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Purposive Sampling for Digital Soil Mapping for Areas with Limited Data

  • A. Xing Zhu
  • Lin Yang
  • Baolin Li
  • Chengzhi Qin
  • Edward English
  • James E. Burt
  • Chenghu Zhou

Abstract

Digital soil mapping requires two basic pieces of information: spatial information on the environmental conditions which co-vary with the soil conditions and the information on relationship between the set of environment covariates and soil conditions. The former falls into the category of GIS/remote sensing analysis. The latter is often obtained through extensive field sampling. Extensive field sampling is very labor intensive and costly. It is particularly problematic for areas with limited data. This chapter explores a purposive sampling approach to improve the efficiency of field sampling for digital soil mapping. We believe that unique soil conditions (soil types or soil properties) can be associated with unique combination (configuration) of environmental conditions. We used the fuzzy c-means classification to identify these unique combinations and their spatial locations. Field sampling efforts were then allocated to investigate the soil at the typical locations of these combinations for establishing the relationships between soil conditions and environmental conditions. The established relationships were then used to map the spatial distribution of soil conditions. A case study in China using this approach showed that this approach was effective for digital soil mapping with limited data.

Keywords

Soil Mapping Slope Gradient Soil Science Society Topographic Wetness Index America Journal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Refrences

  1. Ahn, C.W., Baumgardner, M.F., Biehl, L.L., 1999. Delineation of soil variability using geostatistics and fuzzy clustering analyses of hyperspectral data. Soil Science Society of America Journal 63, 142–150.Google Scholar
  2. Bell, J.C., Cunningham, R.L., Havens, M.W., 1992. Calibration and validation of a soil-landscape model for predicting soil drainage class. Soil Science Society of America Journal 56,1860–1866.Google Scholar
  3. Bell, J.C., Cunningham, R.L., Havens, M.W., 1994. Soil drainage probability mapping using a soil-landscape model. Soil Science Society of America Journal 58, 464–470.Google Scholar
  4. Bui, E.N., Loughhead, A., Corner, R., 1999. Extracting soil-landscape rules from previous soil surveys. Australian Journal of Soil Research 37, 495–508.CrossRefGoogle Scholar
  5. Bezdek, J.C., 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.Google Scholar
  6. Bezdek, J.C., Ehrlich, R., Full, W., 1984. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences 10, 191–203.CrossRefGoogle Scholar
  7. Burgess, T.M. Webster, R., 1980a. Optimal interpolation and isarithmic mapping of soil properties: I. The semivariogram and punctual kriging. Journal of Soil Science 31, 315–331.CrossRefGoogle Scholar
  8. Burgess, T.M. Webster, R., 1980b. Optimal interpolation and isarithmic mapping of soil properties: II. Block kriging. Journal of Soil Science 31, 333–341.CrossRefGoogle Scholar
  9. Chinese Soil Taxonomy Research Group, 2001. Keys to Chinese Soil Taxonomy (3rd edition). University of Science and Techonology of China Press, Hefei.Google Scholar
  10. English, E.M., 2001. Assisting Knowledge-Based Inferential Soil Mapping: The Application of Fuzzy c-means Clustering to Expose Environmental Niches. M.Sc. Thesis, University of Wisconsin-Madison, Madison, Wisconsin, U.S.A., 80pp.Google Scholar
  11. Evans, I.S., 1972. General geomorphometry, derivatives of altitude, and descriptive statistics. In: Chorley, R.J. (Ed.), Spatial Analysis in Geomorphology, Methuen, London, pp. 17–90.Google Scholar
  12. Evans, I.S., 1998. What do terrain statistics really mean? In: Lane S.N., Richards, K.S.,Chandler, H. (Eds.), Land Monitoring Modelling and Analysis, John Wiley & Sons, Chichester, pp.119–138.Google Scholar
  13. Gessler, P.E., Chadwick, O.A., Chamran, F., Althouse, L., Holmes, K., 2000. Modeling soil-landscape and ecosystems properties using terrain attributes. Soil Science Society of America Journal 64, 2046–2056.Google Scholar
  14. Hudson, B.D., 1992. The soil survey as paradigm-based science. Soil Science Society of America Journal 56, 836–841.Google Scholar
  15. Jenny, H., 1941. Factors of Soil Formation, A System of Quantitative Pedology. McGraw-Hill, New York.Google Scholar
  16. Jenny, H., 1980. The Soil Resource: Origin and Behaviour, Springer-Verlag, New York.Google Scholar
  17. Lark, R.M., Papritz, A., 2003. Fitting a linear model of coregionalization for soil properties using simulated annealing. Geoderma 115, 245–260.CrossRefGoogle Scholar
