Abstract
Handling uncertain spatial data and modelling of spatial data quality and data uncertainty are currently major challenges in GIS. Geodata usage is growing, for example in agricultural and environmental models. If the data are of a low quality, then model results will be poor as well. An important issue to address is the accuracy of GIS applications for model output. Spatial data uncertainty models, therefore, are necessary to quantify the reliability of model results. In this study we use a combination of fuzzy methods within geostatistical modelling for this purpose. The main motivation is to jointly handle uncertain spatial and model information. Fuzzy set theory is used to model imprecise variogram parameters. Kriging predictions and kriging variances are calculated as fuzzy numbers, characterized by their membership functions. Interval width of predictions measures the effect of variogram uncertainty. The methodology is applied on methane (CH4) emissions at the Island of Java. Kriging standard deviations ranged from 12 to 26.45, as compared to ordinary kriging standard deviations, ranging from 12 to 33.11. Hence fuzzy kriging is considered as an interesting method for modeling and displaying the quality of spatial attributes when using deterministic models in a GIS.
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References
Agumya, A. and Hunter, G.J., 1996, Assessing Fitness for Use of Spatial Information: Information Utilisation and Decision Uncertainty. Proceedings of the GIS/LIS ’96 Conference, Denier, Colorado, pp. 349–360
Bardossy, A., Bogardi, I. and Kelly, W.E., 1990a, Kriging with Imprecise (Fuzzy) Variograms I: Theory. Mathematical Geology 22, 63–79
Bardossy, A., Bogardi, I., and Kelly, W.E., 1990b, Kriging with Imprecise (Fuzzy) Variograms II: Application, Mathematical Geology 22, 81–94
Bezdek, J.C., 1981, Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York.
Burrough P.A., 1986. Principles of geographical information systems for land resources assessment. Clarendon press, Oxford.
Burrough, P.A., 1991, The Development of Intelligent Geographical Information Systems. Proceedings of the 2nd European Conference on GIS (EGIS ’91), Brussels, Belgium, vol. 1, pp. 165–174
Burrough, P.A., 2001, GIS and geostatistics: essential partners for spatial analysis. Environmental and ecological statistics 8, 361–378
Chilès, J.P., and Delfiner, P. 1999. Geostatistics: modelling spatial uncertainty. John Wiley & Sons, New York.
Diamond, P., and Kloeden, P. 1989. Characterization of compact subsets of fuzzy sets. Fuzzy Sets and Systems 29, 341–348
Elmes, G.A. and Cai, C., 1992, Data Quality Issues in User Interface Design for a Knowledge-Based Decision Support System. Proceedings of the 5th International Symposium on Spatial Data Handling, Charleston, South Carolina, vol. 1, pp. 303–312
Goodchild, M. and Jeansoulin, R. 1998. Data quality in geographic information. Hermes, Paris.
Guptill S.C. and Morrison, J.L. (1995). Elements of Spatial Data Quality. Elsevier Science Ltd, Exeter, UK.
Heuvelink, G.M.H., 1998. Error Propagation in Environmental modeling with GIS. Taylor Francis, London
Holzapfel-Pschorn, A. and Seiler, W. 1986. Methane emission during a cultivation period from an Italian rice paddy. Journal of Geophysical Research 91, 11804–14
Houghton, J.T., Meira Filho, L.G., Calander, B.A., Harris, N., Kattenberg, A. and Marskell, K. 1996. Climate change 1995. The science of climate change. Cambridge University Press, Cambridge.
Klir G.J. and Folger, T.A., 1988. Fuzzy sets, uncertainty and Information. Prentice Hall, New Jersey.
NCGIA (1989) The research plan of the National Center for Geographic Information and Analysis. International Journal of Geographical Information Systems 3 117–136
Schütz, H., Seiler, W. and Conrad, R., 1990. Influence of soil temperature on methane emission from rice paddy fields. Biogeochemistry 11, 77–95
Stein, A. and Van der Meer, F. 2001. Statistical sensing of the environment, International Journal of Applied Earth Observation and Geoinformation 3, 111–113
Van Bodegom, P.M.; R. Wassmann; and T.M. Metra-Corton, 2001, A processbased model for methane emission predictions from flooded rice paddies, Global biogeochemical cycles 15, 247–264
Van Bodegom, P.M, Verburg, P.H., and Denier van der Gon, H.A.C. 2002. Upscaling methane emissions from rice paddies: problems and possibilities, Global biogeochemical cycles 16, 1–20
Zadeh. L., 1965, Fuzzy Sets. Information Control 8, 338–353
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Stein, A., Verma, M. (2005). Handling Spatial Data Uncertainty Using a Fuzzy Geostatistical Approach for Modelling Methane Emissions at the Island of Java. In: Developments in Spatial Data Handling. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26772-7_14
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DOI: https://doi.org/10.1007/3-540-26772-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22610-9
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