Skip to main content
SpringerLink
Log in
SpringerLink
Log in

An object-based cellular automata model to mitigate scale dependency

  • Chapter
Object-Based Image Analysis

Part of the book series: Lecture Notes in Geoinformation and Cartography ((LNGC))

Abstract

Cellular automata (CA) are individual-based spatial models increasingly used to simulate the dynamics of natural and human systems and forecast their evolution. Despite their simplicity, they can exhibit extraordinary rich behavior and are remarkably effective at generating realistic simulations of land-use patterns and other spatial structures. However, recent studies have demonstrated that the standard raster-based CA models are sensitive to spatial scale, more specifically to the cell size and neighborhood configuration used for the simulation. To mitigate cell size dependency, a novel object-based CA model has been developed where space is represented using a vector structure in which the polygons correspond to meaningful geographical entities composing the landscape under study. In addition, the proposed object-based CA model allows the geometric transformation of each polygon, expressed as a change of state in part or in totality of its surface, based on the influence of its respective neighbors. The implementation and testing of this model on real data reveals that it generates spatial configurations of landscape patches that are more realistic than the conventional raster-based CA model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Ahern, F.J. D.N.N. Horler, J. Cihlar, W.J. Bennett, and E. MacAulay. 1983. “Digital Processing to Improve Classification Results at Resolutions of 5 to 50 Meters”. In Proceedings of the SPIE Symposium on Techniques for Extraction from Remotely Sensed Images, Rochester, NY, 16-19 August, pp. 153-170.

    Google Scholar 

  • Allen, T.F.H., and T.B. Starr. 1982. Hierarchy Perspective for Ecological Complexity. University of Chicago Press, Chicago.

    Google Scholar 

  • Almeida, C. M. d., M. Batty, A.M.V. Monteiro, G. Câmara, B.S. Soares-Filho, G.C. Cerqueira, and C.L. Pennachin. 2003. “Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation”, Computers, Environment and Urban Systems, Vol. 27, pp. 481-509.

    Article  Google Scholar 

  • Bach, L. 1981. “The Problem of Aggregation and Distance for Analysis of Accessibility and access Opportunity in Location-Allocation Models”, Environment and Planning A, Vol. 13, pp. 955-978.

    Article  Google Scholar 

  • Band, L. E. and I. D. Moore. 1995. “Scale: landscape attributes and geographical information systems”, Hydrological Processes, Vol. 9, pp. 401-422.

    Article  Google Scholar 

  • Batty, M., Y. Xie, and A. Sun. 1999. “Modeling urban dynamics through GIS-based cellular automata”, Computers, Environment and Urban Systems, Vol. 23, pp. 205-233.

    Article  Google Scholar 

  • Benenson, I., and P.M. Torrens. 2004. “Geosimulation: object-based modeling of urban phenomena”, Computers, Environment and Urban Systems, Vol. 28, pp. 1-8.

    Article  Google Scholar 

  • Benson, B.J., and M.D. McKenzie. 1995. “Effects of Sensor Spatial Resolution on Landscape Structure Parameters”, Landscape Ecology, Vol. 10, No. 2, pp. 113-120

    Article  Google Scholar 

  • Bian, L., and S.J. Walsh. 1993. “Scale Dependencies of Vegetation and Topography in a Mountainous Environment of Montana”, The Professional Geographer, Vol. 45, No. 1, pp. 1-11.

    Article  Google Scholar 

  • Bruneau, P., C. Gascuel-Odoux, P. Robin, Ph. Merot, and K. Beven. 1995. “Sensitivity to Space and Time Resolution of a Hydrological Model Using Digital Elevation Data”, Hydrological Processes, Vol. 9, pp. 69-81.

    Article  Google Scholar 

  • Cao, C., and N.S.-N. Lam. 1997. “Understanding the Scale and Resolution Effects in Remote Sensing and GIS”. In Scale in Remote Sensing in GIS, Quattrochi, D.A. and M.F. Goodchild, eds., pp. 57-72.