  18. Li, W., Burt, J.E., Zhu, A.X., Zhang, C., Feyen, J., 2004. Two-dimensional Markov chain for simulating spatial distribution of soil types, Soil Science Society of America Journal 68, 1479–1490.Google Scholar
  19. Li, W., Zhang,C., Burt, J.E., Zhu, A.X., 2005. A Markov chain-based probability vector approach for modeling spatial uncertainties of soil classes. Soil Science Society of America Journal, 69, 1931–1942.CrossRefGoogle Scholar
  20. Lillesand, T.M., Kiefer, R.W., 2000. Remote Sensing and Image Interpretation, John Wiley, New York.Google Scholar
  21. McBratney, A.B., Odeh, I.O.A., Bishop, T.F.A., Dunbar, M.S., Shatar, T.M., 2000. An overview of pedometric techniques for use in soil survey. Geoderma 97, 293–327.CrossRefGoogle Scholar
  22. McBratney, A.B., Mendonca Santos, M.L., Minasny, B., 2003. On digital soil mapping. Geoderma 117, 3–52.CrossRefGoogle Scholar
  23. McBratney, A.B., Webster, R., 1983. Optimal interpolation and isarithmic mapping of soil properties: V. Co-regionalization and multiple sampling strategy. Journal of Soil Science 34, 137–162.CrossRefGoogle Scholar
  24. Slater, B.K., Hammer, R.D., Bell, J.C., Gessler, P.E., Petersen, G.W., 1994. Towards a new framework for modeling the soil-landscape continuum. In: R. Amundson (Ed.), Factors of Soil Formation: A Fiftieth Anniversary Publication, Soil Science Society of America, Madison, Wisconsin, pp. 127–154.Google Scholar
  25. Moran, C.J., Bui, E.N., 2002. Spatial data Moran soil map modeling\textbf International Journal of Geographical Information Science 16, 533–549.CrossRefGoogle Scholar
  26. Odeh, I.O.A., McBratney, A.B., Chittleboruough, D.J., 1995. Further results on prediction of soil properties from terrain attributes: heterotopic cokringing and regression-kriging. Geoderma 67, 215–225.CrossRefGoogle Scholar
  27. Qi, F., Zhu, A.X., 2003. Knowledge discovery from soil maps using inductive learning, International Journal of Geographical information Science 17, 771–795.CrossRefGoogle Scholar
  28. Qi, F., Zhu, A.X., Harrower, M., Burt, J.E., 2006. Fuzzy soil mapping based on prototype category theory. Geoderma 136, 774–787.CrossRefGoogle Scholar
  29. Ross, T.J., 1995. Fuzzy logic with Engineering Applications. McGraw Hill, New York.Google Scholar
  30. Shary, P.A., Sharayab, L.S., Mitusov, A.V., 2002. Fundamental quantitative methods of land surface analysis. Geoderma 107, 1–43.CrossRefGoogle Scholar
  31. Shi, X., Zhu, A.X., Burt, J.E., Qi, F., Simonson, D., 2004. A cased-based reasoning approach to fuzzy soil mapping. Soil Science Society of America Journal 68, 885–894.Google Scholar
  32. Walvoort, D.J.J., de Gruijter, J.J., 2001. Compositional kriging: a spatial interpolation method for compositional data. Mathematical Geology 33, 951–966.CrossRefGoogle Scholar
  33. Ward, A.W., Ward, W.T., McBratney, A.B., de Gruijter, J.J., 1992. MacFUZZY: A program for data analysis by generalized fuzzy k-means on the Macintosh. CSIRO Australia Division of Soils Divisional Report 116, Glen Osmond, South Australia.Google Scholar
  34. Webster, R., 1991. Local disjunctive kriging of soil properties with change of support. Journal of Soil Science 42, 301–318.CrossRefGoogle Scholar
  35. Webster, R., Burgess, T.M., 1980. Optimal interpolation and isarithmic mapping of soil properties: III Changing drift and universal kriging. Journal of Soil Science 31, 505–524.CrossRefGoogle Scholar
  36. Wilson, J.P., Gallant, J.C., (Eds.), 2000. Terrain Analysis: Principles and Applications, John Wiley, New York.Google Scholar
  37. Zadeh, L. 1965. Fuzzy sets. Information and Control 8, 338–353.CrossRefGoogle Scholar
  38. Zevenbergen, L.W., marginpar Thorne, C.R., 1987. Quantitative analysis of land surface topography. Earth Surface Processing and Landforms 12, 47–56.CrossRefGoogle Scholar
  39. Zhu, A.X., 1997. A similarity model for representing soil spatial information. Geoderma 77, 217–242.Google Scholar
  40. Zhu, A.X., 1999. A personal construct-based knowledge acquisition process for natural resource mapping using GIS. International Journal of Geographic Information Science 13, 119–141.CrossRefGoogle Scholar
  41. Zhu, A.X., 2000. Mapping soil landscape as spatial continua: the neural network approach. Water Resources Research 36, 663–677.CrossRefGoogle Scholar
  42. Zhu, A.X., Hudson, B., Burt, J.E., Lubich, K., Simonson, D., 2001. Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Science Society of America Journal 65, 1463–1472.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • A. Xing Zhu
    • 1
    • 2
  • Lin Yang
  • Baolin Li
  • Chengzhi Qin
  • Edward English
  • James E. Burt
  • Chenghu Zhou
  1. 1.State Key Lab of Resources and Environmental Information SystemInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesChina
  2. 2.Department of GeographyUniversity of WisconsinMadisonUSA

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