Environmental Management

, Volume 36, Issue 4, pp 576–591 | Cite as

Modeling Change-Pattern-Value Dynamics on Land Use: An Integrated GIS and Artificial Neural Networks Approach

  • Erfu Dai
  • Shaohong Wu
  • Wenzhong Shi
  • Chui-kwan Cheung
  • Ahmed Shaker


The use of spatial methods to detect and characterize changes in land use has been attracting increasing attention from researchers. The objectives of this article were to formulate the dynamics of land use on the temporal and spatial dimensions from the perspectives of the Change-Pattern-Value (CPV) and driving mechanism, based on multitemporal remote sensing data and socioeconomic data. The Artificial Neural Networks were used to identify the factors driving changes in land use. The Pearl River Delta Region of southeast China, which was experiencing rapid economic growth and widespread land conversion, has been selected as the study region. The results show that from 1985 to 2000 in the study region (1) the most prominent characteristics of change in land use were the expansion of the urban land at the expense of farmland, forests, and grasslands, (2) the land-use pattern was being optimized during this period, (3) in an analysis of value, built-up land can yield a return of more than 30 times that of farmland, water area, and forests lands, and (4) rapid economic development, growth in population, and the development of an infrastructure were major driving factors behind ecological land loss and the nonecological land expansion.


Change in land use Change-Pattern-Value (CPV) analysis Driving factors for land-use change Remote sensing GIS Artificial Neural Networks Pearl River Delta Region 

