Document Type : Research Paper

Authors

1 PhD. Student of Desertification, College of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources,

2 Assis. Prof. College of Rangeland and Watershed Management, Gorgan University of Agricultural Sciences & Natural Resources

Abstract

Extended Abstract
Introduction
In recent years, the growth of urbanization in Iran and the increase of migration to the major cities have led to the sudden and abnormal expansion of these cities, degradation of fertile lands and natural resources, and irreparable damages to the nature. As the population of the city of Shushtar has increased, there has been a lot of growth in the built lands in the region, causing a large change in the use of the lands around the city and the degradation of the fertile lands in the suburbs; so that, the continuation of this process could cause irreparable damages to the environmental resources of the region. Land-use prediction models are essential in planning for sustainable use of the lands (Kamusoko et al., 2009: 435, Mas et al., 2004: 94, Sohl and Claggett, 2013: 235). In addition, predicting land use changes and creating a relation between these changes and their socio-economic consequences is very important for sustainable land management (Whitford et al., 2008: 340). So far, the Markov-genetic model has been used in several studies. Wu et al. (2006) studied the monitoring and forecasting of the Beijing region of China over a 16-year period and used the Markov chain model and regression to predict the land use. Therefore, the purpose of this study was to investigate the trend of land use changes over the past years and predicting the land use and land use changes using the Markov chain model in the city of Shushtar in Khuzestan province. By predicting land use variations, the development and degradation of the resources can be identified and it can be led to managing the changes in the appropriate pathways (Brown et al. 2000: 247, Hathout, 2002: 229 and Jenerette et al., 2001).
 
Materials & Methods
The study area of this research is Shushtar city with an area of 340645.2 hectares located in the North of Khuzestan province. The software packages used in this research include ArcGIS 10.2, ENVI 4.8 and IDRISI Selva 17.0. The images used to extract ground cover classes include Landsat series satellite images; these images were used in this research due to having a long time series, having an appropriate spatiotemporal resolution to study the land cover changes, and being free. Regarding the existing land uses in the region, the research objectives, and the capabilities of the images used to extract useful information, especially the land use mapping, four land uses including rangeland, irrigated agricultural lands, rainfed agricultural lands and residential lands were considered. In the analysis of the Markov chain, the cover classes are used as the states of the chain. To determine the possibility of a change, the chain needs two land use maps (model inputs), which are usually obtained by processing the satellite images (Mitsova et al., 2011: 141). Markov chain analysis was performed using Markov chain order in the Idrisi Selva software. Markov chain analysis is provided for two purposes, the first matrix is used for calibration and the second one is used to simulate the possible changes occurring in the future. The output of the model also includes the possibility of transforming the state, transition area matrix for each class, and at the end of the conditional probability images for converting different uses (Gilks et al., 1996: 19 and Weng, 2002: 273).
Regarding the trend of changes during these three periods, the irrigated and residential lands classes had an increasing state, but on the contrary, rainfed lands and rangelands classes had been decreasing. The accuracy of classifications is generally more than 77%, and suitable for use in the Markov model. The results of the detection of changes in 2030 are such that if the current trend continues in the region, 20.33% will be added to the area of the irrigated agricultural land use, so that irrigated agricultural land use constitutes 60.95% of the area in 2030. This increase is due to the changes of the land uses of rangeland and rainfed to the irrigated agriculture. The decrease in the rangeland and rainfed classes will be 21.12% and 0/21% respectively which will be added to the area of the irrigated agricultural lands. These changes are more pronounced around the rural areas in the region.
 
Results & Discussion
During the research period, irrigated agriculture has been the most dynamic land use in the region. The area of these lands has increased from 1989 to 2015, so that, 1350131.69 ha has been added to the area of this land use during the three study periods. In the first period, the annual rate of increase was 3650 hectares and in the second period the annual rate increase was 3998 hectares. Considering the lack of change in regional governance and planning, the trend is such, that more than 60 percent of the plain area will be covered by this class in 2030 which can be led to changes in the ecosystem conditions. This result is consistent with the results of Gholamali Fard et al. (2014) in the middle coasts of Bushehr province and is not consistent with the results of Ali Mohammadi et al (2010), Dejkam et al. (2015), and Ramezani and Jafari (2014).
 
Conclusion
In general, the results of this study indicate an increase in the area of irrigated agriculture, as well as development of the Shushtar, which has occurred through the disappearance of rangelands and rainfed lands. As it is well known, if the current strategy of land use in this area continues to reduce natural lands and increase urban lands, regardless of sustainable development considerations until 2030, significant environmental problems, including degradation of rangeland, decline in production of the major agricultural products of the region, decrease in the fertility, and increase in the deserts, will be a serious threat to the future ecosystem of the region. Also, considering the current productivity status, the region's economy which is based on the agricultural and livestock production will face a serious threat in 2030. Therefore, this research recommends the use of resulting maps to identify the sensitive areas for better planning and management of the executive organizations.

