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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 265))

Abstract

Ill-known environmental phenomena are often modeled by means of multisource spatial data fusion. Generally, these fusion strategies have to cope with distinct kinds of uncertainty, related to the ill-defined knowledge of the phenomenon, the lack of classified data, the distinct trust of the information sources, the imprecision of the observed variables. In this chapter we discuss the advantage of modeling multisource spatial data fusion in the environmental field based on the OWA operator, and overview two applications. The first application is aimed at defining an environmental indicator of anomaly at continental scale based on a fusion of partial hints of evidence of anomaly. The second application computes seismic hazard maps based on a consensual fusion strategy defined by an extended OWA operator that accounts for data imprecision, and reliability of the data sources. In particular, the proposed fusion function models a consensual dynamics and is parameterized so as to consider a varying spatial neighborhood of the data to fuse.

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References

  1. Adeh, L.A.: On the validity of Dempster’s rule of combination of evidence, Memo M79/24, Univ. of California, Berkeley (1979)

    Google Scholar 

  2. Bloch, I., Maître, H.: Information combination operators for data fusion: A comparative review with classification. IEEE Transactions on Systems, Man, and Cybernetics, part A: systems and humans 26(1), 52–67 (1996)

    Article  Google Scholar 

  3. Bone, C., Dragicevic, S., Roberts, A.: Integrating high resolution remote sensing, GIS and fuzzy set theory for identifying susceptibility areas of forest insect infestations. International Journal of Remote Sensing 26(21), 4809–4828 (2005)

    Article  Google Scholar 

  4. Bordogna, G., Pagani, M., Pasi, G.: A Flexible Decision support approach to model ill-defined knowledge in GIS. Presented at the NATO Workshop on Environmental Impact Assement, Kiev (June 2006)

    Google Scholar 

  5. Breiman, L., Friedman, J., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth, Belmont (1984)

    MATH  Google Scholar 

  6. Brivio, P.A., Boschetti, M., Carrara, P., Stroppiana, D., Bordogna, G.: Fuzzy integration of satellite data for detecting environmental anomalies across Africa. In: Hill, J., Roeder, A. (eds.) Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment. Taylor & Francis, London (2006)

    Google Scholar 

  7. Burrough, P.A., McDonnel, R.A.: Principles of Geographical Information Systems. Oxford University Press, Oxford (1998)

    Google Scholar 

  8. Carrara, P., Bordogna, G., Stroppiana, D., Brivio, P.A., Nelson, A., Boschetti, M.: A flexible multi-source spatial data fusion system for environmental status assessment at continental scale. Journal of Geographic Information Science 22(7) (2008)

    Google Scholar 

  9. Chanussot, J., Mauris, G., Lambert, P.: Fuzzy fusion techniques for linear features detection in multitemporal sar images. IEEE Trans. on Geoscience and Remote Sensing 37(3), 1292–1305 (1999)

    Article  Google Scholar 

  10. Chen, H., Meer, P.: Robust Fusion of Uncertain Information. IEEE Transactions on Systems, Man and Cybernetics, Part B 35(3), 578–586 (2005)

    Article  Google Scholar 

  11. DeCETI Project, Multi-sources information fusion for satellite image classification, electronic Report of the DeCETI Project, Leonardo da Vinci Programme, Strand II, Measure II.1.1.C, Contract No: GR/1996/II/0953/PI/II.1.1.c/FPC (2000), http://www.survey.ntua.gr/main/labs/rsens/DeCETI/IRIT/MSI-FUSION/ (accessed 20/12/2006)

  12. Dempster, P.: A generalization of the Bayesian inference. Journal of Royal Statistical Society 30, 205–447 (1968)

    MathSciNet  Google Scholar 

  13. Bordogna, G., Pagani, M., Pasi, G.: Consensual Fusion of Uncertain Multisource Spatial data. In: Proc. of the FUZZIEEE 2007, London, July 24-27 (2007)

    Google Scholar 

  14. Gogo, L., Torra, V.: On Aggregation Operators for Ordinal Qualitative Information. IEEE Trans. on Fuzzy Systems 8(2), 143–153 (2000)

    Article  Google Scholar 

  15. di Lavoro, G.: Redazione della Mappa di pericolosità sismica prevista dall’ordinanza PCM 3274 del 20 March 2003. Rapporto conclusivo per il Dipartimento di Protezione Civile, INGV, Milano-Roma, appendixes (April 2004), http://zonesismiche.mi.ingv.it/

  16. Herrera, F., Herrera-Viedma, E.: Linguistic decision analysis: Steps for solving decision problems under linguistic information. Fuzzy Sets Syst. 115(1), 67–82 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  17. Jeon, B., Landgrebe, D.A.: Decision fusion approach for multitemporal classification. IEEE Transactions on Geoscience and Remote Sensing 37(3), 1227–1233 (1999)