    Google Scholar 

  • Chang, K. T. and B. W. Tsai. 1991. “The effect of DEM resolution on slope and aspect mapping”, Cartography and Geographic Information Science, Vol. 18, No. 1, pp. 69-77.

    Article  Google Scholar 

  • Chen, Q., and A.E. Mynett. 2003. “Effects of cell size and configuration in cellular automata based prey-predator modeling”, Simulation Modeling Practice and Theory, Vol. 11, pp. 609-625.

    Article  Google Scholar 

  • Clarke, W.A.V., and K.L. Avery. 1976. “The Effects of Data Aggregation in Statistical Analysis”, Geographical Analysis, Vol. 28, pp. 428-438.

    Google Scholar 

  • Cushnie, J.L. 1987. The Interactive Effect of Spatial Resolution and Degree of Internal Variability within Land-Cover Types on Classification Accuracies”, International Journal of Remote Sensing, Vol. 8, No. 1, pp.15-29.

    Google Scholar 

  • Daubechies, I. 1988. “Orthonormal bases of compactly supported wavelets”, Communications on Pure and Applied Mathematics, Vol. 41, pp. 906-966.

    Google Scholar 

  • Dietzel, C., and K. Clarke, K. 2006. “The effect of disaggregating land use categories in cellular automata during model calibration and forecasting”, Computers, Environment and Urban Systems, Vol. 30, No. 1, pp. 78-101.

    Article  Google Scholar 

  • Donoho, D.L., and X. Huo. 2000. “Beamlets and multiscale image analysis”, In Lecture Notes in computational Science and Engineering: Multiscale and Multiresolution Methods, Springer, pp. 149-196.

    Google Scholar 

  • Durieux, L., J. Kropacek, G.D. De Grandi and F. Achard. 2006. “Object-oriented wavelet multi-resolution image analysis of the Siberia GBFM radar mosaic combined with MERIS imagery for continental scale land cover mapping”. First International Conference on Object-Based Image Analysis (OBIA 2006), Salzburg University, Austria, July 4-5. ESRI. (2005). "ArcGIS 9. Desktop GIS. <http://www.esri.com/software/arcgis/about/desktop.html>."

    Google Scholar 

  • Favier, C., J. Chave, A. Fabing, D. Schwartz, and M.A. Dubois. 2004. “Modelling forest-savanna mosaic dynamics in man-influenced environments: effects of fire, climate and soil heterogeneity”, Ecological Modelling, Vol. 171, pp. 85-102.

    Article  Google Scholar 

  • Flache, A., and R. Hegselmann. 2001. “Do irregular grids make a difference? Relaxing the spatial regularity assumption in cellular models of social dynamics”, Journal of Artificial Societies and Social Simulation, Vol. 4, No. 4, http://jasss.soc.surrey.ac.uk/4/4/6.html.

    Google Scholar 

  • Fotheringham, A.S. 1989. “Scale-Independent Spatial Analysis”, In Accuracy of Spatial Databases, Goodchild, M., S. Gopal, Eds, Taylor and Francis, pp. 221-228.

    Google Scholar 

  • Fotheringham, A.S., and D.W.S. Wong. 1991. “TheModifiable Areal Unit Problem in Multivariate Statistical Analysis”, Environment and Planning A, Vol. 23, pp.1025-1044.

    Google Scholar 

  • Friedl, M.A. 1997. “Examining the Effects of Sensor Resolution and Sub-pixel Heterogeneity on Spectral Vegetation Index: Implications for Biophysical Modeling”, In Scale in Remote Sensing in GIS, Quattrochi, D.A. and M.F. Goodchild, eds., pp.113-139.

    Google Scholar 

  • Friedl, M.A. 1997. “Examining the Effects of Sensor Resolution and Sub-Pixel Heterogeneity on Spectral Vegetation Indices: Implication for Biophysical Modeling”. In Scale in Remote Sensing in GIS, Quattrochi, D.A. and M.F. Goodchild, eds., pp. 113-139.

