پایش و پیش‌بینی تغییرات زمانی- مکانی کاربری اراضی و رشد شهر کرمانشاه با استفاده از سنجش از دور و مدل CA-Markov

نوع مقاله : مقالات مستقل پژوهشی

نویسندگان

1 استاد گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی، ساری، ایران

2 استادیار گروه مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی، ساری، ایران

3 دانش آموخته کارشناسی ارشد سنجش از دور و سامانه اطلاعات جغرافیایی، موسسه آموزش عالی آبان هراز، آمل، ایران

چکیده

رشد شهری یک پدیده‌ی جهانی است اما در کشورهای درحال توسعه مانند ایران به دلیل فقدان برنامه‌ریزی صحیح بسیار نامظم صورت می‌گیرد که این مسئله منجر به تخریب پوشش زمین اطراف مناطق شهری شده است. از اینرو در این مطالعه با استفاده از داده‌های سنجش از دور و مدل CA-Markov تغییرات پوشش زمین شهر کرمانشاه در طبقات ارتفاعی و رشد این شهر در جهات جغرافیایی در مقیاس زمانی 1987 تا 2047 بررسی و پیش‌بینی گردید. تحلیل نتایج بررسی تغییرات پوشش زمین نشان می‌دهد که نواحی شهری و کشاورزی روند افزایشی و کاربری‌های پوشش‌گیاهی و زمین‌های بایر روند کاهشی داشته است که بیشتر این تغییرات در ارتفاعات 1042 تا 1587 متری اتفاق افتاده است و این روند تغییرات تا سال‌های 2032 و 2047 ادامه خواهد یافت. همچنین بررسی تاثیر رشد شهر بر تغییرات پوشش زمین نشان می‌دهد با توجه به رشد زیاد شهر در جهات شمال و شمال شرق تخریب اراضی به نواحی شهری در این جهات بیشتر از جهات دیگر رخ خواهد داد. این روند هم در دوره بررسی (1987 تا 2017) و هم در دوره‌ی پیش‌بینی (2017 تا 2047) دیده می‌شود. بدیهی است ارائه الگوهای رشد شهری در جهات جغرافیایی برای برنامه‌های توسعه پایدار بسیار مفید بوده و برنامه ریزان می‌توانند با استفاده از آنها رشد مناطق شهری را به جهات بهینه هدایت نمایند و در نتیجه تخریب اراضی را به حداقل برسانند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Monitoring and Forecasting of Spatiotemporal Changes in Land Use and the Growth of Kermanshah Township Using Remote Sensing and the CA-Markov Model

نویسندگان [English]

  • karim solaimani 1
  • Fatemeh Shokrian 2
  • Shadman Darvishi 3
1 Professor, RS & GIS Centre and Department of Watershed Management, Faculty of Natural resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
2 Assistant Professor, Department of Watershed Management, Faculty of Natural resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
3 M.Sc. of Remote Sensing & GIS, Aban Haraz Higher Education Institute, Amol, Iran
چکیده [English]

In this study, land use changes in Kermanshah Township in altitude classes and the city's growth in geographical directions were investigated and predicted on a time scale of 1987-2047 using remote sensing data and a CA-Markov model. The analysis of land use change results shows that urban and agricultural areas have increased, while vegetation and barren lands have decreased. Most of these changes have occurred at altitudes ranging from 1042 to 1587 meters, and this process of changes will continue until 2032–2047. Also, the investigation of the impact of city growth on land use changes shows that due to the large growth of the city in the north and northeast directions, land destruction will occur in urban areas in these directions more than in other areas. This trend can be seen both in the evaluation period (1987–2017) and the forecast period (2017–2047).

کلیدواژه‌ها [English]

  • Land cover changes
  • elevation classes
  • geographical directions
  • Kermanshah twonship
 
