Skip to main content

Advertisement

SpringerLink
Log in

Modeling land suitability/capability using fuzzy evaluation

  • Published:
GeoJournal Aims and scope Submit manuscript

Abstract

Modeling the suitability of land to support specific land uses is an important and common GIS application. Three classic models, specifically pass/fail screening, graduated screening and weighted linear combination, are examined within a more general framework defined by fuzzy logic theory. The rationale underlying each model is explained using the concepts of fuzzy intersections, fuzzy unions and fuzzy averaging operations. These fuzzy implementations of the three classic models are then operationalized and used to analyze the distribution of kudzu in the conterminous United States. The fuzzy models achieve better predictive accuracies than their classic counterparts. By incorporating fuzzy suitability membership of environment factors in the modeling process, these fuzzy models also produce more informative fuzzy suitability maps. Through a defuzzification process, these fuzzy maps can be converted into conventional maps with clearly defined boundaries, suitable for use by individuals uncomfortable with fuzzy results.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 239–258.

    Article  Google Scholar 

  • Boroushaki, S., & Malczewski, J. (2008). Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers & Geosciences, 34(4), 399–410.

    Article  Google Scholar 

  • Burrough, P. A., & McDonnell, R. A. (1998). Fuzzy sets and fuzzy geographical objects. In Principles of geographical information systems (pp. 265–292). Oxford, New York: Oxford University Press.

  • Eastman, J. R. (1999). Multi-criteria evaluation and GIS. In P. A. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind (Eds.), Geographical information systems (pp. 493–502). New York: Wiley.

    Google Scholar 

  • Eastman, J. R., Jin, W., Kyem, P. A. K., & Toledano, J. (1995). Raster procedures for multi-criteria/multi-objective decisions. Photogrammetric Engineering and Remote Sensing, 61(5), 539–547.

    Google Scholar 

  • Eastman, J. R., Kyem, P. A. K., Toledano, J., & Jin, W. (1993). GIS and decision making. Geneva: UNITAR.

    Google Scholar 

  • FAO. (1976). A framework for land evaluation. FAO Soils Bulletin No. 32. Rome: UN Food and Agriculture Organization.

  • FAO. (1982). Fourth meeting of the eastern African sub-committee for soil correlation and land evaluation. Arusha, Tanzania: UN Food and Agriculture Organization.

    Google Scholar 

  • Fritz, S., & See, L. (2005). Comparison of land cover maps using fuzzy agreement. International Journal of Geographical Information Science, 19(7), 787–807.

    Article  Google Scholar 

  • Hall, G. B., Wang, F., & Subaryono, J. (1992). Comparison of Boolean and fuzzy classification methods in land suitability analysis by using geographical information systems. Environment and Planning A, 24(4), 497–516.

    Article  Google Scholar 

  • Jiang, H., & Eastman, J. R. (2000). Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173–184.

    Article  Google Scholar 

  • Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: Theory and applications. Upper Saddle River, NJ: Prentice Hall PTR.

    Google Scholar 

  • Lukasiewicz, J. (1970). In defence of logistic. In L. Borkowski (Ed.), Selected works. London: North-Holland.

    Google Scholar 

  • Malczewski, J. (2000). On the use of weighted linear combination method in GIS: Common and best practice approaches. Transactions in GIS, 4(1), 5–22.

    Article  Google Scholar 

  • Malczewski, J. (2002). Fuzzy screening for land suitability analysis. Geographical and Environmental Modelling, 6(1), 27–39.

    Article  Google Scholar 

  • Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3–65.

    Article  Google Scholar 

  • Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726.

    Article  Google Scholar 

  • Malczewski, J., & Rinner, C. (2005). Exploring multicriteria decision strategies in GIS with linguistic quantifiers: A case study of residential quality evaluation. Journal of Geographical Systems, 7(2), 249–268.

    Article  Google Scholar 

  • McHarg, I. (1969). Design with nature. New York: Doubleday.

    Google Scholar 

  • Openshaw, S., & Openshaw, C. (1997). Fuzzy logic, fuzzy systems and soft computing. In Artificial intelligence in geography (pp. 268–308). Chichester, New York: Wiley.

  • Saaty, T. J. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281.

    Google Scholar 

  • Stoms, D. M., McDonald, J. M., & Davis, F. W. (2002). Fuzzy assessment of land suitability for scientific research reserves. Environmental Management, 29(4), 545–558.

    Article  Google Scholar 

  • Tomlin, D. (1990). Geographic information systems and cartographic modeling. New York: Prentice Hall.

    Google Scholar 

  • Wang, F., Hall, G. B., & Subaryono, J. (1990). Fuzzy information representation and processing in conventional GIS software: Database design and application. International Journal of Geographical Information Science, 4(3), 261–283.

    Article  Google Scholar 

  • Winberry, J. J. (1996). Kudzu: The vine that almost ate the south. In G. G. Bennett (Ed.), Snapshots of the Carolinas (pp. 99–102). Washington, DC: Association of American Geographers.

    Google Scholar 

  • Winberry, J. J., & Jones, D. M. (1973). Rise and decline of the “miracle vine” kudzu in the southern landscape. Southeastern Geographer, 13(3), 61–70.

    Article  Google Scholar 

  • Woodcock, C. E., & Gopal, S. (2000). Fuzzy set theory and thematic maps: Accuracy assessment and area estimation. International Journal of Geographical Information Science, 14(2), 153–172.

    Article  Google Scholar 

  • Yager, R. R. (1988). Ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Geospatial Information Sciences, University of Texas at Dallas, 800 W. Campbell Rd. MS. GR 32, Richardson, TX, 75080, USA

    Fang Qiu, Bryan Chastain, Yuhong Zhou, Caiyun Zhang & Harini Sridharan

Authors
  1. Fang Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bryan Chastain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuhong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Caiyun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Harini Sridharan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fang Qiu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Qiu, F., Chastain, B., Zhou, Y. et al. Modeling land suitability/capability using fuzzy evaluation. GeoJournal 79, 167–182 (2014). https://doi.org/10.1007/s10708-013-9503-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10708-013-9503-0

Keywords

Search

Navigation