    Google Scholar 

  • Frihida, A., D.J. Marceau, and M. Thériault. 2002. “Spatio-temporal object-oriented data model for disaggregate travel behaviour”, Transactions in GIS, Vol. 6, No. 3, pp. 277-294.

    Article  Google Scholar 

  • Gao, J. 1997. “Resolution and accuracy of terrain representation by grid DEMs at a micro-scale”, International Journal of Geographical Information Science, Vol. 11, No. 2, pp. 199-212.

    Article  Google Scholar 

  • Gehlke, C.E., and K. Biehl. 1934. “Certain Effects of Grouping Upon the size of the Correlation Coefficient in Census Tract Material”, Journal of American Statistical Association Supplement, Vol. 29, pp. 169-170.

    Article  Google Scholar 

  • Grimm, V. 1999. “Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future”, Ecological Modelling, Vol. 115, pp. 129-148.

    Article  Google Scholar 

  • Hall, O. and G. Hay. 2003. “A multiscale object-specific approach to digital change detection”, International Journal of Applied Earth Observations and Geoinformation, Vol. 4, pp. 311-327.

    Article  Google Scholar 

  • Hay, G. and G. Castilla. 2006. “Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT)”. First International Conference on Object-Based Image Analysis (OBIA 2006), Salzburg University, Austria, July 4-5.

    Google Scholar 

  • Hay, G.J. and D.J. Marceau. 2004. “Multiscale object-specific analysis (MOSA): An integrative approach for multiscale analysis”. In: Remote Sensing Image Analysis: Including the Spatial Domain, S. de Jong and F. van der Meer, eds, Kluwer Academic Publishers, pp. 71-92.

    Google Scholar 

  • Hay, G., G. Castilla, M. Wulder and J. R. Ruiz. 2005. “An automated object-based approach for the multiscale image segmentation of forest scenes”, International Journal of Applied Earth Observations and Geoinformation, Vol. 7, pp. 339-359.

    Article  Google Scholar 

  • Hay, G.J., P. Dubé, A. Bouchard, and D.J. Marceau. 2002. “A Scale-Space Primer for Exploring and quantifying Complex Landscape”, Ecological Modelling, Vol. 153, Nos. 1/1, pp. 27-49.

    Article  Google Scholar 

  • Hu, S., and D. Li. 2004. “Vector Cellular Automata Based Geographical Entity”, 12th International Conference on Geoinformatics - Geoespatial Information Research: Bridging the Pacific and Atlantic., University of Gävle, Sweden.

    Google Scholar 

  • Hunt, L., and B. Boots. 1996. “MAUP Effects in the Principal Axis Factoring Technique”, Geographical Systems, Vol. 3, pp. 101-121.

    Google Scholar 

  • Irons, J.R., B.L. Markham, R.F. Nelson, D.L. Toll, D.L. Williams, R.S. Latty, and M.L. Stauffer. 1985. “The Effects of Spatial Resolution on the Classification of Thematic Mapper Data”, International Journal of Remote Sensing, Vol. 6, No. 8, pp. 1385-1403.

    Article  Google Scholar 

  • Jantz, C. A., and S.J. Goetz. 2005. “Analysis of scale dependencies in an urban land-use-change model”, International Journal of Geographical Information Science, Vol. 19, No. 2, pp. 217-241.

    Article  Google Scholar 

  • Jarvis, P.G. 1995. “Scaling Processes and Problems”, Plant, Cell and Environment, Vol. 18, pp. 1079-1089.

    Article  Google Scholar 

  • Jenerette, G. D., and J. Wu. 2001. “Analysis and simulation of land-use change in the central Arizona-Phoenix region, USA”, Landscape Ecology, Vol. 16, pp. 611-626.

    Article  Google Scholar 

  • Kershaw, K.A. 1957. “The Use of Cover and Frequency in the Detection of Pattern in Plant Communities”, Ecology, Vol. 38, pp. 291-299.

    Article  Google Scholar 

  • Kim, M. and M. Madden. 2006. “Determination of optimal scale parameter for alliance-level forest classification of multispectral IKONOS images”. First International Conference on Object-Based Image Analysis (OBIA 2006). Salzburg University, Austria, July 4-5.

