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• ## Re: AI-GEOSTATS: average semi-variogram

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• ... Hard one without the mathematical writing, I looked it up and you refer to following calculations I presume (end of the page):
Message 1 of 9 , Mar 2, 2004
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> ...
> gamma_Y(x,x') = 1/|B|^2 \sum_{w in B} \sum_{w' in B} gamma_Z(x+w,x'+w')
>
> stays a biproduct, which would more or less (or more less) allow to infer
> gamma_Z from gamma_Y. However you don't seem to need relation. You can go
> along with gamma_Y only.

Hard one without the mathematical writing, I looked it up and you refer to
following calculations I presume (end of the page):

http://uk.geocities.com/drisobelclark/PG1979/Chapter_3/Part2.htm

> The first problem is that you measure according to equation (2) but you
> want
> to estimate according to equation (1). Anyway (2) should well approximate
> (1).

It could do the trick, but then again... as someone asked before is it
worth the effort, considering the pretty dense sampling grid in the
plot/block maybe this isn't the best way to approach the problem!?

> The second problem is: The variogram of Y should be very smooth in the
> origing, but it is most sure not Gaussian.

Correct on that one.
Or, I've got data that is highly skewed (LAI, leaf area indexes) and other
data that isn't (that much, reflectance/albedo measurements). So getting a
variogram in the first place has horror qualities (transforming,
backtransforming and that mess) even if I do the actual fitting on all the
data from multiple plots/blocks.

Thanks for all the input,
Koen.

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