A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India

Arabian Journal of Geosciences

Volumn 9, Issue 4, 2016, Pages

  Mishra, Varun Narayan ^a   Rai, Praveen Kumar ^b  

^a BANARAS HINDU UNIVERSITY   (India)

^b BANARAS HINDU UNIVERSITY   (India)

Author keywords

Land change modeler; LULC change; Markov chain analysis; Multi layer perceptron; Prediction

Indexed keywords

AGRICULTURAL LAND; ARTIFICIAL NEURAL NETWORK; ENVIRONMENTAL CHANGE; EQUIPMENT; GROWTH RATE; LAND COVER; LAND USE; LANDSAT THEMATIC MAPPER; MARKOV CHAIN; PREDICTION; REMOTE SENSING; URBANIZATION;

BIHAR; INDIA; PATNA;

EID: 84961588328     PISSN: 18667511     EISSN: 18667538     Source Type: Journal    
DOI: 10.1007/s12517-015-2138-3     Document Type: Article

References (73)

1. Ahmed B, Ahmed R (2012) Modeling urban land cover growth dynamics using multi-temporal satellite images: a case study of dhaka, Bangladesh. ISPRS Int J GeoInf 1:3–31
2. Al-shalabi M, Billa L, Pradhan B, Mansor S, Al-Sharif AAA (2012) Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environ Earth Sci: 1–13
3. Al-sharif AA, Pradhan B (2014a) Urban sprawl analysis of Tripoli metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. J Indian Soc Remote Sens 42:149–163
4. Al-Sharif AA, Pradhan B (2014b) Monitoring and predicting land use change in Tripoli metropolitan city using an integrated markov chain and cellular automata models in GIS. Arab J Geosci 7:4291–4301
5. Al-Sharif AA, Pradhan B (2015) A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS. Geocarto Int. doi:10.1080/10106049.2014.997308
6. Araya YH, Cabral P (2010) Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sens 2:1549–1563
7. Arora MK, Das Gupta AS, Gupta RP (2004) An artificial neural network approach for landslide hazard zonation in the Bhagirathi (Ganga) Valley, Himalayas. Int J Remote Sens 25:559–572
8. Arsanjani JJ, KainzW MAJ (2011) Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. Int J Image Data Fusion 2:329–345
9. Arsanjani JJ, Helbich M, Kainz W, Boloorani AD (2013) Integration of logistic regression, markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf 21:265–275
10. Atkinson PM, Tatnall ARL (1997) Introduction neural networks in remote sensing. Int J Remote Sens 18(4):699–709
11. Balzter H (2000) Markov chain models for vegetation dynamics. Ecol Model 126:139–154
12. Basse RM, Omrani H, Charif O, Gerber P, Bodis K (2014) 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 5:160–171
13. Bonan GB (2008) Ecological climatology—concepts and applications (2nd edition). Cambridge University Press, Cambridge
14. Butchart SHM, Walpole M, Collen B, Van Strien A, Scharlemann JPM, Almond REA, Baillie JEM, Bomhard B, Brown C, Bruno J, Carpenter KE, Carr GM, Chanson J, Chenery AM, Csirke J, Davidson NC, Dentener F, Foster M, Galli A, Galloway JN, Genovesi P, Gregory RD, Hockings M, Kapos V, Lamarque JF, Leverington F, Loh J, McGeoch MA, McRae L, Minasyan A, Hernández Morcillo M, Oldfield TEE, Pauly D, Quader S, Revenga C, Sauer JR, Skolnik B, Spear D, Stanwell-Smith D, Stuart SN, Symes A, Tierney M, Tyrrell TD, Vié JC, Watson R (2010) Global biodiversity: indicators of recent declines. Science 328:1164–1168
15. Chan JC-W, Chan K-P, Yeh AG-O (2001) Detecting the nature of change in an urban environment: a comparison of machine learning algorithms. PhotogrammEng Remote Sens 67(2):213–225
16. Civco DL (1993) Artificial neural networks for land-cover classification and mapping. Int J Geogr Inf Syst 7(2):173–186
17. Clarke KC, Hoppen S, Gaydos L (1997) A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environ Plann B Plann Des 24:247–261
18. Cohen J (1960) A coefficient of agreement for nominal scale. EducPsychol Meas 20:37–46
19. Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles and practices. CRC/Lewis Press, Boca Raton
20. Coppedge BR, Engle DM, Fuhlendorf SD (2007) Markov models of land cover dynamics in a southern Great Plains grassland region. Landscape Ecol 22:1383–1393
21. Dadhich PN, Hanaoka S (2010) Markov method integration with multi-layer perceptron classifier for simulation of urban growth of Jaipur city. Selected topics in power systems and remote sensing, 10th WSEAS/IASME international conference on electric power systems, high voltages, electric machines (power‘10) and 6th Wseas international conference on remote sensing (remote ‘10), Iwate Prefectural University, Japan, October 4–6, 2010, pp 118–123
