I tidied up my email today and discovered that I'd not posted a summary to
my question (see below). Thanks to Brian Gray, Carme Hervada-Sala,
Pierre Goovaerts. Some people could read the gif image of our
semivariogram and some couldn't. Apparently a gif generated from
alchemy is not a good format to use.
Nobody came up with any papers where aspect had been included in a
kriging analysis. I was advised to check the semi variogram cloud and
the number of pairs used. We concluded that the variogram showed a
pure nugget effect.
> We have two sets of data (average monthly soil temperature) from
> different scales:
> 1) climate station data throughout the south island of New Zealand -
> always on flat sites, mostly but not only on the plains or in rolling hill
> country. #data = 23
> 2) a set of 7 clusters of data from data loggers. Each cluster covers
> approximately 10-15km and is designed to sample a range of elevations
> and aspects (stratified). The clusters are located at 7 sites throughout
> the south island but only on the steeplands. #data = 109
> We have detrended this data for latitude, elevation, distance from coast
> and aspect. The resulting semivariogram of the residuals is attached. X
> axis is metres. No declustering has been done. The semivariogram for
> the data from the datalogger only shows a similar pattern but with
> for the intermediate lags missing. A semivariogram from climate station
> data only (but a decade earlier when there were more stations)
> a range of spatial autocorrelation of 100km.
> 1) Any comments on the validity of what we have done (there are a
> number of things we are unsure about)
> 2) Does the point at the first lag on the semivariogram indicate that
> is no spatial autocorrelation at 10km
> 3) Can this semivariogram be modelled (we presume not)
> Can anyone advise of any useful papers where aspect has been
> included in a kriging analysis?
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