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Kacprzyk, J.: Group decision making with a fuzzy linguistic majority. Fuzzy Sets and Systems 18, 105–118 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  20. Kam, M.: geometric interpretation for decision fusion with memory. IEEE Trans. on Systems, Man and Cybernetics, Part A 29(1), 52–62 (1999)

    Article  Google Scholar 

  21. Keenan, P.B.: Spatial Decision Support Systems: An coming of age. Control and Cybernetics 35, 9–27 (2006)

    Google Scholar 

  22. Lenz, R., Malkina-Pykh, I.G., Pykh, Y.: Introduction and overview. Ecological Modelling 130, 1–11 (2000)

    Article  Google Scholar 

  23. Pagani, M., Pagani, M., Bordogna, G., Marcellini, A.: About the use of the OWA operator in case of a PSHA based on a logic tree, xxx

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Metternicht, G.: Assessing temporal and spatial changes of salinity using fuzzy logic, remote sensing and GIS. Foundations of an expert system. Ecol. Model. 144, 163–179 (2001)

    Google Scholar 

  27. Morris, A., Jankowski, P.: Fuzzy techniques for multiple criteria decision making in GIS. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, CA, July 25-28, pp. 2446–2451 (2001) (CD ROM proceedings)

    Google Scholar 

  28. Rabinowitz, N., Steinberg, D.M., Leonard, G.: Logic Trees, sensitivity analysis and data reduction in probabilistic seismic hazard assessment. Earthquake spectra 14(1), 189–201 (1998)

    Article  Google Scholar 

  29. Robinson, P.B.: A perspective on the fundamentals of fuzzy sets and their use in Geographic Information Systems. Transactions in GIS 7(1), 3–30 (2003)

    Article  Google Scholar 

  30. Ruger, N., Schluter, M., Matthies, M.: A fuzzy habitat suitability index for Populus euphratica in the Northern Amudarya delta (Uzbekistan). Ecol. Model. 184, 313–328 (2005)

    Article  Google Scholar 

  31. Shafer, G.: A mathematical theory of evidence. Princeton University Press, London (1976)

    MATH  Google Scholar 

  32. Silvert, W.: Ecological impact classification with fuzzy sets. Ecological Modelling 96, 1–10 (1997)

    Article  Google Scholar 

  33. Solaiman, B.: Multisensor data fusion using fuzzy concepts: application to land-cover classification using ERS-1/JERS-1 SAR composites. IEEE Trans. on Geoscience and Remote Sensing 37(3), 1316–1326 (1999)

    Article  Google Scholar 

  34. Stroppiana, D., Brivio, P.A., Boschetti, M., Carrara, P., Bordogna, G.: A fuzzy anomaly indicator for environmental monitoring at continental Scale. Ecological Indicators 9, 92–106 (2009)

    Article  Google Scholar 

  35. Tran, L.T., Knight, C.G., O’Neill, R.V., Smith, E.R., Riitters, K.H., Wickham, J.: Environmental assessment, fuzzy decision analysis of integrated environmental vulnerability assessment of the Mid-Atlantic region. Environmental Monitoring 29(6), 845–859 (2002)

    Google Scholar 

  36. Valet, L., Mauris, G., Bolon, P.: A statistical overview of recent literature in information fusion. IEEE AESS Systems Magazine 1, 7–14 (2001)

    Article  Google Scholar 

  37. Wald, L.: Some terms of reference in data fusion. IEEE Trans. on Geoscience and Remote Sensing 37(3), 1190–1193 (1999)

    Article  Google Scholar 

  38. Wan, W., Fraser, D.: Multisource data fusion with multiple self-organizing maps. IEEE Trans. on Geoscience and Remote Sensing 37(3), 1344–1349 (1999)

    Article  Google Scholar 

  39. Yager, R.R.: Interpreting Linguistically Quantified Propositions. International Journal of Intelligent Systems 9, 541–569 (1994)

    Article  MATH  Google Scholar 

  40. Yager, R.R.: On ordered weighted averaging aggregation operators in multi-criteria decision making. IEEE Trans. on Systems, Man and Cybernetics 18, 183–190 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  41. Yager, R.R.: Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems 11, 49–73 (1996)

    Article  Google Scholar 

  42. Yager, R.R.: A framework for multi-source data fusion. Information Sciences 163, 175–200 (2004)

    Article  MathSciNet  Google Scholar 

  43. Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Computers and Mathematics with Applications 9, 149–184 (1983)

    Article  MATH  MathSciNet  Google Scholar 

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Bordogna, G., Boschetti, M., Brivio, A., Carrara, P., Pagani, M., Stroppiana, D. (2011). Fusion Strategies Based on the OWA Operator in Environmental Applications. In: Yager, R.R., Kacprzyk, J., Beliakov, G. (eds) Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice. Studies in Fuzziness and Soft Computing, vol 265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17910-5_10

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  • DOI: https://doi.org/10.1007/978-3-642-17910-5_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17909-9

  • Online ISBN: 978-3-642-17910-5

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