Kriging standard errors as function of grid spacing and block size
ossfim.Rd
Calculate, for a given variogram model, ordinary block kriging standard errors as a function of sampling spaces and block sizes
Arguments
- spacings
range of grid (data) spacings to be used
- block.sizes
range of block sizes to be used
- model
variogram model, output of
vgm
- nmax
set the kriging neighbourhood size
- debug
debug level; set to 32 to see a lot of output
Value
data frame with columns spacing
(the grid spacing),
block.size
(the block size), and kriging.se
(block kriging
standard error)
References
Burrough, P.A., R.A. McDonnell (1999) Principles of Geographical Information Systems. Oxford University Press (e.g., figure 10.11 on page 261)
Burgess, T.M., R. Webster, A.B. McBratney (1981) Optimal interpolation and isarithmic mapping of soil properties. IV Sampling strategy. The journal of soil science 32(4), 643-660.
McBratney, A.B., R. Webster (1981) The design of optimal sampling schemes for local estimation and mapping of regionalized variables: 2 program and examples. Computers and Geosciences 7: 335-365.
Note
The idea is old, simple, but still of value. If you want to map a variable with a given accuracy, you will have to sample it. Suppose the variogram of the variable is known. Given a regular sampling scheme, the kriging standard error decreases when either (i) the data spacing is smaller, or (ii) predictions are made for larger blocks. This function helps quantifying this relationship. Ossfim probably refers to ``optimal sampling scheme for isarithmic mapping''.