Advertisement

Locating boundaries between mapping units

  • James B. Campbell
Article

Abstract

A linear discriminant function can be used to place the boundary between two mapping units, if multiple measurements are available for samples collected from both units. The function is calculated using samples of known group membership, then is applied to classify samples of unknown group identity. The plot of the discriminant index on a map of the study area locates the contact between the two groups. Boundary position can be evaluated by comparing data models based upon the boundary with the distributions of the original variables. This method is applied to locate the boundary between two soil mapping units. Samples of two contrasting soils were collected on a gridded sampling pattern positioned at the contact between two mapping units. Samples were analyzed for physical and chemical properties, including sand content, silt content, and pH. The boundary was located, then separately evaluated with respect to the sand, silt, and pH measurements. The discriminant score boundary corresponded closely to the sand content measurements, but did not match the silt or pH distributions.

Key words

classification discriminant analysis soil science autocorrelation boundary 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abmeyer, W., and Campbell, H. V., 1970, Soil Survey of Shawnee County, Kansas: Soil Conservation Service, U.S. Department of Agriculture, Washington, D.C., 77 p.Google Scholar
  2. Box, G. E. P., and Jenkins, G. M., 1970, Time series analysis: forecasting and control: Holden-Day, San Francisco, 553 p.Google Scholar
  3. Campbell, J. B., 1976, Geographic analysis of variation across a soil boundary and within soil mapping units. Ph.D. Dissertation, Univ. of Kansas: Univ. Microfilms, Ann Arbor, Mich., 267 p. (Diss. Abstr. v. 37, p. 3840-B).Google Scholar
  4. Cliff, A. D., and Ord, J. K., 1973, Spatial autocorrelation: Pion, London, 178 p.Google Scholar
  5. Davis, J. C., 1973, Statistics and data analysis in geology: John Wiley & Sons, New York, 550 p.Google Scholar
  6. Day, P. R., 1965, Particle fractionation and particle-size analysis,in Methods of soil analysis, Part 1: Physical and minerological properties, Ed.: C. A. Black: American Society of Agronomy, Madison, Wisconsin, p. 545–567.Google Scholar
  7. Fisher, R. A., 1936, The use of multiple measurements in taxonomic problems: Ann. Eugenics, v. 19, p. 379–391.Google Scholar
  8. Frey, J. C., and Leonard, A. B., 1952, Pleistocene geology of Kansas: State Geological Survey Kansas Bull. 99, Lawrence, 230 p.Google Scholar
  9. Johnson, W. D., and Adkison, W. L., 1967, Geology of eastern Shawnee County, Kansas, and vicinity: USGS Bull. 1215-A, U.S. Government Printing Office, Washington, D.C., 123 p.Google Scholar
  10. Overall, J.E., and Klett, C.J., 1972, Applied multivariate analysis: McGraw-Hill, New York, 500 p.Google Scholar
  11. Peech, M., 1965, Hydrogen-ion activity,in Methods of soil analysis, Part 2: Chemical and microbiological properties, Ed.: C. A. Black: American Society of Agronomy, Madison, Wisconsin, p. 914–926.Google Scholar
  12. Yevjevich, V., 1972, Stochastic processes in hydrology: Water Resources Publications, Fort Collins, Colorado, 276 p.Google Scholar

Copyright information

© Plenum Publishing Corporation 1978

Authors and Affiliations

  • James B. Campbell
    • 1
  1. 1.Department of GeographyVirginia Polytechnic Institute and State UniversityBlacksburg

Personalised recommendations