Urban Growth Modeling in Bojnurd by Using Remote Sensing Data (Based on neural network and Markov modeling changes of land)

Authors

• maryam yousefi
• ali ashrafi

Abstract

In the recent decades, population growth, increasing urbanizationand invading to agricultural land have become a serious environmental problem. Change assessment of Land use/land cover (LULC) is receiving considerable attention in the prospective modeling domain. The study's purposes refers to analyze and predicte of LULC change by using Land Change Modeler and the neural network with an integrated Markov model in 2032. MLP neural network was used for generating LULC maps by using Landsat images from  2005 to 2014. The overall accuracy and kappa coefficients of the maps were up to 82%. The accuracy of transition potential modeling showed high accuracy more than 95.2% in all sub-models. According to the results,  the most salient increase was in urban areas from 1529.38 ha in 2005 to 1837 ha in 2014. This ascending trend will continue in the future and will increase to 2856.31 ha of the total area by year 2003. In conclusion, the study revealed that such models were useful for recognizing the Spatio-temporal LULC change.

Keywords

• Urban growth modeling
• Markov chain
• Multiple-layer Perceptron neural network
• Land use changes modeler
• Bojnurd city

20.1001.1.22516735.1395.6.21.14.2

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References

1. کامیاب، حمیدرضا؛ ماهینی، عبدالرسول؛ حسینی، سید حمزه و مهدی غلامعلی فرد (1390): «کاربرد شبکه عصبی مصنوعی مدلسازی رشد شهری»، مجله پژوهش­های جغرافیای انسانی، شماره 76، تهران، صص 113-99.
2. حدادی، عطاالله؛ صاحبی، محمودرضا؛ مختارزاده، مهدی (1388): «ارائه روش ترکیبی از شبکه­های عصبی نضارت شده و نظارت نشده در طبقه بندی تصاویر سنجش ازدور»، مجله سنجش از دور و GIS ایران، شماره 30، صص 33-50.
3. غلامعلی فرد، مهدی؛ جورابچیان شوشتری، شریف؛ کهنوج، حمزه و محسن میرزایی (1391): «.مدلسازی تغییرات کاربری اراضی سواحل مازندران با استفاده از LCM  در محیط GID»، فصلنامه محیط شناسی، شماره 9، تهران، صص 124-109.
4. مرکز آمار ایران (1390): نتایج سرشماری نفوس و مسکن استان خراستان شمالی، تهران.
   1. Dendoncker, N. Roundsevell, and M. Bogaert, P. (2007): Spatial analysis and modeling of land use distributions in Belgium. Computers, Environment and Urban Systems, 31,p188–205.
   2. Eastman, J.R. (2012): IDRISI Selva Help System. Clark Labs, Clark University, Worcester
   3. Elena G. I. Jacqueline, G. (2001): Theory, data, methods: developing spatially explicit economic models of land use change , 85, pp : 7–24 , DOI: 10.1016/S0167-8809(01)00200-6
   4. Fang, S. George, Z. Gertnera, G. Z. Sun, Z. Andersonc, A. (2005): The Impact of Interactions in Spatial Simulation of the Dynamics of Urban Sprawl, Landscape and Urban Planning,N.73.
   5. Fkirin, M.A. Badwai, S.M. and Mohamed. S.A. (2009): Change Detection Using Neural Network with Improvement Factor in Satellite Images. American Journal of Environmental Sciences .6, pp: 706-713.
   6. Helmy, A.K and El-Taweel, Gh.S. (2010): Using Textural and Spectral Characteristics. American J. of Engineering and Applied Sciences, 4, pp: 604-610.
   7. Gobinda Roy, H.  Fox, M. D. Emsellem, K. (2014): Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales, B. Murgante et al. (Eds.): Springer International Publishing Switzerland, pp: 315–330.
   8. Li, X. & Yeh, A.G. (2002): Neural Network Based Cellular Automata for Simulating Multiple Land Use Changes Using GIS. International Journal of Geographical Information Science, 16, pp: 323–343.