Almeida, C. M., Gleriani, J. M., Castejon, E. F., Soares-Filho, B. S., 2008, Using neural networks and cellular automata for modelling intra-urban land-use dynamics, International Journal of Geographical Information Science, 22(9): 943-963.
Bowen, Richard L., Linda J. Cox, and Morton Fox, (1991), the Interface between Tourism and Agriculture. Journal of Tourism Studies 2(2): 43-54
Celik, A. (2005), Land-use effects on organic matter and physical properties of soil in a southern editerranean highl and of Turkey, Soil and Tillage Research 83(2): 270–277.
Cheng, J. Masser, I., 2003, Urban Growth Pattern Modeling: A Case Study of Wuhan City, PR China, Landscape & Urban Planning, 62(4): 199–217.
Darvishi, Sh, Solaimani, K., Shabani, M., 2020, Analysis and prediction of urban growth and its impact on land use using remote sensing and CA-Markov ; Case study: Marivan, Baneh and Saqqez cities, Journal of Geographical Data (SEPEHR), 29(114): 147-163 (InParsian).
Darvishi, Sh, Solaimani, K., 2021, Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city), Environmental Sciences, 18 (4): 161-182 (InParsian).
Dey, N. N., Al Rakib, A., AlKafy, A., Raikwar, V., 2021, Geospatial modelling of changes in land use/land cover dynamics using Multi-layer Perceptron Markov chain model in Rajshahi City, Bangladesh, Environmental Challenges, 4, 100148
Dixon, B., Candade, N., 2008, Multispectral land use classification using neural networks and support vector machines: one or the other, or both?, International Journal of Remote Sensing, 29(4): 1185-1206 .
Frumkin, H. 2002. Urban sprawl and public health, Public Health Report 117(3): 201-207.
Fang, S., Gertner, G.Z.,  Anderson, A.B., 2007, Prediction of multinomial probability of land use change using a bisection decomposition and logistic regression,  Landscape Ecology, 22(3): 419-430.
Girma, R., Fürst, Ch., Moges, A., 2022, Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift, Environmental Challenges, 6, 100419.
Hegazy, I. R., Kaloop, M. R., 2015, Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt, International Journal of Sustainable Built Environment, 4(1): 117–124.
Halmy, M. W. A, Gessler, P. E, Hicke, G. A, Salem, B. B, 2015, Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA, Applied Geography, 63: 101-112.
 Hamad, R.; Kolo, K.; Balzter, H. 2018,  Land Cover Changes Induced by Demining Operations in Halgurd-Sakran National Park in the Kurdistan Region of Iraq. Sustainability, 7(10):1-15.
Hendrik Prinz,J, Wu, H, Sarich, M, Keller, B, Senne, M, Held, M, Chodera, J. D, Schütte, Ch, Noé, F, 2011, Markov models of molecular kinetics: Generation and validation, The Journal of  chemical physics 134 (17): 1-23.
Hishe, S. Bewket, W, Nyssen, J, Lyimo, J, 2018, Analyzing past land use land cover change and CAMarkov based future modeling in the Middle Suluh Valley, Northern Ethiopia, Journal of Geocarto International, 35(3): 225-255.
Houghton, R.1984. The Worldwide Extent of Land-use Change. Bioscience, 44(5), 305–313.
Huang, B., Zhao, B., Song, Y., 2018, Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery, Remote Sensing of Environment, 214: 73-86.
Itten, K. I., Meyer, P., 1993, Geometric and radiometric correction of TM data of mountainous Forested Areas, IEEE Transactions on Geoscience and Remote Sensing 31(4):764 – 770.
Jensen, J.R. 2007, Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd Edition, Prentice Hall Publisher, 316 p.
Karimi, K., Zehtabian, Gh., Faramarzi, M., Khosravi, H., 2016, Land Use /Cover Change Monitoring and Prediction Using Markov Chain (Case Study: The Abbas Plain), Journal of range and watershed management, 69 (3): 711-724 (InParsian).
Karimzadeh Motlagh, Z.,  Lotfi, A., Pourmanafi, S., Ahmadizadeh, S., 2021. Evaluation and Prediction of Land-Use Changes using the CA_Markov Model, Geography and Environmental  Planning, 33(2): 63-80 (InParsian).
Khawaldah, H. A, 2016. A Prediction of Future Land Use/Land Cover in Amman Area Using GIS-Based Markov Model and Remote Sensing, journal of Geographic Information System, 8(3): 412-427.
Kiptala, J., Mohamed , Y, Mul, M. L., Cheema, M.J.M., Van der Zaag, P. 2013. Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin, Eastern Africa. Physics and Chemistry of the Earth, Parts A/B/C, 66: 112-122.
Lausch, A. and Herzog, F., 2002. Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecological indicator,2(1-2),3-15.
Lillesand, T.M., Kiefer, R.W, 1994. Remote sensing and image interpretation, 3rd Edition, John Wiley and Sons publisher, 750 p.
