A conditioned Latin hypercube sampling algorithm incorporating operational constraints

Digital Soil Assessments and Beyond - Proceedings of the Fifth Global Workshop on Digital Soil Mapping

Volumn , Issue , 2012, Pages 227-231

  Roudier, Pierre ^a   Hewitt, Allan E ^a   Beaudette, Dylan E ^b  

^a LANDCARE RESEARCH   (New Zealand)

^b USDA NRCS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COST EFFECTIVENESS; MAPPING; SOIL SURVEYS;

ALLOCATION METHODS; COST EFFECTIVE; DIGITAL SOIL MAPPINGS; LATIN HYPERCUBE SAMPLING; OPERATIONAL CONSTRAINTS; OPTIMIZED SAMPLING; SAMPLING PROCESS; SAMPLING SCHEMES;

SOILS;

EID: 84866371499     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1201/b12728-46     Document Type: Conference Paper

References (8)

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3. Metropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller (1953). Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics 21, 1087-1092.
4. Minasny, B. and McBratney, A.B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences 32, 1378-1388.
5. Minasny, B. and McBratney, A.B. (2010). Methodologies for global soil mapping. In J. Boettinger, D. Howell, A. Moore, A. Hartemink, and S. Kiesnast-Brown (Eds.), Digital Soil Mapping: Bridging Research, Environmental Application, and Operation, Progress in Soil Science. New York: Springer.
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8. Roudier, P. (2011). clhs: a R package for conditioned Latin hypercube sampling. http://cran.r-project.org/web/packages/clhs/index.html.