22. Dadhich PN, Hanaoka S (2011) Spatio-temporal urban growth modeling of Jaipur, India. J Urban Technol 18:45–65
23. Dhinwa PS, Pathan SK, Sastry SVC, Rao M, Majumder KL, Chotani ML, Singh JP, Sinha RLP (1992) Land use change analysis of Bharatpur district using GIS. J Indian Soc Remote Sens 20(4):237–250
24. Di Gregorio A, Jansen LJM (2005) Land cover classification system: LCCS: classification concepts and user manual. Food and Agriculture Organization of the United Nations, Rome
25. Eastman JR (2006) IDRISI Andes Tutorial. Clark Labs, Worcester, MA
26. Eastman JR (2009) IDRISI Taiga Guide to GIS and Image Processing; Manual Version 16.02; Clark Labs: Worcester, MA, USA
27. Eastman JR (2012) IDRISI Selva Tutorial. Clark University, Worcester, MA, USA
28. Geneletti D (2012) Assessing the impact of alternative land-use zoning policies on future ecosystem services. Environ Impact Assess Rev 40:25–35
29. Ghosh S, Sen KK, Rana U, Rao KS, Saxena KG (1996) Application of GIS for land use/land-cover change analysis in a mountainous terrain. J Indian Soc Remote Sens 24(3):193–202
30. Gontier M, Mortberg U, Balfors B (2009) Comparing GIS-based habitat models for applications in EIA and SEA. Environ Impact Assess Rev 30(1):8–18
31. Guan D, Li H, Inohae T, Su W, Nagaie T, Hokao K (2011) Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Model 222:3761–3772
32. Hu Z, Lo C (2007) Modeling urban growth in Atlanta using logistic regression. Comput Environ Urban Syst 31:667–688
33. Hua L, Tang L, Cui S, Yin K (2014) Simulating urban growth using the SLEUTH model in a coastal peri-urban district in China. Sustainability 6:3899–3914
34. Jantz CA, Goetz SJ, Shelley MK (2004) Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environ Plann B- Plann Des 31(2):251–271
35. Kamusoko C, Aniya M, Adi B, Manjoro M (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. Appl Geogr 29:435–447
36. Kilic S (2006) Environmental monitoring of land use and land cover changes in a Mediterranean region of turkey. Environ Monit Assess 114(1–3):157–168
37. Koi DD, Murayama Y (2010) Forecasting areas vulnerable to forest conversion in the Tam Dao National Park Region, Vietnam. Remote Sens 2:1249–1272
38. Li X, Yeh AGO (2002) Neural-network-based cellular automata for simulating multiple land use changes using GIS. Int J Geogr Inf Sci 16:323–343
39. Lillesand TM, Kiefer RW (1999) Remote sensing and image interpretation. John Wiley & Sons, New York, USA
40. Maithani S (2015) Neural networks-based simulation of land cover scenarios in Doon valley, India. Geocarto Int 30:163–185
41. Maithani S, Arora MK, Jain RK (2010) An artificial neural network based approach for urban growth zonation in Dehradun city, India. Geocarto Int 25:663–681
42. Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29:617–663
43. Mas J-F, Kolb M, Paegelow M, Olmedo MTC, Houet T (2014) Inductive pattern-based land use/cover change models: a comparison of four software packages. Environ Model Softw 51:94–111
44. Meyer WB, Turner BL (1992) Human population growth and global land-use/cover change. Annu Rev Ecol Syst 23:39–61
45. Mishra VN, Rai PK, Mohan K (2014) Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. J GeogrInst Jovan Cvijic 64:111–127
46. Mitsova D, Shuster W, Wang X (2011) A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc Urban Plan 99:141–153
47. Mitsuda Y, Ito S (2010) A review of spatial-explicit factors determining spatial distribution of land use/land-use change. Landsc Ecol Eng 7(1):117–125
48. Mozumder C, Tripathi NK (2014) 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 32:92–104
49. Muller MR, Middleton J (1994) A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecol 9:151–157
50. Ozturk D (2015) Urban growth simulation of atakum (Samsun, Turkey) using cellular automata-Markov chain and multi-layer perceptron-markov chain models. Remote Sens 7:5918–5950
51. Pijanowski BC, Brown DG, Shellito BA, Manik GA (2002) Using neural networks and GIS to forecast land use changes: a land transformation model. Comput Environ Urban Syst 26:553–575