Keywords

1- احدنژاد روشتی، زلفی، شکری‌پور؛ محسن، علی، حسین؛1390، ارزیابی و پیش‌بینی گسترش فیزیکی شهرها با استفاده از تصاویر ماهواره‌ای چند زمانه و سیستم اطلاعات جغرافیایی (مطالعه موردی شهر اردبیل 1400-1363)، فصلنامه آمایش محیط، شماره 10715، 15107-124.
2- اصلاح، المدرسی، مفیدی‌فر، ملک‌زاده بافقی؛ مهدی،سیدعلی، مهدی، شاهرخ؛ 1393، بررسی کارایی مدل زنجیره‌ای مارکوف در برآورد تغییرات کاربری اراضی و پوشش زمین با استفاده از تصاویر ماهواره‌ایLANDSAT، نخستین همایش ملی کاربرد مدلهای پیشرفته تحلیل فضایی (سنجش از دور و GIS) در آمایش سرزمین، 10 صفحه.
3- دژکام، جباریان امیری، درویش صفت؛ سیدصادق، بهمن، علی‌اصغر؛ 1394، پیش‌بینی تغییرات کاربری و پوشش زمین در شهرستان رشت با استفاده از مدل سلول­‌های خودکار و زنجیره مارکوف، پژوهش‌­های محیط زیست، 6، 11، 193-204.
4- رمضانی، جعفری؛ نفسیه، رضا؛ 1393، آشکارسازی تغییرات کاربری و پوشش اراضی در افق 1404 با استفاده از مدل زنجیره‌­ای CA مارکوف (مطالعه موردی: اسفراین)، فصلنامه تحقیقات جغرافیایی، 29، 4، 115.
5- علی­‌محمدی، موسیوند، جعفری، شایان؛ عباس، ع.ج.، علی، س. سیاوش؛ 1389، پیش‌­بینی تغییرات کاربری اراضی و پوشش زمین با استفاده از تصاویر ماهواره‌ای و مدل زنجیره‌ای مارکوف، فصلنامه مدرس علوم انسانی، 14، 3، 117-130.
6- غلامعلی فرد، میرزایی، جورابیان شوشتری؛ مهدی، محسن، شریف؛ 1393، مدلسازی تغییرات پوشش اراضی با استفاده از شبکه عصبی مصنوعی و زنجیر ة مارکف (مطالعة موردی: سواحل میانی استان بوشهر)، نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 5، 1، 65-79.
7- کریمی، چوقی؛ کامران، بایرام؛ 1394، پایش، ارزیابی و پیش بینی روند تغییرات مکانی کاربری اراضی/پوشش زمین با استفاده از مدل زنجیره ای مارکوف (مطالعه موردی: دشت بسطاق- خراسان جنوبی)، نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 6، 2، 75-88.
8- نشاط، عبدالمجید، 1381، تجزیه وتحلیل و ارزیابی تغییرات کاربری و پوشش زمین با استفاده از داده‌های سنجش ازدور و سامانه‌های -اطلاعات جغرافیایی در استان گلستان. ، پایان نامه کارشناسی ارشد رشته سنجش ازدور و GIS .، دانشگاه تربیت مدرس.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. Congalton, R.G., 1991, A review of assessing the accuracy of classifications of remotely sensed data, Rentote Sensing of Environment, 37, 35-46.
15. Dontree, S., 2003, Land use dynamics from multi-temporal remotely sensed data - a case study Northern Thailand. Paper (no AD 091) presented at Map Asia, Malaysia.
16. Gilks, WR, Richardson, S, Spiegelhalter, D.J., 1996, introducing markov chain montecarlo. Markov chain Monte Carlo in practice, 1: 19- 44.
17. 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.
18. 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.
19. https://fa.wikipedia.org/wiki/%D8%B4%D9%88%D8%B4%D8%AA%D8%B1.
20. 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.
21. 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.
22. Lambin, EF, Geist HJ., 2008, Land-use and land-cover change: local processes and global impacts. Springer Science & Business Media, New York.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. Ozesmi, S.L., E.M., Bauer, E.M., 2002, Satellite remote sensing of wetlands, Wetlands Ecology and Management, 10, 381-402.
29. 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.
30. Richards, John A., Xiuping, Jia, 2006, Remote Sensing Digital Image Analysis: An Introduction, 4th Edition, Springer.
31. 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.
32. 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.
33. 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.
34. 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.
35. Whitford, Walter G., Translated by, Azarnivand, Hossein, and Malekian, Arash, 2008, Ecology of desert systems, Tehran: University of Tehran. , P. 340.
36. Wu, Qiong, Li, Hong-qing, Wang, Ru-song, Paulussen, Juergen, He, Yong, Wang, Min, Wang, Bi-hui, Wang, Zhen, 2006, Monitoring and predicting land use change in Beijing using remote sensing and GIS, Landscape and Urban Planning, 78 , 322–333.