Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata-Markov chain and Multi-layer Perceptron-Markov chain models

Remote Sensing

Volumn 7, Issue 5, 2015, Pages 5918-5950

  Ozturk, Derya ^a  

^a ONDOKUZ MAYIS UNIVERSITY   (Turkey)

Author keywords

Atakum; Cellular automata; Geographical information system; Land use land cover; Landsat; Markov chain; Multi layer perceptron; Simulation validity; Urban growth simulation

Indexed keywords

CELLULAR AUTOMATA; CHAINS; GEOGRAPHIC INFORMATION SYSTEMS; INFORMATION SYSTEMS; LAND USE; MARKOV PROCESSES; MOBILE SECURITY;

ATAKUM; GROWTH SIMULATION; LAND USE/LAND COVER; LANDSAT; MULTI LAYER PERCEPTRON; SIMULATION VALIDITY;

URBAN GROWTH;

EID: 84930027723     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70505918     Document Type: Article

References (88)

1. Sezgin, D.; Varol, C. Ankara'daki kentsel büyüme ve saçaklanmanin verimli tarim topraklarinin amaç dişi kullanimina etkisi. METU JFA 2012, 29, 273-288.
2. Gold, R.L. Ranching, Mining and the Human Impact of Natural Resource Development; Transaction: Piscataway, NJ, USA, 1985.
3. Ersoy, M.; Sengul, T. Kalkinma kurumlarinda kentler. Iktisat Derg. 1997, 97, 70-78.
4. Kilinc, Z.A.; Bezci, B. Kentlesme, Gecekondu ve Hemserilik. (in Turkish). Available online: http://dergipark.ulakbim.gov.tr/akademikincelemeler/article/download/5000049791/5000047078 (accessed 8 January 2015).
5. Chen, Y.T. Sketch technology roadmap by using a novel vision-oriented service innovation approach: Environmental-technology roadmap for designing sustainable city as an example. In Information Technologies in Environmental Engineering; Golinska, P., Fertsch, M., Marx-Gómez, J., Eds.; Springer-Verlag: Heilderberg, Germany, 2011; pp. 135-149.
6. Zang, S.; Wu, C.; Liu, H.; Na, X. Impact of urbanization on natural ecosystem service values: A comparative study. Environ. Monit. Assess. 2011, 179, 575-588.
7. Yeh, C.-T.; Huang, S.-L. Global urbanization and demand for natural resources. In Carbon Sequestration in Urban Ecosystems; Lal, R., Augustin, B., Eds.; Springer: Heidelberg, Germany, 2012; pp. 355-371.
8. Akseki, H.; Meshur, M.C. Kentsel yayilma sonucu yapilasmaya açilan verimli tarim alanlari: Konya kenti. Megaron 2013, 8, 165-174.
9. Speth, J.G. Global Environmental Challenges: Transitions to a Sustainable World; Orient Longman Privat Ltd.: New Delhi, India, 2004.
10. Blanco-Canqui, H.; Lal, R. Principles of Soil Conservation and Management; Springer: New York, NY, USA, 2008.
11. Musaoglu, N.; Gurel, M.; Ulugtekin, N.; Tanik A.; Seker, D.Z. Use of remotely sensed data for analysis of land-use change in a highly urbanized district of mega city, Istanbul. J. Environ. Sci. Health 2006, 41, 2057-2069.
12. Pine, J.C. Natural Hazards Analysis: Reducing the Impact of Disasters; CRC Press Taylor Francis: Boca Raton, FL, USA, 2008.
13. Seto, K.C.; Fragkias, M.; Guneralp, B.; Reilly, M.K. A meta-analysis of global urban land expansion. PLoS ONE 2011, 6, e23777.
14. Fernando, H.J.S.; Dimitrova, R.; Sentic, S. Climate change meets urban environment. In NATO Science for Peace and Security Series C: Environmental Security, National Security National Security and Human Health Implications of Climate Change; Fernando, H.J.S., Klaic, Z.B., McCulley J.L., Eds.; Springer: Dordrect, The Netherlands, 2012; pp. 115-133.
15. Pacione, M. Urban Problems: An Applied Urban Analysis; Routledge: London, UK, 1990.
16. Bhatta, B. Analysis of Urban Growth and Sprawl from Remote Sensing Data; Springer-Verlag: Heidelberg, Germany, 2010.
17. Kaya, S.; Basar, U.G.; Karaca, M.; Seker, D.Z. Assessment of urban heat islands using remotely sensed data. Ekoloji 2012, 21, 107-113.
18. Onate-Valdivieso, F.; Bosque-Sendra, J. Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. J. Hydrol. 2010, 395, 256-263.
19. Arsanjani, J.J.; Helbich, M.; Kainz, W.; Boloorani, A.D. Integration of logistic regression, markov chain and cellular automata models to simulate urban expansion. Int. J. Appl. Earth Observ. Geoinf. 2013, 21, 265-275.