Literature Cited

  1. Adams, R. M., R. A. Fleming, C. C. Change, B. A. Mccarl, and C. Rosenzweig. 1995. A reassessment of the economic effects of global climate change on U.S. agriculture. Climate Change 30(2): 147–167CrossRefGoogle Scholar
  2. Almeida C. M., M. Batty, A. M. V. Monteiro, G. Cămara, B. S. Soares-Filho, G. C. Cerqueira, C. L. Pennachin. 2003. Stochastic cellular automata modelling of urban land use dynamics: Empirical development and estimation. Computers, Environment and Urban Systems 27:481–509Google Scholar
  3. Batty M., Longley. 1994. Urban modeling in computer-graphic and geographic information system environments. Environment and Planning B 19:663–688. Google Scholar
  4. Bockstael N., R. Costanza, I. Strand, W. Boyton, K. Bell, L. Wagner. 1995. Ecological economics modeling and evaluation of ecosystems. Ecological Economics 14:143–159CrossRefGoogle Scholar
  5. Christopher D. E., Y. Ding. 1995. Relative radiometric normalization of Landsat multispectral scanner (MSS) data using an automatic scattergram—controlled regression. Photgrammetric Engineering & Remote Sensing 61:1015–1026Google Scholar
  6. Dale V., R. O’Nell, M. Pedlowski, F. Southworth. 1993. Causes and effects of land use change in Central Rondonia, Brazil. Photogrammetric Engineering & Remote Sensing 59:997–1005Google Scholar
  7. Dai, E. F. 2002. Study on sustainable land use: systematic analysis, assessment and management approaches. Ph.D. thesis. Peking University. Beijing, ChinaGoogle Scholar
  8. de Koning G. H. J., P. H. Verburg, P. Veldkamp, L. O. Fresco. 1999. Multi-scale modeling of land use change dynamics in Ecuador. Agricultural Systems 61:77–93CrossRefGoogle Scholar
  9. Ehrlich P. R., A. H. Ehrlich. 1990. The population explosion. Simon and Schuster, New YorkGoogle Scholar
  10. FAO (Food and Agriculture Organization). 1993. FESLM: An international framework for evaluation sustainable land management. World Soil Resource Report 73. FAO, RomeGoogle Scholar
  11. Fischer G., K. Frohberg, M. A. Keyzer, K. S. Parikh, 1988: Linked national models: A tool for international policy analysis, Kluwer Academic, Dordrecht, NetherlandsGoogle Scholar
  12. Grainger A. 1990. Modelling deforestation in the humid tropics. In: M. Palo. G. Mery (eds). Deforestation or development in the Third World? Vol III. Division of Social Economics of Forestry, Metsantutkimslaitoksen Tiedonantoja, Helsinki. pp 51–67Google Scholar
  13. Griffiths G. H., P. M. Mather. 2000. Remote sensing and landscape ecology. International Journal of Remote Sensing 21:2537–2539. CrossRefGoogle Scholar
  14. Houghton R. A. 1994. The world-wide extent of land-use change. Bioscience 44:305–313. Google Scholar
  15. Lek S., M. Delacoste, P. Baran, I. Dimopoulos, J. Lauga, S. Aulanier. 1996. Application of neural networks to modelling non-linear relationships in ecology. Ecological Modelling 90:39–52CrossRefGoogle Scholar
  16. Lek S., J. F. Guégan. 1999. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling 120:65–73CrossRefGoogle Scholar
  17. Li X., Yeh A. G. O. 2002. Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science 4:323–343CrossRefGoogle Scholar
  18. Lucas I F J., J. M. Frans, V. D. Wel. 1994. Accuracy assessment of satellite derived land-cover data: A review. Photogrammetric Engineering &Remote Sensing 60:410–432.Google Scholar
  19. Mas J. F., H. Puig, J. L. Palacio, A. Sosa-Lόpez. 2004. Modelling deforestration using GIS and artificial neural networks. Environmental Modelling & Software 19:461–471Google Scholar
  20. Medley K., B. W. Okey, G. W. Barrett, M. F. Lucas, W. H. Renwick. 1995. Landscape change with agricultural intensification in a rural watershed, southwestern Ohio. USA Landscape Ecology, 10:161–176CrossRefGoogle Scholar
  21. Meyer W. B., B. L. Turner II. 1994. Change in land use and land cover: A global perspective. Cambridge University Press, LondonGoogle Scholar
  22. Parton W. J., D. S. Schimel, C.V. Cole 1987. Analysis of factors controlling soil organic matter levels in Great Plains grasslands in semi-arid regions. Kluwer Academic, DordrechtGoogle Scholar
  23. Pijanowski B. C., S. H. Gage, D. T. Long, W. C. Cooper 2000. A land transformation model: integrating policy, socioeconomics and environmental drivers using geographic information system. In: J. S. Aanderson, L. D. Harris (eds). Landscape ecology: A top-down approach. Lewis Publishers, New York. pp 183–198Google Scholar
  24. Pijanowski B. C., D. G. Brown, B. A. Shellito, G. A. Manik. 2002. Using neural networks and GIS to forecast land use changes: A Land Transformation Model. Computers, Environment and Urban Systems 26:553–575Google Scholar
  25. Riebsame W. E., W. J. Parton, K. A. Galvin, I. C. Burke, L. Bohern, R. Yong, E. Knop. 1994. Integrated modeling of land use and cover change. Bioscience 44:350–356Google Scholar
  26. Rosenblatt F. 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65:386–408PubMedGoogle Scholar
  27. Saraf A. K., P. R. Choudhary, B. S. Sarma, P. Ghosh. 2001. Impacts of reservoirs in groundwater and vegetation: a study based on remote sensing and GIS techniques. International Journal of Remote Sensing 22:2439–2448CrossRefGoogle Scholar
  28. Schneider L., C. Pontius.2001. Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture Ecosystem and Environment 85:83–94. CrossRefGoogle Scholar
  29. Skapura D. 1996. Building neural networks. ACM Press, New York. Google Scholar
  30. Skole D. L., W. H. Chomentowski, W. A. Salas, A. D. Nobre. 1994. Physical and human dimensions of deforestation in Arnazonia. Bioscience 44:239–288Google Scholar
  31. Slater J., R. Brown. 2000. Change landscapes: monitoring environmentally sensitive areas using satellite image. International Journal of Remote Sensing 21:2753–2767CrossRefGoogle Scholar
  32. Soares-Filho B. S., C. L. Pennachin, G. Cerqueira. 2002. DINAMICA—A stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecological Modelling 154:217–235CrossRefGoogle Scholar
  33. Statistica. 2002. STATISTICA 5.5 Electronic Manual [3/19/2001]. Available from http://www.statsoft.com/downloads/maintenance/ download5.html
  34. Theobald D. M., J. M. Miller, N. T. Hobbs. 1997. Estimating the cumulative effects of development on wildlife habitat. Landscape and Urban Planning 39:25–36CrossRefGoogle Scholar
  35. 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 2:65–82Google Scholar
  36. Turner B. L. II. 1990. Two types of global environmental changes: Definitional and spatial scale issues in their human dimensions. Global Environmental Change 1:14–22CrossRefGoogle Scholar
  37. Turner B. L. II, W. B. Meyer, D. Skole. 1994. Global land-use land-cover change: towards an integrated study. Ambio 23:91–95Google Scholar
  38. Turner, B. L. II, R. H. Moss, and D. Skole. eds. 1993. Relating land use and global land-cover change: a proposal for an IGBP-HDP core project. Report from the IGBP-HDP working group on Land-use/Land-cover Change. Joint Publication of the International Geosphere-Biosphere Programme (report no. 24) and the Human Dimensions of Global Environmental Change Programme (report no. 5). Royal Swedish Academy of Sciences, StockholmGoogle Scholar
  39. Turner, B. L. II., D. Skole, S. Sanderson, G. Fischer, L. O. Fresco, and R. Leemans. 1995. Land-use and Land-cover change Science/Research Plan. IGBP Report No. 35/HDP Report No. 7. IGBP of the ICSU and HDP of ISSC, Stockhlom and GenevaGoogle Scholar
  40. Veldkamp A., L. O. Fresco. 1996. CLUE-CR: an integrated multi-scale model to simulate land use change scenarios in Costa Rica. Ecological Modelling 91:231–248CrossRefGoogle Scholar
  41. Veldkamp A., L. O. Fresco. 1997. Reconstructing land use drivers and their spatial scale dependence for Costa Rica (1973 and 1984). Agricultural Systems 55:19–43CrossRefGoogle Scholar
  42. Vesterby M., R. Heimlich. 1991. Land use and demographic change: results from fast-growing countries. Land Economics 67:279–291PubMedGoogle Scholar
  43. Yeh, A. G. O., and X. Li. 1997. An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China. International Planning Studies 2:193–210Google Scholar
  44. Yeh, A. G. O., and X. Li. 1998. Sustainable land development model for rapid growth areas using GIS. International Journal of Geographical Information Science 12:169–189CrossRefGoogle Scholar
  45. Zhuang D. F., J. Y. Liu. 1997. Study on the model of regional differentiation of land use degree in China. Journal of Natural Resource 12:106–111 (in Chinese)Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • Erfu Dai
    • 1
  • Shaohong Wu
    • 1
  • Wenzhong Shi
    • 2
  • Chui-kwan Cheung
    • 2
  • Ahmed Shaker
    • 2
  1. 1.Institute of Geographical Sciences and Natural Resource Research (IGSNRR)Chinese Academy of SciencesChaoyang DistrictChina
  2. 2.Advanced Research Centre for Spatial Information TechnologyDepartment of Land Surveying and Geo-InfomaticsThe Hong Kong Polytechnic UniversityKowloonHong Kong

Personalised recommendations