By Hengl, Tomislav
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Additional resources for A Practical Guide to Geostatistical Mapping of Environmental Variables
G. Krige and the statistician H. S. Sichel. The technique was first published in Krige (1951), but it took almost a decade until a French mathematician G. , 2007). A standard version of kriging is called ordinary kriging (OK). 1) where µ is the constant stationary function (global mean) and ε (s) is the spatially correlated stochastic part of variation. 2) i=1 where λ0 is the vector of kriging weights (wi ), z is the vector of n observations at primary locations. In a way, kriging can be seen as a sophistication of the inverse distance interpolation.
It was basically a theory for modelling stochastic surfaces using spatially sampled variables. 3 Statistical spatial prediction models 15 differences between the pairs are more less equal the global variance11 . This is known as the spatial auto-correlation effect. 879) 1000 2000 3000 distance 4000 5000 1000 2000 3000 4000 5000 distance Fig. 7: Steps of variogram modelling: (a) location of points (300), (b) variogram cloud showing semivariances for 44850 pairs, (c) semivariances aggregated to lags of about 300 m, and (d) the final variogram model fitted using the default settings in gstat.
Oxford University Press, New York, 542 pp. uk/eoenv/ — The Encyclopedia of Environmetrics. org — A research association that promotes use of geostatistical methods for environmental applications. 26 Theoretical backgrounds 2 Regression-kriging As we saw in the previous chapter, there seems to be many possibilities to map environmental variables using geostatistics. In reality, we always try to go for the most flexible, most comprehensive and the most robust technique (preferably implemented in a software with an user-friendly GUI).
A Practical Guide to Geostatistical Mapping of Environmental Variables by Hengl, Tomislav