20. Liu, L.; Wang, X.; Eck, J.; Liang, J. Simulating crime events and crime patterns. In Geographic Information Systems and Crime Analysis; Wang, F., Ed.; Idea Group Inc.: Calgary, AB, USA, 2005; pp. 197-213.
21. Maktav, D.; Sunar Erbek, F.; Jurgens, C. Remote sensing of urban areas. Int. J. Remote Sens. 2005, 26, 655-659.
22. Tewolde, M.G.; Cabral, P. Urban sprawl analysis and modeling in Asmara, Eritrea. Remote Sens. 2011, 3, 2148-2165.
23. Duran, Z.; Musaoglu, N.; Seker, D.Z. Evaluating urban land use change in historical peninsula, Istanbul, by using GIS and remote sensing. Fresenius Environ. Bull. 2006, 15, 806-810.
24. Hu, Z.; Lo, C.P. Modeling urban growth in Atlanta using logistic regression. Comput. Environ. Urban Syst. 2007, 31, 667-688.
25. Mozumder, C.; Tripathi, N.K. Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network. Int. J. Appl. Earth Observ. Geoinf. 2014, 32, 92-104.
26. Agarwal, C.; Green, G.M.; Grove, J.M.; Evans, T.P.; Schweik C.M. A Review and Assessment of Land-use Change Models: Dynamics of Space, Time, and Human Choice; General Technical Report NE-297; U.S. Department of Agriculture, Forest Service, Northeastern Research Station: Washington, DC, USA, 2002.
27. Chu, H.-J.; Lin, Y-P.; Wu, C.-F. Forecasting space-time land use change in the Paochiao Watershed of Taiwan using demand estimation and empirical simulation approaches. Lect. Notes Comput. Sci. 2010, 6016, 116-130.
28. Rui, Y.; Ban, Y. Urban growth modeling with road network expansion and land use development. In Advances in Cartography and GIScience: Selection from ICC 2011; Springer: Heidelberg, Germany, 2011; pp. 399-412.
29. Triantakonstantis, D.; Stathakis, D. Urban growth prediction in Athens, Greece, using artificial neural networks. Int. J. Civil Struct. Constr. Archit. Eng. 2015, 9, 193-197.
30. Triantakonstantis, D.; Mountrakis, G. Urban growth prediction: A review of computational models and human perceptions. J. Geogr. Inf. Syst. 2012, 4, 555-587.
31. Thapa, R.B.; Murayama, Y. Scenario based urban growth allocation in Kathmandu Valley, Nepal. Landsc. Urban Plan. 2012, 105, 140-148.
32. Basse, R.M.; Omrani, H.; Charif, O.; Gerber, P.; Bodis, K. Land use changes modelling using advanced methods: Cellular automata and artificial neural networks. The spatial and explicit representation of land cover dynamics at the cross-border region scale. Appl. Geogr. 2014, 5, 160-171.
33. Mondal, M.S.; Sharma, N.; Kappas, M.; Garg, P.K. Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques. Geocarto Int. 2013, 28, 632-656.
34. Sun, H.; Forsythe, W.; Waters, N. Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada. Netw. Spat. Econ. 2007, 7, 353-376.
35. Maithani, S. Neural networks-based simulation of land cover scenarios in Doon valley, India. Geocarto Int. 2015, 30, 163-185.
36. Bharath, H.A.; Vinay, S.; Ramachandra, T.V. Prediction of land use dynamics in the rapidly urbanising landscape using land change modeller. In Proceedings of the International Conference on Advances in Computer Science, AETACS, NCR Delhi, India, 13-14 December 2013.
37. Ahmed, B. Modelling spatio-temporal urban land cover growth dynamics using remote sensing and GIS techniques: A case study of Khulna City. J. Bangladesh Inst. Plan. 2011, 4, 15-32.
38. Ahmed, B.; Ahmed, R. Modeling urban land cover growth dynamics using multi-temporal satellite images: A case study of Dhaka, Bangladesh. ISPRS Int. J. Geoinf. 2012, 1, 3-31.
39. Baysal, G. Urban Land Use and Land Cover Change Analysis and Modeling a Case Study Area Malatya, Turkey. Master's Thesis, Institute for Geoinformatics (IFGI), Westfälische Wilhelms-Universität, Münster, Germany, 2013.
40. Mas, J.-F.; Kolb, M.; Paegelow, M.; Camacho Olmedo, M.T.; Houet, T. Inductive pattern-based land use/cover change models: A comparison of four software packages. Environ. Model. Softw. 2014, 51, 94-111.
