1 Hydro–Meteorology and Climate change Technology Research Division, Vietnam Institute of Meteorology, Hydrology and Climate change, No. 23, Nguyen Chi Thanh street, Dong Da district, Hanoi 100000, Vietnam

2 Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gullbringvegen 36, N–3800, Bø i Telemark, Norway

3 Department of Health and Environmental Studies (INHM), University College of Southeast Norway, Gullbringvegen 36, N–3800, Bø i Telemark, Norway

*Corresponding author:; Tel.: +84–838734488


Simulation of Land use/Land cover (LULC) change has been conducted extensively in the past with varying techniques and methodologies with Markov Chain incorporating Cellular Automata approach among those. The Markov–Cellular Automata (Markov_CA) model has been applied worldwide, however, model parameter calibration is site–specific. In Viet Nam, research on LULC change a pressing issue given the rapid socio–economic development. Research on LULC change is a necessary starting point for impacts assessment on water resources, land resources, ecosystems, environment, etc. However, what we lack is a method for modeling our insights to simulate LULC fluctuations and to project future LULC. Therefore, this article offers a way to combine known problems to produce a new result. The change of LULC for the period 2005–2015 will be simulated and will result in a prediction of the LULC of 2030. In addition, the calibrated Markov_CA model adapted to the study area will also be a valuable reference for employment in similar areas. Finally, the expected results and the calibrated model are validated by the Kappa coefficient and provide a good level of agreement.


Cite this paper

Bang, N.T.; Phong, D.H. Spatial and Temporal Modeling of Land use/Land cover Change at the Ca River Basin (North Central Viet Nam) Using Markov Chain and Cellular Automata Approach. VN J. Hydrometeorol. 2022, 10, 35-54. 


1. Turner, B.L.; Lambin, E.F.; Reenberg, A. The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 2007, 104(52), 20666–20671.

2. Sciences, Committee on Grand Challenges in Environmental Sciences. Grand challenges in environmental sciences. National Academy Press, 2001.

3. Lambin, E.F. Modeling and monitoring land–cover change processes in tropical regions. Prog. Phys. Geogr. 1997, 21(3), 375–393.

4. Kabat, P. The role of biospheric feedbacks in the hydrological cycle; the IGBP–BAHC special issue. Global Change Newsletter 1999, 39, 1–3.

5. Halmy, M.W.A.; Gessler, P.E.; Hicke, J.A.; Salem, B.B. Land use/land cover change detection and prediction in the north–western coastal desert of Egypt using Markov–CA. Appl. Geogr. 2015, 63, 101–112.

6. Corner, R.J.; Dewan, A.M.; Chakma, S. Monitoring and prediction of land–use and land–cover (LULC) change. Change. In: Dewan A., Corner R. (eds) Dhaka Megacity. Springer Geography. Springer, Dordrecht, 2014, 75–97.

7. Rogan, J.; Chen, D. Remote sensing technology for mapping and monitoring land–cover and land–use change. Prog. Plann. 2004, 61(4), 301–325.

8. Ahmed, B.; Ahmed, R.; Zhu, X. Evaluation of model validation techniques in land cover dynamics. ISPRS Int. J. Geo-Inf. 2013, 2(3), 577–597.

9. Overmars, K.d.; De Koning, G.; Veldkamp, A. Spatial autocorrelation in multi–scale land–use models. Ecol. Modell. 2003, 164(2–3), 257–270.

10. Veldkamp, A.; Lambin, E. F. Predicting land–use change. In: Elsevier, 2001.

11. 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. Gen. Tech. Rep. NE–297. Newton Square, PA: US Department of Agriculture, Forest Service, Northeastern Research Station, 2002, 61, 297.

12. Houet, T.; Hubert–Moy, L. Modeling and projecting land–use and land–cover changes with Cellular Automaton in considering landscape trajectories. EARSeL eProceedings 2006, 5(1), 63–76.

13. D BEHERA, M.; Borate, S.N.; Panda, S.N.; Behera, P.R.; Roy, P.S. Modeling and analyzing the watershed dynamics using Cellular Automata (CA)–Markov model–A geo–information–based approach. J. Earth Syst. Sci. 2012, 121(4), 1011–1024.

