Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Modeling land use changes using Markov chain model and LCM model

Author
Islamic Azad University of Lourestan
Abstract
Land use maps are considered as the most important sources of information in natural resource management. The purpose of this research is to review, model, and predict landslide changes in the 30-year period by LCM model in Shiraz. In this research, TM Landsat 4, 5 and OLI Landsat 8 images were used for 1985, 2000 and 2015 respectively, as well as topographic maps and area coverage. Subsequent validation and detection of changes were made using the prediction model of variation The use of LCM markov and the model of user change approach. The images were classified into four classes of Bayer, garden, urban lands, and arable land for each of the three periods. According to the results, aquaculture is the most dynamic user in the area, which has led to an upward trend during 1985-2015, so that the amount (4337 ha, 12.7%) has been added to this area. The Bayer user change trend was also a downward trend during 1985 to 2015, reducing the 99.1995 hectares of this class. The results of the change in the 1985 changes with a kappa coefficient of 0.88, in the 2000 period with a CAAP of 0.77, and in the period 2015 with a Kappa coefficient of 0.92. The results of the change detection in 2030 are such that if the current trend continues in the region, 20.33% will be added to the crop category, so that in 2030, agricultural cropping will be 95.60% of the area of ​​the area Gets In the Bayer and Garden uses 21.22% and 0.21% of the total area of ​​each user has been reduced and has been added to the urban area. The prediction map derived from the Markov chain model is very important for providing a general view for better management of natural resources.



Keywords

Ahmed, Bayes, Ahmed, Raquib, 2012, Modeling Urban Land Cover Growth Dynamics Using Multi-Temporal Satellite Images: A Case Study of Dhaka, Bangladesh, International Journal of Geo-Information 1, 3-31.
Al-Ahmadi, F, Hames, A. 2009, Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, kingdom of Saudi Arabia., Earth, 20, 1, 167-191.
Amiraslani, Farshad, and Dragovich, Deirdre, 2011, Combating desertification in Iran over the last 50
years: An overview of changing approaches, Journal of Environmental Management, 92, 1-13.
Bell, EJ, 1974, Markov analysis of land use change - an application of stochastic processes to remotely sensed data, Socio-Economic Planning Sciences, 8, 6, 311-316.
Brown, DG, Pijanowski, BC, Duh, J, 2000, Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59, 4, 247-263.
Congalton, R.G., 1991, A review of assessing the accuracy of classifications of remotely sensed data,
Rentote Sensing of Environment, 37, 35-46.
Dontree, S., 2003, Land use dynamics from multitemporal remotely sensed data - a case study Northern
Thailand. Paper (no AD 091) presented at Map Asia, Malaysia.
Gilks, WR, Richardson, S, Spiegelhalter, D.J., 1996, introducing markov chain montecarlo. Markov chain
Monte Carlo in practice, 1: 19- 44.
Gross, JE, Goetz, SJ, Cihlar, J., 2009, Application of remote sensing to parks and protected area monitoring: Introduction to the special issue, Remote Sensing of Environment, 113, 7, 1343-1345.
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.
https://fa.wikipedia.org/wiki/%D8%B4%D9%88%D 8%B4%D8%AA%D8%B1.
Jenerette, G, Darrel, Wu, Jianguo, 2001, Analysis and simulation of land use change in the central Arizona-
Phonix region, USA.Landscape ecology,16, 611-626.
Kamusoko, Courage, Aniya, Masamu, Adi, Bongo and Manjoro, Munyaradzi, 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.
Lambin, EF, Geist HJ., 2008, Land-use and landcover change: local processes and global impacts.
Springer Science & Business Media, New York.
Mas, J.F., H. Puig, H. J.L. Palacio, J.L. & A. Sosa López. A, 2004, Modelling deforestation using GIS and
artificial neural networks, Environmental Modeling & Software, 19: 461–471.
Mas, Jean-François, Melanie, Kolb, Martin, Paegelow, María Teresa, Camacho lmedo, and Thoma,
Houet, 2014, Inductive pattern-based land use/cover change models, A comparison of four software packages,
Environmental Modelling & Software, 51, 94-111.
Mitsova, D, Shuster, W, Wang, X., 2011, A cellular automata model of land cover change to integrate urban
growth with open space conservation, Landscape and Urban Planning, 99, 2, 141-153.
Muller, M. R. and J. Middleton., J., 1994, A Markov model of land-use change dynamics in the Niagara
Region, Ontario, Canada, Landscape Ecology, 9, 151- 157.
Nazarisamani, A.A., Ghorbani, M., Koohbani, H.R., 2010, Assessment of changes in land use in the Taleghan watershed basin in the period from 1987 to 2001, Academic Journal of Range Management Research, 4, 3, 451-442.
Ozesmi, S.L., E.M., Bauer, E.M., 2002, Satellite remote sensing of wetlands, Wetlands Ecology and
Management, 10, 381-402.
Piquer-Rodríguez, Maria, Tobias, Kuemmerle, Domingo, Alcaraz-Segura, Raul, Zurita- Milla, and
Javier, Cabello, 2012, Future land use effects on the connectivity of protected area networks in southeastern Spain, Journal for Nature Conservation, 20 (6), 326-336.
Richards, John A., Xiuping, Jia, 2006, Remote Sensing Digital Image Analysis: An Introduction, 4th
Edition, Springer.
Sohl, Terry L. and Claggett, Peter R., 2013, Clarity versus complexity: Land-use modeling as a practical
tool for decision-makers, Journal of Environmental Management, 129, 235-243.
Upadhyay, Thakur, Solberg, Birger, and Sankhayan, Prem Lall, 2006, Use of odelsmodels to analyseanalyses land-use changes, forest/soil degradation and carbon sequestration with special reference to Himalayan region: A review and analysis, Forest Policy and Economics, 9, 4, 349-371.
Wang, Shi Qing, Zheng, Xizinqi, and Zang, X. B., 2012, Accuracy assessments of land use change simulation based on Markov-cellular automata model, Procedia Environmental Sciences, 13, 1238-1245.
Weng, Q., 2002, Land use change analysis in the Zhujiang Delta of China using satellite remote sensing,
GIS and stochastic modelling, Journal of Environmental Management, 64, 3, 273-284.
Whitford, Walter G., Translated by, Azarnivand, Hossein, and Malekian, Arash, 2008, Ecology of desert
systems, Tehran: University of Tehran. , P. 340.
Wu, Qiong, Li, Hong-qing, Wang, Ru-song, Paulussen, Juergen, He, Yong, Wang, Min, Wang, Bihui,
Wang, Zhen, 2006, Monitoring and predicting land use change in Beijing using remote sensing and GIS,Landscape and Urban Planning, 78 , 322–333.