Land-Use Change Scenario



Land-use change models are tools to support the analysis of the causes and consequences of land-use dynamics. Scenario analysis with land-use models can support land-use planning and policy. In this study, Markov chain model, which describes land-use and land-cover change from one period to another and uses this as the basis to predict future changes, is applied to project land-use changes in the future for Lhasa area located at the central Tibetan Plateau over a 20-year period from 2000 to 2020 based on the land-use change dynamics and transition probability matrix from 1990 to 2000, and comparison analysis between areas from land-use planning and Markov model projection is made. Results indicated that Markov chain model is found to be useful tool for describing and predicting land-use change process in the study area, and the general trends of future land-use change in the study area are effectively captured, which shows that cultivated land, grassland, water body, and unused land-use types would decrease, whereas forest, horticultural, and built-up land would continue to increase. Studying land-use changes in the past few years and predicting these changes in the future years to come may play a significant role in planning and optimal use of natural resources and harnessing the non-normative changes in the future.


Land use change Future scenario Markov model Lhasa area Central Tibetan Plateau 


  1. Baker, W.L. 1989. A review of models of landscape change. Landscape Ecology 2: 111–133.CrossRefGoogle Scholar
  2. Brown, D.G., B.C. Pijanowski, and J.D. Duh. 2000. Modelling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management 59: 247–263.CrossRefGoogle Scholar
  3. Clarke, K.C., and L.J. Gaydos. 1998. Loose-coupling a cellular automaton model and GIS: Long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science 12: 699–714.CrossRefGoogle Scholar
  4. Clarke, K.C., S. Hoppen, and L. Gaydos. 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design 24: 247–261.CrossRefGoogle Scholar
  5. Costanza, R., and M. Ruth. 1998. Using dynamic modeling to scope environmental problems and build consensus. Environmental Management 22: 183–195.CrossRefGoogle Scholar
  6. Findell, K.L., A. Berg, P. Gentine, et al. 2017. The impact of anthropogenic land use and land cover change on regional climate extremes. Nature Communications 8 (1): 1–10.CrossRefGoogle Scholar
  7. Geohegan, J., L.A. Wainger, and N.E. Bockstael. 1997. Spatial landscape indices in a hedonic framework: An ecological economics analysis using GIS. Ecological Economics 23: 251–264.CrossRefGoogle Scholar
  8. Iacono, M., D. Levinson, A. El-Geneidy, et al. 2015. A Markov chain model of land use change in the Twin Cities, 1958–2005. Journal of Land Use, Mobility and Environment 8 (3): 263–276.Google Scholar
  9. Kumar, S., N. Radhakrishnan, and S. Mathew. 2014. Land use change modelling using a Markov model and remote sensing. Geomatics, Natural Hazards and Risk 5 (2): 145–156.CrossRefGoogle Scholar
  10. Lambin, E.F. 1997. Modelling and monitoring land-cover change processes in tropical regions. Progress in Physical Geography 21: 375–393.CrossRefGoogle Scholar
  11. Lambin, E.F., X. Baulies, N. Bockstael, et al. 2000. Land-Use and Land-Cover Change (LUCC), Implementation Strategy, IGBP Report 48, IHDP Report 10, IGBP, IHDP. Bonn: Stockholm.Google Scholar
  12. Landis, J.D. 1994. The California urban future model: A new-generation of metropolitan simulation-models. Environment and Planning B 21: 399–420.CrossRefGoogle Scholar
  13. Levinson, D., and W. Chen. 2005. Paving new ground: A Markov chain model of the change in transportation networks and land use. In Access to Destinations, ed. D. Levinson and K. Krizek, 243–266. Amsterdam/Boston: Elsevier Science.CrossRefGoogle Scholar
  14. McMillen, D., and J. McDonald. 1991. A Markov chain model of zoning change. Journal of Urban Economics 30: 257–270.CrossRefGoogle Scholar
  15. Muller, R.M., and J. Middleton. 1994. A Markov model of land-use change dynamics in the Niagara region, Ontario, Canada. Landscape Ecology 9: 151–157.Google Scholar
  16. Rhoades, R. 1997. Pathways Towards a Sustainable Mountain Agriculture for the 21st Century: The Hindu Kush-Himalayan Experience. Kathmandu: ICIMOD.Google Scholar
  17. Stewart, W.J. 1994. Introduction to the Numerical Solution of Markov Chains. Princeton: Princeton University Press.Google Scholar
  18. Stokey, E., and R. Zeckhauser. 1978. A Primer for Policy Analysis. New York/London: W.W. Norton.Google Scholar
  19. TAR government. 1998. Master Plan for Land Us in Tibet Autonomous Region from 1997 to 2010 (unpublished report). pp. 10–47.Google Scholar
  20. Theobald, D.M., and N.T. Hobbs. 1998. Forecasting rural land-use change: A comparison of regression and spatial transition-based models. Geographical and Environmental Modelling 2: 65–82.Google Scholar
  21. Turner, M. 1987. Spatial simulation of landscape changes in Georgia: A comparison of three transition models. Landscape Ecology 1 (1): 29–36.CrossRefGoogle Scholar
  22. Turner, B.L. 1994. Local faces, global flows: The role of land use and land cover in global environmental change. Land Degradation and Rehabilitation 5: 71–78.CrossRefGoogle Scholar
  23. Turner, B.L., D. Skole, S. Sanderson, et al. 1995. Land Use and Land Cover Change (LUCC): Science/Research Plan, IGBP Report No. 35, HDP Report No. 7. Geneva: Stovkholm.Google Scholar
  24. Verburg, P.H., and A. Veldkamp. 2005. Introduction to the special issue on spatial modeling to explore land use dynamics. International Journal of Geographical Information Science 19 (2): 99–102.CrossRefGoogle Scholar
  25. Verburg, P.H., P.P. Schot, M.J. Dijst, et al. 2004. Land use change modelling: Current practice and research priorities. GeoJournal 61 (4): 309–324.CrossRefGoogle Scholar
  26. Vitousek, P.M. 1994. Beyond global warming: Ecology and global change. Ecology 75 (7): 1861–1876.CrossRefGoogle Scholar
  27. Vitousek, P.M., H.A. Mooney, J. Lubchenco, et al. 1997. Human domination of earth’s ecosystems. Science 277: 494–499.CrossRefGoogle Scholar
  28. Weng, Q. 2002. Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 64: 273–284.CrossRefGoogle Scholar
  29. Zhang, Z., and D. Chu. 1998. Integrated Environmental Assessment in the Central Tibet Using Remote Sensing and GIS. Beijing: Yuhang Press.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Duo Chu
    • 1
  1. 1.Tibet Institute of Plateau Atmospheric and Environmental SciencesTibet Meteorological BureauLhasaChina

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