over the last 50 years: An overview of changing approaches. Journal of Environmental Management, No. 92. Pp. 1-13.
Sohl, T.L., Claggett, P.R., 2013. Clarity versus complexity: Land-use modeling as a practical tool for decision-makers. Journal of Environmental Management, No. 129. Pp. 235-243.
Ramezani, N., Rezae, J., 2014. Land use and land cover change detection for 2025 using CA-Markov. Geographical Researches Quarterly Journal, Vol. 29. No. 4. Pp. 83-96. (In Persian)
Manjarrez-Dominguez, C., Pinedo-Alvarez, A., Villarreal-Guerrero, F., Cortes-Palacios, L., 2015. Vegetation landscape analysis due to land use changes on arid lands. Polish Journal of Ecology, No. 63. Pp. 272-279.
Kraemer, R., Prishchepov, A.V., Muller, D., Kuemmerle, T., Radeloff, V.C., Dara, A., Trekhov, A., Fruhauf, M., 2015. Long-term agricultural land-cover change and potential for cropland expansion in the former Virgin lands area of Kazakhstan. Environmental Research Letters, Vol. 10. No. 5. P. 054012.
Vazquez-Quintero, G., Solis-Moreno, R., Pompa-Garcia, M., Villarreal-Guerrero, F., Pinedo-Alvarez, C., Pinedo-Alvarez, A., 2016. Detection and projection of forest changes by using the Markov Chain model and Cellular Automata. Sustainability, Vol. 8. No. 3. P. 236.
Babykalpana, Y., Thanushkodi, K., 2011. Classification of land use/ land cover change detection using remotely sensed data. International Journal on Computer Science and Engineering, Vol. 3. No. 4. Pp. 150-157.
Vafaei, S., 2012. Monitoing and Predicting of land use changes using remote sensing and GIS. Msc. Thesis, Forestry Department, Tehran University, P. 101. (In Persian)
Aburas, M. M., Ho, Y. M., Ramli, M. F., 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, No. 52. Pp. 380-389.
Ghosh, P., Mukhopadhyay, A., Chanda, A., Mondal, P., Akhand, A., Mukherjee, S., Hazra, S., 2017. Application of Cellular automata and Markov-chain model in geospatial environmental modeling-A review. Remote Sensing Applications: Society and Environment, No. 5. Pp. 64-77.
Jorabian-Shoshtari, Sh., Esmaeli-Sari, A., hosieni, M., Gholamalifard, M., 2013. Using Logestic Regression and Markov Chain for land use changes prediction in east of Mazandaran province. Journal of Natural Environment, Vol. 66. No. 4. Pp. 351-363. (In Persian)
Akinyemi, F. O., Mashame, G., 2018. Analysis of land change in the dryland agricultural landscapes of eastern Botswana. Land Use Policy, No. 76. Pp. 798-811.
Gounaridis, D., Chorianopoulos, I., Symeonakis, E., Koukoulas, S., 2019. A Random Forest-Cellular Automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales. Science of the Total Environment, No. 646. Pp. 320-335.
De Oliveira Barros, K., Ribeiro, C. A. A. S., Marcatti, G. E., Lorenzon, A. S., de Castro, N. L. M., Domingues, G. F., dos Santos, A. R., 2018. Markov chains and Cellular automata to predict environments subject to desertification. Journal of environmental management, No. 225. Pp. 160-167.
Fathizad, H., Karimi, H., Tazeh, M., Tavakoli, M., 2014. Prediction of land use and land cover changes in arid and semi-arid regions using satellite images and Markov Chain model. Desert Management, No. 3. Pp. 61-76. (In Persian)
Imani-Harsini, J., Kaboli, M., Feghhi, J., Taherzadeh, A., 2017. Land use change modelling using Markov Chain and CA. Journal of Environmental of Science and Technology, Vol. 19. No. 1. Pp. 119-129. (In Persian)
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Abdalla, M., Saunders, M., Hastings, A., Williams, M., Smith, P., Osborne, B., Lanigan, G., Jones, M.B., 2013. Simulating the impacts of land use in Northwest Europe on Net Ecosystem Exchange (NEE): The role of arable ecosystems, grasslands and forest plantations in climate change mitigation. Science of the Total Environment, No. 465. Pp. 325-336.
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Eggen, M., Ozdogan, M., Zaitchik, B.F., Simane, B., 2016. Land cover classification in complex and fragmented agricultural landscapes of the thiopian highlands. Remote Sensing, Vol. 8. No. 12. P. 1020.
Ahadnejad, M., 2011. The assessment and predicting of land use changes to urban area using multi-temporal satellite imagery and GIS. Journal of Geographic Information System, No. 3. Pp. 298-305.
Eastman, J.R., Salman Mahiny, A., Kamyab, H., (Translation). 2011. Applied remote sensing and GIS using Idrisi. First Edition, Mehr Mahdis Press, Tehran, P. 582. (In Persian).
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Vazquez, Q.G., Pinedo, A.A., Manjarrez, D.C., De-Leon, M.G., Hernandez, R.A., 2013. Analysis of temperate forest fragmentation using spatial medium-resolution remote sensing in Pueblo Nuevo, Durango. Tecnociencia Chihuahua, No. 7. Pp. 88-98.
Falahatkar, S., Hosieni, M., Salman-Mahiny, A., Aiubi, Sh., 2016. Prediction of land use changes using LCM model. Environmental Researches, Vol. 7. No. 13. Pp. 163-174. (In Persian).
, Food and Agriculture Organization of the United Nations. 2016. State of the World’s Forest. Available online: http://www.fao.org/3/a-i5850e.pdf, accessed on 24 February 2016. P. 36.