    Google Scholar 

  • Klinger, A. 1971. “Pattern and search statistics”. In Optimizing Methods in Statistics, J.S. Rustagi, eds., Academic Press, pp. 303-339.

    Google Scholar 

  • Klinger, A. and C.R. Dyer. 1976. “Experiments in picture representation using regular decomposition”, Computer Graphics Image Processing, Vol. 5, pp. 65-105.

    Google Scholar 

  • Kocabas, V., and S. Dragicevic. 2006. “Assessing cellular automata model behavior using a sensitivity analysis approach”, Computers, Environment and Urban Systems, Vol. 30, No. 6, pp. 921-953.

    Article  Google Scholar 

  • Kok, K. and A. Veldkamp. 2001. “Evaluating impact of spatial scales on land use pattern analysis in Central America”, Agriculture, Ecosystems and Environment, Vol. 85, pp. 205-221.

    Article  Google Scholar 

  • Latty, R.S. and R.M. Hoffer. 1981. “Computer-Based Classification Accuracy Due to the Spatial Resolution Using Per-Point versus Per-Field Classification Techniques”. Symposium of Machine Processing of Remotely Sensed Data, pp. 384-392.

    Google Scholar 

  • Lau, K. H., and B.H. Kam. 2005. “A cellular automata model for urban land-use simulation”, Environment and Planning B: Planning and Design, Vol. 32, pp. 247-263.

    Article  Google Scholar 

  • Li, H. and F. Reynolds. 1997. “Modeling effects of spatial pattern, drought, and grazing on rates of rangeland degradation: a combined Markov and cellular automaton approach”. Scale in Remote Sensing and GIS. D. Quattrochi and M. F. Goodchild, eds, CRC Press, pp. 211-230.

    Google Scholar 

  • Li, X., and A. G.-o. Yeh. 2000. “Modelling sustainable urban development by the integration of constrained cellular automata and GIS”, International Journal of Geographical Information Science, Vol. 14, pp. 131-152.

    Article  Google Scholar 

  • Li, X., and A. G.-o. Yeh. 2002. “Neural-network-based cellular automata for simulating multiple land use changes using GIS”, International Journal of Geographical Information Science, Vol. 16, No. 4, pp. 323-343.

    Article  Google Scholar 

  • Lindeberg, T. 1994. “Scale-space theory: A basic tool for analyzing structures at different scales”, Journal of Applied Statistics, Vol. 21, No. 2, pp. 225-270.

    Article  Google Scholar 

  • Makin, J., R. G. Healey, and S. Dowers. 1997. “Simulation modelling with object-oriented GIS: a prototype application to the time geography of shopping behaviour”, Geographical Systems, Vol. 4, No. 4, pp. 397-429.

    Google Scholar 

  • Mandelbrot, B.B. 1967. “The Fractal geometry of nature”, Science, Vol. 156, pp. 636-642.

    Article  Google Scholar 

  • Marceau, D.J., 2007. “What can be learned from multi-agent systems?”, In: Monitoring, Simulation and Management of Visitor Landscapes, R. Gimblett, ed., University of Arizona Press. (submitted).

    Google Scholar 

  • Marceau, D.J. 1992. The Problem of Scale and Spatial Aggregation in Remote Sensing: An Empirical Investigation Using Forestry Data. Unpublished Ph.D. Thesis, Department of Geography, University of Waterloo, 180 p.

    Google Scholar 

  • Marceau, D.J., 1999. “The Scale Issue in the Social and Natural Sciences”. Canadian Journal of Remote Sensing, Vol. 25, No. 4, pp. 347-356.

    Google Scholar 

  • Marceau, D.J., and G.J. Hay, 1999. “Remote sensing contributions to the scale issue”, Canadian Journal of Remote Sensing, Vol. 25, No. 4, pp. 357-366.

