A markov chain-based probability vector approach for modeling spatial uncertainties of soil classes

Soil Science Society of America Journal

Volumn 69, Issue 6, 2005, Pages 1931-1942

  Li, Weidong ^a   Zhang, Chuanrong ^b   Burt, James E ^a   Zhu, A Xing ^a,c  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b UNIVERSITY OF WISCONSIN WHITEWATER   (United States)

^c INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; DECISION MAKING; MARKOV PROCESSES; PROBABILITY; RISK ASSESSMENT; SOILS; VECTORS;

PROBABILITY VECTORS; SOIL CLASSES; SOIL DISTRIBUTIONS; SURVEY MAPS;

SOIL MECHANICS;

MARKOV CHAIN; SOIL SURVEY; SPATIAL ANALYSIS; UNCERTAINTY ANALYSIS;

EID: 27644492480     PISSN: 03615995     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (33)

1. Abend, K., T.J. Harley, and L.N. Kanal. 1965. Classification of binary random patterns. IEEE Trans. Inf. Theory 11:538-544.
2. Besag, J. 1986. On the statistical analysis of dirty pictures (with discussions). J. Royal Statist. Soc. Ser. B 48:259-302.
3. Bierkens, M.F.P., and P.A. Burrough. 1993a. The indicator approach to categorical soil data: I. Theory. J. Soil Sci. 44:361-368.
4. Bierkens, M.F.P., and P.A. Burrough. 1993b. The indicator approach to categorical soil data: II. Application to mapping and land use suitability analysis. J. Soil Sci. 44:369-381.
5. Caers, J., and T. Zhang. 2004. Multiple-point geostatistics: A quantitative vehicle for integrating geologic analogs into multiple reservoir models. p. 383-394. In G.M. Grammer et al (ed.) Integration of outcrop and modern analogs in reservoir modeling. AAPG Memoirs, AAPG, Tulsa, OK.
6. Carle, S.F., and G.E. Fogg. 1996. Transition probability-based indicator geostatistics. Math. Geol. 28:453-477.
7. Chiles, J.-P., and P. Delfiner. 1999. Geostatistics - Modeling spatial uncertainty. John Wiley & Sons, New York.
8. Dubrule, O., and E. Damsleth. 2001. Achievements and challenges in petroleum geostatistics. Petroleum Geosci. 7:S1-S7.
9. Elfeki, A.M., and F.M. Dekking. 2001. A Markov chain model for subsurface characterization: Theory and applications. Math. Geol. 33:569-589.
10. Goodchild, M.F., G. Sun, and S. Yang. 1992. Development and test of an error model for categorical data. Inter. J. Geog. Inf. Syst. 6:87-104.
11. Goovaerts, P. 1996. Stochastic simulation of categorical variables using a classification algorithm and simulated annealing. Math. Geol. 28:909-921.
12. Goovaerts, P. 1997. Geostatistics for Natural Resources Evaluation. Oxford Univ. Press, New York.
13. Goovaerts, P. 2002. Geostatistical modeling of spatial uncertainty using p-field simulation with conditional probability fields. Int. J. Geog. Inf. Sci. 16:167-178.
14. Gray, A.J., I.W. Kay, and D.M. Titterington. 1994. An empirical study of the simulation of various models used for images. IEEE Trans. Pattern Analysis and Machine Intelligence 16:507-513.
15. Guardiano, F., and M. Srivastava. 1993. Multivariate geostatistics: Beyond bivariate moments. Vol. 1, p. 133-144. In A. Soares (ed.) Geostatistics Troia'92. Kluwer, Dordrecht, the Netherlands.
16. Journel, A. 1997. Foreword. p. vii-viii. In P. Goovaerts Geostatistics for natural resources evaluation. Oxford Univ. Press, New York.
17. Koltermann, E.C., and S.M. Gorelick. 1996. Heterogeneity in sedimentary deposits: A review of structure-imitating, process-imitating, and descriptive approaches. Water Resour. Res. 32:2617-2658.
18. Kyriakidis, P.C., and J.L. Dungan. 2001. A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model prediction. Environ. Ecol. Stat. 8:311-330.
19. Li, W., B. Li, Y. Shi, and D. Tang. 1997. Application of the Markov chain theory to describe spatial distribution of textural layers. Soil Sci. 162:672-683.
20. Li, W., B. Li, and Y. Shi. 1999. Markov-chain simulation of soil textural profiles. Geoderma 92:37-53.
21. Li, W., B. Li, Y. Shi, D. Jacques, and J. Feyen. 2001. Effect of spatial variation of textural layers on regional field water balance. Water Resour. Res. 37:1209-1219.
22. Li, W., C. Zhang, J.E. Burt, A.-X. Zhu, and J. Feyen. 2004. Two-dimensional Markov chain simulation of soil type spatial distribution. Soil Sci. Soc. Am. J. 68:1479-1490.
23. Liu, Y., and A. Journel. 2004. Improving sequential simulation with a structured path guided by information content. Math. Geol. 36:945-964.
24. Luo, J. 1996. Transition probability approach to statistical analysis of spatial qualitative variables in geology. p. 281-299. In A. Foster and D.F. Marriam (ed.) Geologic modeling and mapping. Plenum Press, New York.
25. Mark, D.M., and F. Csillag. 1989. The nature of boundaries on the 'area-class' maps. Cartographica 26:65-78.
26. Norberg, T., L. Rosen, A. Baran, and S. Baran. 2002. On modeling discrete geological structure as Markov random fields. Math. Geol. 34:63-77.
27. Ortiz, J.M., and C.V. Deutsch. 2004. Indicator simulation accounting for multiple-point statistics. Math. Geol. 36:545-565.
28. Qian, W., and D.M. Titterington. 1991. Multidimensional Markov Chain Models for image textures. J. Royal Stat. Soc. Ser. B 53:661-674.
29. USDA. 1962. Soil Survey - Iowa County Wisconsin. USDA, SCS. U.S. Gov. Print. Office, Washington, DC.
30. Wu, K., N. Nunan, J.W. Crawford, I.M. Young, and K. Ritz. 2004. An efficient Markov chain model for the simulation of heterogeneous soil structure. Soil Sci. Soc. Am. J. 68:346-351.
31. Zhang, J., and M. Goodchild. 2002. Uncertainty in geographic information. Taylor & Francis, New York.
32. Zhang, C., and W. Li. 2004. Predictive area class mapping of multinomial land-cover categories using Markov chains. p. 239-242. In Proceedings of the Third International Conference on Geographic Information Science. University of Maryland University College, Adelphi, MD.
33. Zhang, C., and W. Li. 2005. Markov chain modeling of multinomial land-cover classes. GIScience Remote Sens. 42:1-18.