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Re: AI-GEOSTATS: Quick evaluation of geostatistical problem

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  • Sanjay Lamsal
    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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