    Google Scholar 

  • Marceau, D.J., D.J. Gratton, r. Fournier, and J.P. Fortin. 1994. “Remote Sensing and the Measurement of Geographical Entities in a Forested Environment; Part 2: The Optimal Spatial Resolution”, Remote Sensing and Environment, Vol. 49, No. 2, pp. 105-117.

    Article  Google Scholar 

  • Marceau, D.J., P.J. Howarth, D.J. Gratton. 1994. “Remote Sensing and the Measurement of Geographical Entities in a Forested Environment; Part 1: The Scale and Spatial Aggregation Problem”, Remote Sensing and Environment, Vol. 49, No. 2, pp. 93-104.

    Article  Google Scholar 

  • Markham, B.L., and J.R.G. Townshend. 1981. “Land Cover Classification Accuracy as a Function of Sensor Spatial Resolution”. Proceedings of the Fifteenth International Symposium on Remote Sensing of Environment, Ann Arbor, Michigan, pp. 1075-1090.

    Google Scholar 

  • McCarthy, H.H., J.C. Hook, and D.S. Knos. 1956. The Measurement of Association in Industrial Geography. Department of Geography, State University of Iowa, Iowa City.

    Google Scholar 

  • McGarigal, K., B. Marks, E. Ene, and C. Holmes. 1995. “Fragstats”. University of Massachusetts. http://www.umass.edu/landeco/research/fragstats/fragstats.html.

    Google Scholar 

  • McNulty, S.G., J.M. Vose, and W.T. Swank. 1997. “Scaling Predicted Pine Forest Hydrology and Productivity Across the Southern United States”. In Scale in Remote Sensing in GIS, Quattrochi, D.A. and M.F. Goodchild, eds., pp. 187-209.

    Google Scholar 

  • Meetemeyer, V., and E.O. Box. 1987. “Scale Effects in Landscape Studies”. In Landscape Heterogeneity and Disturbance, M.G. Turner, ed., Springer-Verlag, pp. 15-34.

    Google Scholar 

  • Meetemeyer. V. 1989. “Geographical Perspectives of Space, Time, and Scale”, Landscape Ecology, Vol. 3, Nos. 3/4, pp. 163-173.

    Google Scholar 

  • Ménard, A., and D.J. Marceau. 2005. “Exploration of spatial scale sensitivity in geographic cellular automata”, Environment and Planning B: Planning and Design, Vol. 32, pp. 693-714.

    Article  Google Scholar 

  • Ménard, A., and D.J. Marceau. 2007. “Simulating the impact of forest management scenarios in an agricultural landscape of southern Quebec, Canada, using a geographic cellular automata”, Landscape and Urban Planning, Vol. 79, Nos. 3/4, pp. 253-265.

    Article  Google Scholar 

  • Moody, A., and C.E. Woodcock. 1995. “The Influence of Scale and the Spatial Characteristics of Landscapes on Land-Cover Mapping Using Remote Sensing”, Landscape Ecology, Vol. 10, No. 6, pp. 363-379.

    Article  Google Scholar 

  • Moreno, N. and D.J. Marceau, 2006. “A vector-based cellular automata model to allow changes of polygon shape”, In Proceedings of The 2006 SCS International Conference on Modeling and Simulation - Methodology, Tools, Software Applications, R. Huntsinger, H. Vakilzadian and T. Ören, eds, July 31 - August 2, 2006, Calgary, Canada, pp. 85-92.

    Google Scholar 

  • Moreno, N. and D.J. Marceau, 2007. “Modeling land-use changes using a novel vector-based cellular automata model”, In Proceedings of the 18th IASTED International Conference on Modelling and Simulation, May 30 - June 1, 2007, Montreal, Quebec.

    Google Scholar 

  • O’Neill, R.V., D.L. De Angelis, J.B. Waide, and T.F.H. Allen. 1986. A Hierarchical Concept of Ecosystems. Princeton University Press, Princeton, New Jersey.

    Google Scholar 

  • Openshaw, S. 1977. “A Geographical Solution to Scale and Aggregation Problems in Region-Building, Partitioning and Spatial Modelling”, Institute of British Geographers, Transactions, New Series, Vol. 2, pp. 459-472.