   9. Lu, D.,Weng,Q.(2007): A survey of image classification methods and techniques for improving classification  performance. International Journal of Remote Sensing, 5, pp: 823–870.
   10. Jensen, J. R. (2005): Introductory digital image processing 3rd edition.
   11. Jokar Arsanjani, J. Kainz, W. and Mousivand, A. (2011): Tracking Dynamic Land Use Change Using Spatially Explicit Markov Chain Based on Cellular Automata: the Case of Tehran. International Journal of Image and Data Fusion, 2, pp: 329-345.
   12. Khoi, D.D. and Yuji, M. (2011): Modeling Deforestation Using a Neural Network-Markov Model, the GeoJournal Library, 100, pp: 169-190, DOI 10.1007/978-94-007-0671-2_11, C.
   13. Lippmann, R.P. (1987): An Introduction To Computing With Neural Networks,” IEEE. Acoustics,Speech, and Signal Processing Newsletter, 2 , pp: 4-22.
   14. Richards, J.A. (1999): Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin.
   15. Rumelhart, D.E. Hinton, G.E. Williams, R.J. (1986): Learning Internal Representation by Error Propagation, Parallel Distributed Processing, exploration in the microstructure of Cognition, 1:318-332.
   16. Mostapha, M.R. Lim, H.S, Mat Jafri, M.Z. (2010): Comparison of Neural Network and Maximum Likelihood Approaches in Image Classification .Journal of Applied Science, 22, pp: 2847-2854.
   17. M. Munsi, G. Areendran, and P. Joshi. (2012): Modeling spatio-temporal change patterns of forest cover: a case study from the Himalayan foothills (India). Reg. Environ. Change, 12, pp: 619-632.
   18. Maier, H, R. and Dandyg, C. (2001): Neural Network Based Modelling of Environmental Ariables: a Systematic Approach”, Mathematical and Computer Modelling. 6-7, pp: 669-682.
   19. Pontius, R.G. (2011): Material of the Workshop”Land Change Modeling: Calibration, Validation, Extrapolation, and Interpretation”. XIX Reunion Nacional SELPER-Mexico, 3e7 octubre, Morelia, Mich, Mexico.
   20. Pérez-Vega, A. Mas, J. Ligmann-Zielinska, A. (2012): Comparing two Approaches to Land Use/Cover Change Modeling and Their Implications for The Assessment of Biodiversity Loss in a Deciduous Tropical Forest. Environmental Modelling & Software, 29 ,pp; 11-23
   21. Roy, H.G. Fox, D.M. and. Emsellem, K. (2014): Predicting Land Cover Change in a Mediterranean Catchment at Different Time Scales. Lect Notes Comput Sc Springer. 5, pp: 315-330, [doi: 10.1007/978-3-319-09147-1_23].
   22. Song, C. Woodcock, C.E. Seto, K. C, Lenney,M.p. and Macomber, S. A.(2001):  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects?. REMOTE SENS. ENVIRON, 75, pp: 230–244.

5. Atkinson, P.M. Tattnall, A.R.L, (1997): Neural Networks in Remote Sensing. International Journal  of Remote Sensing, 4, PP: 699-709.
6. Carbajal, E. Crisanto, F.J. Aguilar, F., Aguera, M. A. (2006): Green House Detection Using Neural Network With a Very High Resolution Satellite Image. ISPRS Technical Commission II Symposium, pp: 37-42.
7. Cheng, J. Masser, I. and Ottens, H, (2001): Understanding Urban Growth System: Theories and Methods”, Department of Urban and Regional Planning, ITC Institute, Netherlands.
8. Civco, D. L. Waug, Y. (1994): Classification of multi-spectral, multi- temporal multi-source spatial data using artificial neural networks. In: Proceeding of the ASPRS. Annual Convention, Reno, NV, USA, Pp: 123–133. 
9. Dai, X.L. and. Khorram, S. (1999): Remotely sensed change detection based on artificial neural Networks. Photogrammetric Engineering & Remote Sensing, 10, pp: 1187-1194.