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This article in JEQ

  1. Vol. 27 No. 5, p. 1078-1086
     
    Received: Sept 23, 1997


    * Corresponding author(s): groenigen@itc.nl
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doi:10.2134/jeq1998.00472425002700050013x

Constrained Optimization of Spatial Sampling using Continuous Simulated Annealing

  1. J. W. van Groenigen * and
  2. A. Stein
  1. S oil Science Div., International Institute for Aerospace Survey and Earth Sciences (ITC), P.O. Box 6, 7500 AA Enschede, the Netherlands;
    D ep. of Environmental Sciences, Wageningen Agricultural University, P.O. Box 37, 6700 AA Wageningen, the Netherlands.

Abstract

Abstract

Spatial sampling is an important issue in environmental studies because the sample configuration influences both costs and effectiveness of a survey. Practical sampling constraints and available preinformation can help to optimize the sampling scheme. In this paper, spatial simulated annealing (SSA) is presented as a method to optimize spatial environmental sampling schemes. Sampling schemes are optimized at the point-level, taking into account sampling constraints and preliminary observations. Two optimization criteria have been used. The first optimizes even spreading of the points over a region, whereas the second optimizes variogram estimation using a proposed criterion from the literature. For several examples it is shown that SSA is superior to conventional methods of designing sampling schemes. Improvements up to 30% occur for the first criterion, and an almost complete solution is found for the second criterion. Spatial simulated annealing is especially useful in studies with many sampling constraints. It is flexible in implementing additional, quantitative criteria.

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