    Article  Google Scholar 

  • Openshaw, S. 1978. “An Empirical Study of Some Zone-Design Criteria”, Environment and Planning A, Vol. 10, pp.781-794

    Article  Google Scholar 

  • Openshaw, S. 1984. The Modifiable Areal Unit Problem. Concepts and Techniques in Modern Geography (CATMOG), No. 38.

    Google Scholar 

  • Parrott, L. and R. Kok. 2000. “Incorporating complexity in ecosystem modeling”, Complexity International, Vol. 7, pp. 1-19.

    Google Scholar 

  • Platt, T., and K.L. Denman. 1975. “Spectral analysis in ecology”, Annals of Rev. Ecological System, Vol. 6, pp. 189-210.

    Article  Google Scholar 

  • Putman, S.H., and S.–H. Chung. 1989. “Effects of Spatial System Design on Spatial Interaction Models, 1: The Spatial System Definition Problem”, Environment and Planning A, Vol. 21, pp. 27-46.

    Article  Google Scholar 

  • Qi, Y., and J. Wu. 1996. “Effects of Changing Spatial Resolution on the Results of Landscape Pattern Analysis Using Spatial Autocorrelation Indices”, Landscape Ecology, Vol. 11, pp. 39-49.

    Article  Google Scholar 

  • Rietkerk, M., S.C. Dekker, P.C.D. Ruiter, and J.V.D Koppel. 2004. “Self-Organized Patchiness and Catastrophic Shifts in Ecosystems”, Review, Vol. 305, pp. 1926-1929.

    Google Scholar 

  • Sadowski, F.G., W.A. Malila, J.E. Sarno, and R.F. Nalepka. 1977. “The Influence of Multispectral Scanner Spatial Resolution on Forest Feature Classification”. Proceedings of the 11th International Symposium on Remote Sensing of Environment, 25-29 April, Ann Arbor, pp. 1279-1288.

    Google Scholar 

  • Shi, W., B. Yang, and Q. Li. 2003. “An object-oriented data model for complex objects in three-dimensional geographical information systems”, International Journal of Geographical Information Science, Vol. 17, No. 5, pp. 411-430.

    Article  Google Scholar 

  • Shi, W., and M.Y.C. Pang. 2000. “Development of Voronoi-based cellular automata - an integrated dynamic model for Geographical Information Systems”, International Journal of Geographical Information Science, Vol. 14, No. 5, pp. 455-474.

    Article  Google Scholar 

  • Soucy-Gonthier, N., Marceau, D., Delage, M., Cogliastro, A., Domon, G., and Bouchard, A. (2003). "Détection de l’évolution des superficies forestières en Montérégie entre juin 1999 et août 2002 à partir d’images satellitaires Landsat TM." Agence forestière de la Montérégie.

    Google Scholar 

  • Stevens, D., S. Dragicevic, S., and K. Rothley. 2007. “iCity: A GIS-CA modelling tool for urban planning and decision making”, Environmental Modelling & Software, Vol. 22, pp. 761-773.

    Article  Google Scholar 

  • Straatman, B., R. White, and G. Engelen. 2004. “Towards an automatic calibration procedure for constrained cellular automata”, Computers, Environment and Urban Systems, Vol. 28, pp. 149-170.

    Article  Google Scholar 

  • Torrens, P. and D. O’Sullivan. 2001. “Cellular automata and urban simulation: where do we go from here?”, Environment and Planning B, Vol. 28, pp. 163-168.

    Article  Google Scholar 

  • Turner, D.P., R. Dodson, and D. Marks. 1996. “Comparison of Alternative Spatial Resolutions in the Application of a Spatially Distributed Biogeochemical Model over Complex Terrain”, Ecological Modeling, Vol. 90. pp. 53-67.

    Article  Google Scholar 

  • Turner, M.G., R.V. O’Neil, R.H. Gardner, and B.T. Milne. 1989. “Effects of Changing spatial Scale on the analysis of Landscape Pattern”, Landscape Ecology, Vol. 3, pp. 153-162.