52. Pijanowski BC, Pithadia S, Shellito BA, Alexandridis K (2005) Calibrating a neural network based urban change model for two metropolitan areas of the Upper Midwest of the United States. Int J Geogr Inf Sci 19:197–215
53. Pontius RG (2000) Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens 66:1011–1016
54. Raju K, Kumar RA (2006) Land use changes in Udumbanchola taluk, Idukki district-Kerala: an analysis with the application of remote sensing data. J Indian Soc Remote Sens 34(2):161–169
55. Ramankutty N, Foley JA, Olejniczak NJ (2002) People on the land: changes in population and global croplands during the 20th century. Ambio 31(3):251–257
56. Roberts DA, Batista GT, Pereira JLG, Waller EK, Nelson BW (1998) Change identification using multitemporal spectral mixture analysis: applications in eastern Amazonia. In Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Edited by R.S. Lunetta and C.D. Elvidge, Ann Arbor Press, Ann Arbor, Mich, pp 137–161
57. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation, In parallel distributed processing: explorations in the microstructure of cognition, Vol. 1, (Edited by D.E. Rumelhart and J.L. McClelland), MIT Press, Cambridge, MA, 318–362.
58. Sadidy J, Firouzabadi PZ, Entezari A (2009). The use of Radarsat and Landsat Image Fusion Algorithms and Different Supervised Classification Methods to Use Map Accuracy-Case Study: Sari Plain-Iran. http://www.isprs.org/proceedings/XXXVI/5-C55/papers/sadidy_javad.pdf
59. Sang L, Zhang C, Yang J, Zhu D, Yun W (2011) Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model 54:938–943
60. Schröter D, Cramer W, Leemans R, Prentice IC, Araujo MB, Arnell NW, Bondeau A, Bugmann H, Carter TR, Gracia CA, De la Vega-Leinert AC, Erhard M, Ewert F, Glendining M, House JI, Kankaanpää S, Klein RJT, Lavorel S, Lindner M, Metzger MJ, Meyer J, Mitchell TD, Reginster I, Rounsevell M, Sabaté S, Sitch S, Smith B, Smith J, Smith P, Sykes MT, Thonicke K, ThuillerW TG, Zaehle S, Zierl B (2010) Ecosystem service supply and vulnerability to global change in Europe. Science 310:1333–1337
61. Sellers PJ, Meeson BW, Hall FG, Asrar G, Murphy RE, Schiffer RA, Bretherton FP, Dickinson RE, Ellingson RG, Field CB, Huemmric KF, Justice CO, Melack JM, Roulet NT, Schime DS, Try PD (1995) Remote sensing of the land surface for studies of global change: models—algorithms—experiments. Remote Sens Environ 51(1):3–26
62. Sheeja RV, Joseph S, Jaya DS, Baiju RS (2011) Land use and land cover changes over a century (1914–2007) in the Neyyar River Basin, Kerala: a remote sensing and GIS approach. Int J Digit Earth 4(3):258–270
63. Triantakonstantis D, Mountrakis G (2012) Urban growth prediction: a review of computational models and human perceptions. J Geogr Inf Syst 4:555–587
64. Turner BL, Skole D, Sanderson S, Fischer G, Fresco L, Leemans R (1995) Land-use and land-cover change—science/research plan, IGBP Report No.35, HDP Report No. 07, Stockholm and Geneva
65. Václavík T, Rogan J (2009) Identifying trends in land use/land cover changes in the context of post-socialist transformation in central Europe: a case study of the greater Olomouc region, Czech Republic. GISci Remote Sens 46(1):54–76
66. Veldkamp A, Fresco LO (1996) CLUE: a conceptual model to study the conversion of land use and its effects. Ecol Model 85:253–270
67. Verburg PH, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura SS (2002) Modeling the spatial dynamics of regional land use: the CLUE-S model. Environ Manag 30:391–405
68. Wakode HB, Baier K, Jha R, Azzam R (2013) Analysis of urban growth using Landsat TM/ETM data and GIS—a case study of Hyderabad. India Arab J Geosci 1–13
69. Wang SQ, Zheng XQ, Zang XB (2012) Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environ Sc 13:1238–1245
70. Yang X, Zheng X-Q, Lv L-N (2012) A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol Model 233:11–19
71. Youssef AM, Pradhan B, Tarabees E (2011) Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process. Arab J Geosci 4:463–473
72. Yuan F (2010) Urban growth monitoring and projection using remote sensing and geographic information systems: a case study in the Twin Cities Metropolitan Area, Minnesota. Geocarto Int 25:213–230
73. Zhang Q, Ban Y, Liu J, Hu Y (2011) Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China. Comput Environ Urban Syst 35:126–139