Naidoo, R. Hill, K. 2006. Emergence of indigenous vegetation classifications through integration of traditional ecological knowledge and remote sensing analyses, Environmental Management, 38(3): 377-387.
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. (InParsian)
Omar, N. Q, Sanusi, S. A. M, Hussin, W. M.W, Samat, N, Mohammed, K.s, 2014, Markov-CA model using analytical hierarchy process and multi regression technique, 7th IGRSM International Remote Sensing & GIS Conference and Exhibition,20: 1-17.
Pijanowskia, B.C., Brown, D.G., Shellitoc, B.A. and Manikd, G.A., 2002, Using Neural Networks and GIS to Forecast Land Use Changes: A Land Transformation Model, Computers, Environment and Urban Systems 26(6), 553 –575.
Quintero, G, V, Moreno, R, S, García, M, P, Guerrero, F, V, Alvarez, C, P, Alvarez, A, P, 2016, Detection and Projection of Forest Changes by Using the Markov Chain Model and Cellular Automata, Sustainability 2016, 8, 236: 1-13.
Rafiee, R., A. Salman Mahiny and N. Khorasani. 2009. Assessment of changes in urban green spaces of Mashhad city using satellite data. International Journal of Applied Earth Observation and Geoinformation 11: 431-438.
Richards. J. A., Xiuping. J .2006. Remote sensing Digital Image Analysis, An Introduction, 4th Edition, Springer-Verlag Berlin Heidelberg, 439 p.
Reveshty, M.A., 2011. The assessment and predicting of land use changes to urban area using multi-temporal satellite imagery and GIS: A case study on Zanjan, Iran (1984-2011). J. Geogr. Inform. Syst., 3(4): 298-305.
Shamsipour, A. A., Heydari, S., Bagheri, K., 2017, Monitoring the Process of Land Use/cover Changes Using Markov CA Model: a Case Study of Kermanshah City, Geographical Urban Planning Research, 5 (3): 495-514 (InParsian).
Siddiqui, A., Siddiqui, A., Maithani, S., Jha, A. K., Kumar, P., Srivastav, S. K., 2018, Urban growth dynamics of an Indian metropolitan using CA Markov and Logistic Regression, The Egyptian Journal of Remote Sensing and Space Science,21(3): 229-236.
Smits, P. C., Dellepiane, S. G., Schowengerdt, R. A.  1999. Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach, International Journal of Remote Sensing, 20(8):1461-1486.
Statistical Centre of Iran, 2016, Population-and-Housing-Censuses  in 1986 and 2016, https://www.amar.org.ir/english/Population-and-Housing-Censuses (InParsian)
Surabuddin Mondal, M. D, Sharma, N, Garg, P. K, Kappas, M, 2016, Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results, The Egyptian Journal of Remote Sensing and Space Sciences 19(2): 259–272.
Tayyebi, A., Delavar, M. R., Yazdanpanah. M. J., Pijanowski, B. J., 2010, A spatial logistic regression model for simulating land use patterns: A case study of the Shiraz Metropolitan Area of Iran, Advances in Earth Observation of Global Change, 27-42.
Tang, Z., B.A. Engel, B.C. Pijanowski and K.J. Lim, 2005. Forecasting land use change and its environmental impact at a watershed scale. J. Environ. Manage., 76(1): 35-45.
 Thapa, R.B., Murayama.Y., 2012. Scenario based urban growth allocation in Kathmandu Valley, Nepal. Landscape and Urban Planning 105(1-2): 140-148.
Tso, B., Mather, P. 2001. Classification methods for remotely sensed Data, Taylor& Francis publisher, first Edition, 253 p.
US Geological Survey. 2017. Landsat-8 images and Shuttle Radar Topography Mission Digital elevation model. Retrieved from https://earthexplorer.usgs.gov
Van Dessel, W., Van Rompaey, A., Szilassi, P., 2011, Sensitivity analysis of logistic regression parameterization for land use and land cover probability estimation, International Journal of Geographical Information Science , 25(3):489-508.
Vaz, E., Arsanjani, J.J., 2015, Predicting Urban Growth of the Greater Toronto Area - Coupling a Markov Cellular Automata with Document Meta-Analysis, Journal of Environmental Informatics, 25(2): 71-80.
 Wang, S. Q., Zheng, X. Q., Zang, X. B., 2012, Accuracy assessments of land use change simulation based on Markov-cellular automata model, Procedia Environmental Sciences, 13: 1238-1245.
Wagrowski, D. M., . Hites, R. A., 1996. Polycyclic aromatic  hydrocarbon accumulation in urban, suburban and rur ual vegetation,  Environmental Science & Technology, 31(1): 279- 282
Zhang, R, Tang, Ch. Ma, S. Yuan, H, Gao, L, Fan, W, 2011, Using Markov chains to analyze changes in wetland trends in arid Yinchuan Plain‖, China. Mathematical and Computer Modeling, 54(3-4): 924-930.