14. Guan, D.; Li, H.; Inohae, T.; Su, W.; Nagaie, T.; Hokao, K. Modeling urban land–use change by the integration of cellular automaton and Markov model. Ecol. Modell. 2011, 222(20–22), 3761–3772.

15. Ozturk, D. Urban growth simulation of Atakum (Samsun, Turkey) using cellular automata–Markov chain and multi–layer perceptron–Markov chain models. Remote Sen. 2015, 7(5), 5918–5950.

16. Tung, H.T.; Cat, V.M.; Ranzi, R.; Hoa, T.T. Research on medium-term flood forecasting in Ca River basin. Journal of Water Resources and Environmental Engineering, 2010, 28. (In Vietnamese)

17. Ly, N.T.K. Flood Characteristics of Lam River basin. University of Science - Viet Nam National University, 2017. In Vietnamese.

18. Giang, P.T. Studying flood characteristics for flood warning in the Lower Lam River basin. (Master Thesis), Hanoi University of Science – Viet Nam National University, 2014. In Vietnamese.

19. Jayawardena, A.; Takahasi, Y.; Tachikawa, Y.; Takeuchi, K. Catalogue of Rivers for Southeast Asia and the Pacific – Volume 6: UNESCO–IHP Regional Steering Committee for Southeast Asia and the Pacific, 2012.

20. Barsi, J.A.; Lee, K.; Kvaran, G.; Markham, B.L.; Pedelty, J.A. The spectral response of the Landsat–8 operational land imager. Remote Sen. 2014, 6(10), 10232–10251.


22. Olivera, F.; Valenzuela, M.; Srinivasan, R.; Choi, J.; Cho, H.; Koka, S.; Agrawal, A. ARCGIS&SWAT: A geodata model and GIS interface for SWAT 1. JAWRA J. Am. Water Resour. Assoc. 2006, 42(2), 295–309.

23. Al–sharif, A.A.; Pradhan, B. Monitoring and predicting land–use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian J. Geosci. 2014, 7(10), 4291–4301.

24. Foody, G.M.; Campbell, N.; Trodd, N.; Wood, T. Derivation and applications of probabilistic measures of class membership from the maximum–likelihood classification. Photogramm. Eng. Remote Sen. 1992, 58(9), 1335–1341.

25. Strahler, A.H. The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sen. Environ. 1980, 10(2), 135–163.

26. Manandhar, R.; Odeh, I. O.; Ancev, T. Improving the accuracy of land use and land cover classification of Landsat data using post–classification enhancement. Remote Sen. 2009, 1(3), 330–344.

27. López, G.G.I., Hermanns, H.; Katoen, J.P. Beyond memoryless distributions: Model checking semi–Markov chains. In Process Algebra and Probabilistic Methods. Performance Modelling and Verification. Springer, 2001, 57–70.

28. Araya, Y.H.; Cabral, P. Analysis and modeling of urban land cover change in Setúbal and Sesimbra, Portugal. Remote Sens. 2010, 2(6), 1549–1563.

29. Moreno, N.; Wang, F.; Marceau, D. J. Implementation of a dynamic neighborhood in a land–use vector–based cellular automata model. Comput. Environ. Urban Syst.        2009, 33(1), 44–54.

30. Balzter, H.; Braun, P.W.; Köhler, W. Cellular automata models for vegetation dynamics. Ecol. Modell. 1998, 107(2–3), 113–125.

31. Al–shalabi, M.; Billa, L.; Pradhan, B.; Mansor, S.; Al–Sharif, A.A. Modeling 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. 2013, 70(1), 425–437.

32. Aronoff, S. Classification accuracy: a user approach. Photogramm. Eng. Remote Sens. 1982, 48(8), 1299–1307.

33. Aronoff, S. The minimum accuracy value is an index of classification accuracy. Photogramm. Eng. Remote Sens. 1985, 51(1), 99–111.

34. Kalkhan, M.A.; Reich, R.M.; Czaplewski, R.L. Statistical properties of five indices in assessing the accuracy of remotely sensed data using simple random sampling. Paper presented at the Proceedings ACSM/ASPRS Annual Convention and Exposition, 1995.