41. Ongsomwang, S.; Pimjai, M. Land Use and Land Cover Prediction and Its Impact on Surface Runoff. Available online: http://ird.sut.ac.th/e-journal/Journal/suwit/1403843/1403843.pdf (accessed on 30 April 2015).
42. Atakum Municipality. Atakum Belediyesi 2015-2019 Stratejik Plani; Atakum Municipality: Atakum, Turkey, 2014.
43. District Governorship of Atakum. Degisirken Gelisen Atakum; District Governorship of Atakum: Atakum, Turkey, 2013.
44. Turkish Statistical Institute. Statistical Indicators. Available online: http://www.tuik.gov.tr/(accessed 7 January 2015).
45. Miller, H.M.; Sexton N.R.; Koontz, L.; Loomis, J.; Koontz, S.R., and Hermans, C. The Users, Uses, and Value of Landsat and Other Moderate-Resolution Satellite Imagery in the United States-Executive Report; U.S. Geological Survey Open-File Report 2011-1031, U.S. Geological Survey: Reston, VA, USA, 2011.
46. Weng, Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens. Environ. 2012, 117, 34-49.
47. U.S. Geological Survey. Earth Explorer. Available online: http://earthexplorer.usgs.gov/(accessed on 3 January 2015).
48. U.S. Geological Survey. Landsat Bulk Metadata Service. Available online: http://landsat.usgs.gov/consumer.php (accessed on 4 January 2015).
49. Eastman J.R. IDRISI Selva Manuel; Clark University: Worcester, MA, USA, 2012.
50. Herold, M.; di Gregorio, A. Evaluating land-cover legends using the UN land-cover classification system. In Remote Sensing of Land Use and Land Cover: Principles and Applications; Giri, C.P., Ed.; Taylor & Francis: Boca Raton, FL, USA, 2012; pp. 65-90.
51. Lillesand, M.T.; Kiefer, W.R. Remote Sensing and Image Interpretation; John Wiley & Sons: New York, NY, USA, 2000.
52. Zheng H.W.; Shen G.Q.; Wang H.; Hong J. Simulating land use change in urban renewal areas: A case study in Hong Kong. Habitat Int. 2015, 46, 23-34.
53. Eastman J.R. IDRISI Selva Tutorial; Clark University: Worcester, MA, USA, 2012.
54. Yuzer, M.A; Yuzer, S. Cellular automata tabanli LUCAM modeli ile Istanbul'un gelisim ve dönüşümüne ilişkin makro form simülasyonlari. J. I˙stanb. Kült. Univ. 2006, 4, 231-244.
55. Ayazli, I.E. Ulaşim Ağlarinin Etkisiyle Kentsel Yayilmanin Simülasyon Modeli: 3. Bogaz Köprüsü örneği. Ph.D. Thesis, Yildiz Teknik üniversitesi, I˙stanbul, Turkey, 2011. (in Turkish).
56. Houet, T.; Hubert-Moy L. Modelling and projecting land-use and land-cover changes with a cellular automaton in considering landscape trajectories: An improvement for simulation of plausible future states. EARSeL eProc. 2006, 5, 63-76.
57. Wang, S.; Zheng, X.; Wang L. Simulation of precise scale land use change based on the Markov-cellular Automata Model. In Proceedings of the 18th International Conference on Geoinformatics, Beijing, China, 18-20 June 2010; 702-706.
58. Pham, D.T.; Alcock R.J. Smart Inspection Systems: Techniques and Applications of Intelligent Vision; Academic Press: San Diego, CA, USA, 2003.
59. Broese, E.; Löffler, H.U. Artificial neural network. In Continuum Scale Simulation of Engineering Materials: Fundamentals-Microstuctures-Process Applications; Raabe, D., Roters, F., Barlat, F., Chen, L.-Q., Eds.; Wiley-VCH Verlag: Weinheim, Germany, 2004; pp. 185-198.
60. Albuquerque, A.C.M.L.; Melo, J.D.; Dória Neto, A.D. Evolutionary computation and parallel processing applied to the design of multilayer perceptrons. In Evolvable Machines: Theory and Practice (Studies in Fuzziness and Soft Computing); Nedjah, N., Mourelle, L.M, Eds.; Springer-Verlag: Heidelberg, Germany, 2005, Volume 161, pp. 181-203.
61. Driggers, R.G. Automatic target detection. In Encyclopedia of Optical Engineering; Marcel Dekker: New York, NY, USA, 2003; Volume 1, pp. 101-114.
62. Paegelow, M.; Camacho Olmedo, M.T. Modelling Environmental Dynamics: Advances in Geomatic Solutions; Springer-Verlag: Berlin, Germany, 2008.
