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AI-GEOSTATS: CV via kriging ?

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  • Dean Monroe
    Group: I am working on a project where we are looking at the local variance of crop density, by using a vegetative index (NDVI). The spot sensor readings are
    Message 1 of 1 , Oct 10, 2003
      Group:

      I am working on a project where we are looking at the local variance of crop density, by using a vegetative index (NDVI). The spot sensor readings are approx 4m apart (grid form) and cover a 160 acre field. Specific to my research, I am interested in the local CV (coefficient of variation) of crop density - but what constitutes local? My thought to answer this question is to 1.) estimate the variogram for the field, 2.) use the range as a search radius (definition of local) and 3.) use an objective function to estimate the weighted CV value points already on the collection grid (i.e. no prediction). In other words, I want to krige based on a spatially weighted CV rather than a spatially weighted mean.

      I am relatively new to geostatistics. May I have some feedback on this procedure. Is it valid? Is there a better way? What are your thoughts?

      Thanks very much and my best,
      Dean Monroe
      Oklahoma State University
      Environmental Sciences



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