    Article  Google Scholar 

  • Usher, M.B. 1969. “The Relation Between Mean Square and Block Size in the Analysis of Similar Patterns”, Journal of Ecology, Vol. 57, pp. 505-514.

    Article  Google Scholar 

  • Visvalingam, M. 1991. “Areal Units and the Linking of Data: Some Conceptual Issues”, In Spatial Analysis and Spatial Policy Using Geographic Information Systems, L. Worrall, ed., Belhaven Press, pp. 12-37.

    Google Scholar 

  • Wachowicz, M. 1999. Object-oriented design for temporal GIS, Taylor and Francis.

    Google Scholar 

  • Walsh, S.J. A. Moody, T.R. Allen, and D.G. Brown. 1997. “Scale Dependence of NDVI and its Relationship to Mountains Terrain”. In Scale in Remote Sensing in GIS, Quattrochi, D.A. and M.F. Goodchild, eds., pp. 27-55.

    Google Scholar 

  • White, R., and G. Engelen. 2000. “High-resolution integrated modelling of the spatial dynamics of urban and regional systems”, Computers, Environment and Urban Systems, Vol. 24, pp. 383-400.

    Article  Google Scholar 

  • White, R., G. Engelen, and I. Uljee. 2000. “Modelling land use change with linked cellular automata and socio-economic models: a tool for exploring the impact of climate change on the island of St Lucia”. In Spatial Information for Land Use Management, M.J. Hill, and R.J. Aspinall, eds., pp. 189-204.

    Google Scholar 

  • Wiens, J.A. 1989. “Spatial Scaling in Ecology”, Functional Ecology, Vol. 3, pp. 385-397.

    Article  Google Scholar 

  • Wolfram, S. 1984. “Cellular automata as models of complexity”, Nature, Vol. 311, pp. 419-424.

    Article  Google Scholar 

  • Wu, 2004

    Google Scholar 

  • Wu, F. 2002. “Calibration of stochastic cellular automata: the application to rural-urban land conversions”, International Journal of Geographical Information Science, Vol. 16, No. 8, pp. 795-818.

    Article  Google Scholar 

  • Wu. J., and O.L. Loucks. 1995. “From Balance of Nature to Hierarchical Patch Dynamics: A Paradigm Shift in Ecology”, The Quarterly Review of Biology, Vol. 70, pp. 439-466.

    Article  Google Scholar 

  • Yule, G.U., and M.G. Kendall. 1950. An Introduction to the Theory of Statistics. Griffin.

    Google Scholar 

  • Zhang, X., N. A. Drake, and J. Wainwright. 2004. “Scaling issues in environmental modeling”. Environmental Modeling: Finding simplicity in complexity. J. Wainwright and M. Mulligan, eds., Wiley, pp. 319-334.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Geocomputing Laboratory, Department of Geomatics Engineering, University of Calgary, Canada

    D. J. Marceau & N. Moreno

  2. Centro de Simulación y Modelos, Facultad de Ingeniería, Universidad de Los Andes, Venezuela

    N. Moreno

Authors
  1. D. J. Marceau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Moreno
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universität Salzburg Zentrum für Geoinformatik, Hellbrunner Str. 34, 5020 Salzburg, Austria

    Thomas Blaschke (Prof.) (Prof.)

  2. Universität Salzburg Zentrum für Geoinformatik, 5020 Salzburg, Austria

    Stefan Lang (Dr.) (Dr.)

  3. Foothills Facility for Remote Sensing & GIScience, University of Calgary, 2500 University Dr. NW, Earth Sciences Bldg., Calgary T2N 1N4, Canada

    Geoffrey J. Hay (Dr.) (Dr.)

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Marceau, D.J., Moreno, N. (2008). An object-based cellular automata model to mitigate scale dependency. In: Blaschke, T., Lang, S., Hay, G.J. (eds) Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77058-9_3

Download citation

Publish with us

Policies and ethics

Search

Navigation