63. Camacho Olmedo, M.T.; Paegelow, M.; Mas, J.F. Interest in intermediate soft-classified maps in land change model validation: Suitability versus transition potential. Int. J. Geogr. Inf. Sci. 2013, 27, 2343-2361.
64. Thapa, R.B.; Murayama, Y. Urban growth modeling using the Bayesian probability function. In Progress in Geospatial Analysis; Murayama, Y., Ed.; Springer: Tokyo, Japan, 2012; pp. 197-214.
65. Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation; McGraw-Hill Comp.: New York, NY, USA, 1980.
66. Tavana, M.; Kennedy, D.T. N-Site: A distributed consensus building and negotiation support system. Int. J. Inf. Technol. Decis. Mak. 2006, 5, 123-154.
67. Despotis, D.K.; Derpanis, D. A min-max goal programming approach to priority derivation in AHP with interval judgements. Int. J. Inf. Technol. Decis. Mak. 2008, 7, 175-182.
68. Saaty T.L. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 2008, 1, 83-98.
69. Saaty, T.L. Decision making. In Mathematical Methods of Operations Research; Dover Publications: Mineola, NY, USA, 2004; pp. 415-447.
70. Ozturk, D.; Batuk, F. Implementation of GIS-based multi-criteria decision analysis with VB in ArcGIS. Int. J. Inf. Technol. Decis. Mak. 2011, 10, 1023-1042.
71. Ozturk, D.; Batuk, F. Konumsal karar problemlerinde analitik hiyerarsi yönteminin kullanilmasi. Sigma Mühendis. Fen Bilim. Derg. 2010, 28, 124-137.
72. Kent, A.; Williams J.G. The analytic hierarchy process and the personal computer. In Encyclopedia of Computer Science and Technology; CRC Press: Boca Raton, FL, USA, 1988; Volume 19, pp. 22-32.
73. Moghadam, H.S.; Helbich, M. Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model. Appl. Geogr. 2013, 40, 140-149.
74. Sahalu A.G. Analysis of Urban Land Use and Land Cover Changes: A Case Study in Bahir Dar, Ethiopia. Master's Thesis, Institute for Geoinformatics (IFGI), Westfälische Wilhelms-Universität, Münster, Germany, 2014.
75. Malczewski, J. GIS and Multicriteria Decision Analysis; John Wiley & Sons: New York, NY, USA, 1999.
76. Nti, I.K.; Sallis, P. Geospatial workflow process for modelling complex land use cover change. In Proceedings of the 7th Intternational Congress on Environmental Modelling and Software, San Diego, CA, USA, 15-19 June 2014.
77. Nti, I.K. Geospatial Process Modelling for Land Use Cover Change. Ph.D. Thesis, School of Computing and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand, 2013.
78. Zawadzka, J.; Mayr, T.; Bellamy, P.; Corstanje, R. Comparing physiographic maps with different categorisations. Geomorphology 2015, 231, 94-100.
79. Rees, W.G. Comparing the spatial content of thematic maps. Int. J. Remote Sens. 2008, 29, 3833-3844.
80. Kubiak, M.; Dzieszko, P. Using thermal remote sensing in environmental studies. Trans. GIS 2012, 16, 715-732.
81. Gingrich, P. Introductory Statistics for the Social Sciences. Available online: http://uregina.ca/~gingrich/ch11a.pdf (accessed on 18 April 2015).
82. Thapa, R.B.; Murayama, Y. Urban growth modeling of Kathmandu metropolitan region, Nepal. Comput. Environ. Urban Syst. 2011, 35, 25-34.
83. Pontius, R.G., Jr. Quantification error versus location error in comparison of categorical maps. Photogramm. Eng. Remote Sens. 2000, 66, 1011-1016.
84. Cohen J. A coefficient of agreement for nominal scale. Educ. Psychol. Measure. 1960, 20, 37-46.
85. Geri, F.; Amici, V.; Rocchini, D. Spatially-based accuracy assessment of forestation prediction in a complex Mediterranean landscape. Appl. Geogr. 2011, 31, 881-890.
86. Turkish Ministry of Environment and Urban Planning. Samsun Bütünleşik Kiyi Alanlari Yönetim ve Planlama Projesi-Mekansal Strateji Plani; Mekansal Planlama Genel Müdürlüğü: Ankara, Turkey, 2012.
87. Demirel, H.; Sertel, E.; Kaya, S.; Seker, D.Z. Exploring impacts of road transportation on environment: A spatial approach. Desalination 2008, 226, 279-288.
88. Li, X.; Yeh, A.G.-O. Modelling sustainable urban development by the integration of constrained cellular automata and GIS. Int. J. Geogr. Inf. Sci. 2000, 14, 131-152.