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• ## Re: AI-GEOSTATS: Quick evaluation of geostatistical problem

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• Hello Koen, ... So this means that you have 37*13 samples? ... hmmm... According to this I would try two things, 1. Indicator kriging (you do it with binomial
Message 1 of 4 , Apr 5, 2004
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Hello Koen,

> The data I use is the leaf area index of a semi-arid vegetation. The leaf
> area index is a vegetation parameter so on locations with no vegetation this
> gives a 0 value. This is an actual measurement.

> Measurements are made in a 13 point 20x20m grid, called an ESU (elementary
> sample unit). The picture below gives the exact configuratoin.
> http://users.pandora.be/requested/images/ESU.gif
> 37 of such 13 point plots were measured, look at the link below for a
> figure.
> http://users.pandora.be/requested/images/allplots.gif
So this means that you have 37*13 samples?

> Given the sparse vegetation this results in the following data distribution
> for all the data points:
> http://users.pandora.be/requested/images/histtot.gif
hmmm...
According to this I would try two things,
1. Indicator kriging (you do it with binomial variable. So you actully
reclassify your variable into 1 and 0). You can do this with gstat in R, I
belive.
2. Have you tried box.cox transformations? MAybe this might give better results
(you do this with geoR in R).
3. remove any outlier (according to a threshhold level) and see what happens.

I think you have classical type of data for ecology (I have similar problems),
so it is somehow dificult to see what is happening because the distribution of
the variable may 'hide' the spatial structure of the variogram. I have
references that jusify any of those, so if you need them just tell me.
Anyway, your variograms look very 'weird'. What program did you used?

Hope this helps,
Good luck,
Marta

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• Hi Keon, I am also a kind of new to this field of Geostatistics. Your research is interesting. I think you and me are dealing with similar type of dataset - my
Message 1 of 4 , Apr 5, 2004
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Hi Keon,

I am also a kind of new to this field of Geostatistics. Your research is
interesting.

I think you and me are dealing with similar type of dataset - my case is
with the nitrate-nitrogen distribution in soils at a watershed scale (3585
Sq km). My data is more skewed than yours, and as Dr Goovaerts suggested-
Indicator kriging remains as a method of choice.

It might be useful for you to go through the beauty of indicator thresholds
(see Isaaks and Srivastava' book - page 417-457). You might explore some
thresholds, as the change in threshold gives you a different scenario about
the spatial distribution of 0s and 1s (see some pictures of the exhaustive
data set from Isaaks and Srivastava's book - page 81-89). Perhaps after
going through these chapters, another interesting step might be to go
through the chapters on Dr. Goovaerts book, and some articles from Van
Meirvienne and Goovaerts might be an asset.

If these efforts do not work out and seems like the distribution of
vegetation patches is totally random, it might be interesting to see what is
the driving force to create these patches to be random (here i might be just
haunching - however - Imagination is more important than knowledge "A.
Einstein").

Good luck. And you seem to be Belgian - thats a great nation - I got my
Masters from Ghent - wonderful memories.

Sanjay

-----------------------------
Sanjay Lamsal
GIS Research Lab
Soil and Water Science Dept.
University of Florida
2169 McCarty A
PO Box 110290
Gainesville, FL 32611-0290
Phone: 352-392-1951 ext 233
Fax: 352-392-3902
----- Original Message -----
From: "Koen Hufkens" <koen.hufkens@...>
To: "AI-GEOSTATS" <ai-geostats@...>
Sent: Sunday, April 04, 2004 6:29 PM
Subject: AI-GEOSTATS: Quick evaluation of geostatistical problem

Hi list,

Sorry for the long mail but I need an opinion on what I've been up to the
last weeks/months.

I was asked as a student to look at spatial relations in sample data with
the intention to optimize the sampling protocol for future field campaignes.

This is a wrapup of my findings:

The data I use is the leaf area index of a semi-arid vegetation. The leaf
area index is a vegetation parameter so on locations with no vegetation this
gives a 0 value. This is an actual measurement.

To give you an idea of the vegetation and it's very sparse nature:

http://users.pandora.be/requested/images/vegetation.jpg

Measurements are made in a 13 point 20x20m grid, called an ESU (elementary
sample unit). The picture below gives the exact configuratoin.

http://users.pandora.be/requested/images/ESU.gif

37 of such 13 point plots were measured, look at the link below for a
figure.

http://users.pandora.be/requested/images/allplots.gif

Given the sparse vegetation this results in the following data distribution
for all the data points:

http://users.pandora.be/requested/images/histtot.gif

The distribution of the seperate ESU plots is therefor also highly skewed.

To investigate spatial relations I used the Moran's I index. This turned out
to be slightly NEGATIVE, wich is rare for ecological data but can be
explained based on the desert like nature of the vegetation. But, none of
these indexes were significant (under Randomization and Monte Carlo terms).

Knowing my data is a little to skewed to evaluate using semivariograms I
plotted them anyway. Wich gave some strange but to be expected results. No
relation is to be detected using all of the 13x37 data points.

omnidirectional:
http://users.pandora.be/requested/images/variotot.gif

directional:
http://users.pandora.be/requested/images/variodir.gif

On an ESU level (13 points, 20x20m plot) I did this again to look at it on a
smaller scale avoiding some of the exess 0's. The outcome was this:

http://users.pandora.be/requested/images/varioesu.gif

So on a small scale it isn't that much better.

I read an article from the man who made up the this sampling protocol in the
first place. He tests the kriging average over the ESU's vs the normal
average over the 13 samples. So I fitted a linear model on the last
semivariogram and did the same calculation.
The outcome was the same: No signicant difference between the kriging
average and the normal average. (I repeated this for all the useable ESU
plots)

So my conclusions would be:

1) this is some fucked up data to let a novice in geostatistics work upon
2) there is no spatial relation to be detected or not significant anyway
3) you don't gain any information by knowing the location of a sample:
3.1 it doesn't say anything about the structure of the vegetation
3.2 because of a lack of spatial relations, random sampling is to be
considered = cheaper less work

Any input on this would be greatly appreciated, I'm a novice so give me hell
if I go wrong. I rather learn the hard way then not at all. Tips to put this
in a more positive daylight would also be appreciated, I've got to sell this
thing to people who started with the believes that some positive relation
would be detected.

Best regards and my excuses for the long post,
Koen.

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