35. Koukoulas, S.; Blackburn, G.A. Introducing new indices for accuracy evaluation of classified images representing semi–natural woodland environments. Photogramm. Eng. Remote Sen. 2001, 67(4), 499–510.

36. Foody, G.M. Status of land cover classification accuracy assessment. Remote Sen. Environ. 2002, 80(1), 185–201.

37. Lucas, I.; Janssen, F.; van der Wel, F.J. Accuracy assessment of satellite–derived landcover data: A review. Photogramm. Eng. Remote Sen. 1994, 60(4), 479–426.

38. Story, M.; Congalton, R.G. Accuracy assessment: a user’s perspective. Photogramm. Eng. Remote Sens. 1986, 52(3), 397–399.

39. Smits, P.; Dellepiane, S.; Schowengerdt, R. Quality assessment of image classification algorithms for land–cover mapping: a review and a proposal for a cost–based approach. Int. J. Remote Sen. 1999, 20(8), 1461–1486.

40. Campbell, J.B.; Wynne, R.H. Introduction to remote sensing: Guilford Press. 2011.

41. Lu, Q.; Chang, N.B.; Joyce, J.; Chen, A.S.; Savic, D.A.; Djordjevic, S.; Fu, G. Exploring the potential climate change impact on urban growth in London by a cellular automata–based Markov chain model. Comput. Environ. Urban Syst. 2018, 68, 121–132. doi:10.1016/j.compenvurbsys.2017.11.006.

42. Eastman, J.R. TerrSet manual. TerrSet Version 2015, 18, 1–390.

43. Anderson, J.R. Land use and land cover classification system for use with remote sensor data, US Government Printing Office, 1976, pp. 964.

44. Hadi Memarian, S.K.B.; Talib, J.B.; Sung, C.T.B.; Sood, A.M.; Abbaspour, K. Validation of CA–Markov for Simulation of Land Use and Cover Change in the Langat Basin, Malaysia. J. Geog. Inf. Sys. 2012, 4, 13. doi:10.4236/jgis.2012.46059.

45. Shafizadeh Moghadam, H.; 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. doi:10.1016/j.apgeog.2013.01.009.

46. Yang, Y.; Zhang, S.; Liu, Y.; Xing, X.; Sherbinin, A. Analyzing historical land–use changes using a Historical Land Use Reconstruction Model: a case study in Zhenlai County, northeastern China. Sci. Rep. 2017, 7, 41275. doi:10.1038/srep41275.

47. Zabihi, H.; Ahmad, A.; Vogeler, I.; Said, M.N.; Golmohammadi, M.; Golein, B.; Nilashi, M. Land suitability procedure for sustainable citrus planning using the application of the analytical network process approach and GIS. Comput. Electron. Agric. 2015, 117, 114–126.

48. An, P.; Moon, W.; Rencz, A. Integration of geological, geophysical, and remote sensing data using fuzzy set theory. Can. J. Explor. Geophys.   1991, 27(1), 1–11.

49. Luo, X.; Dimitrakopoulos, R. Data–driven fuzzy analysis in quantitative mineral resource assessment. Comput. Geosci. 2003, 29(1), 3–13.

50. Ranjbar, H.; Honarmand, M. Integration and analysis of airborne geophysical and ETM+ data for exploration of porphyry type deposits in the Central Iranian Volcanic Belt using fuzzy classification. Int. J. Remote Sen. 2004, 25(21), 4729–4741.

51. Saaty, T.L. A scaling method for priorities in hierarchical structures. J. Math. Psychol.      1977, 15(3), 234–281.

52. Saaty, T.L. Analytic hierarchy process. In Encyclopedia of operations research and management science. Springer, 2013, 52–64.

53. Arsanjani, J.J.; Kainz, W.; Mousivand, A.J. Tracking dynamic land–use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. Int. J. Image Data Fusion 2011, 2(4), 329–345.

54. Pontius, R.G. Quantification error versus location error in comparison of categorical maps. Photogramm. Eng. Remote Sen. 2000, 66(8), 1011–1016.

55. Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20(1), 37–46.

56. Pontius Jr, R.G. Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogramm. Eng. Remote Sen. 2002, 68(10), 1041–1050.

57. Kirppendorff, K. Content analysis: An introduction to its methodology. Beverley Hills: Sage. 1989.