Abstract
Conditioned Latin Hypercube Sampling (cLHS) is a type of stratified random sampling that accurately represents the variability of environmental covariates in feature space. As the smallest possible sample is important for efficient field work, what is the optimal sample size for digital soil mapping? An optimal sample size accurately represents the variability in the environmental covariates and provides enough samples for predictive models. This paper briefly reviews cLHS and investigates different sample sizes for representing five environmental covariates in a 30,000-ha complex landscape in the Great Basin of southwestern Utah. The cLHS code was run in Matlab™ (Mathworks, 2008) and statistical analysis was performed using the R statistical language (R Development Core Team, 2009). Graphical analysis for continuous data and chi-square analysis of categorical data suggested optimal sample size for this study area is approximately 200 to 300 (0.05–0.1%).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Best, M.G., Lemmon, D.M., and Morris, H.T., 1989. Geologic Map of the Milford Quadrangle and East Half of the Frisco Quadrangle, Beaver County, Utah. USGS, Reston, VA, USA.
Bui, E.N., Simon, D., Schoknecht, N., and Payne, A., 2007. Adequate prior sampling is everything: lessons from the Ord River Basin, Australia, pp. 193–204. In: Lagacherie, P., McBratney, A.B., and Voltz, M. (eds.), Digital Soil Mapping: An Introductory Perspective. Developments in Soil Science, Vol. 31. Elsevier, Amsterdam.
Chavez, P.S. Jr., 1996. Image-based atmospheric corrections – revisited and revised. Photogrammetric Engineering and Remote Sensing 62(9):1025–1036.
ERDAS, 2006. ERDAS Imagine V9.1. ERDAS, Atlanta, GA, USA.
Howell, D., Kim, Y., Haydu-Houdeshell, C., Clemmer, P., Almaraz, R., and Ballmer, R., 2007. Fitting soil property spatial distribution models in the Mojave Desert for digital soil mapping, pp. 465–475. In: Lagacherie, P., McBratney, A.B., Voltz, M. (eds.), Digital Soil Mapping: An Introductory Perspective. Developments in Soil Science, Vol. 31. Elsevier, Amsterdam.
Huete, A.R., 1988. A soil adjusted vegetation index (SAVI). Remote Sensing of the Environment 25:295–309.
IRDIAC (Intermountain Region Digital Image Archive Center), 2008. IRDIAC image search. Retrieved September, 18, 2008 from http://earth.gis.usu.edu.
Mathworks, 2008. Matlab release 2008a. The Mathworks Inc., Natick, MA.
McBratney, A.B., Santos, M.L.M., and Minasny, B., 2003. On digital soil mapping. Geoderma 117(1–2):3–52.
McKenzie, N.J., and Ryan, P.J., 1999. Spatial prediction of soil properties using environmental correlation. Geoderma 89(1–2):67–94.
Minasny, B., 2007. Software (cLHS code). Retrieved August, 2008 from http://www.usyd.edu.au/agric/acpa/people/budi/software.htm
Minasny, B., and McBratney, A.B., 2006. A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences 32(9):1378–1388.
Minasny, B., and McBratney, A.B., 2007. Latin hypercube sampling as a tool for digital soil mapping, pp. 153–165. In: Lagacherie, P., McBratney, A.B., and Voltz, M. (eds.), Digital Soil Mapping: An Introductory Perspective. Developments in Soil Science, Vol. 31. Elsevier, Amsterdam.
McKenzie, N.J.N., and Ryan, P.J., 1999. Spatial prediction of soil properties using environmental correlation. Geoderma, 89(1–2): 67–94.
NED, 2006. USGS National Elevation Dataset. Retrieved March 21, 2009 from http://ned.usgs.gov
NRCS, 2008. National Resource Conservation Service MLRA Explorer. Retrieved September 18, 2008 from http://www.cei.psu.edu/mlra
R Development Core Team, 2009. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org (Last accessed 22 April 2010).
Soil Survey Division Staff, 1993. Soil Survey Manual. Soil Conservation Service. U.S. Department of Agriculture Handbook 18.
Stum, A.K., 2007. Aspect correction tool. Unpublished model. Soil Genesis Lab. Utah State University. Model available upon request.
Tarboton, D., 2008. Terrain Analysis Using Digital Elevation Models (TauDEM). Retrieved June, 8, 2009 from http://hydrology.neng.usu.edu/taudem.
USGS National Gap Analysis Program, 2004. Provisional Digital Land Cover Map for the Southwestern United States. Version 1.0. RS/GIS Laboratory, College of Natural Resources, Utah State University, Logan, UT.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this chapter
Cite this chapter
Brungard, C.W., Boettinger, J.L. (2010). Conditioned Latin Hypercube Sampling: Optimal Sample Size for Digital Soil Mapping of Arid Rangelands in Utah, USA. In: Boettinger, J.L., Howell, D.W., Moore, A.C., Hartemink, A.E., Kienast-Brown, S. (eds) Digital Soil Mapping. Progress in Soil Science, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8863-5_6
Download citation
DOI: https://doi.org/10.1007/978-90-481-8863-5_6
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-8862-8
Online ISBN: 978-90-481-8863-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)