Hi,
I'm new to this forum and would like to retrieve anisotropic parameters
from a sample semi-variogram. I don't need to krig, because I'm already
working at 1 m resolution and don't have to refine any further. Instead,
I'm just looking for an anisotropic description of the autocorrelation
patterns in the area where I'm working. I have three questions that I
hope you can address, for I've been going in circles with them, a bit:
I think that I don't want to fit a spherical model to the the experimental
variogram, because I want to know what's actually coming out of the
observations themselves, but I am unclear about how the initial
experimental parameters are generated by gstat, prior to using them to
fit/converge with a model of choice (for smoothing/kriging). Perhaps the
initial raw "curve" parameters aren't a good representation of the
experimental cloud and curve fitting is necessary to best represent my
data? I will be comparing scenes through time, so my most important
criteria for generating the nugget, sill, and range, is consistency. I'm
less concerned about smoothing in a way that would permit kriging,
particularly if smoothing moves me away from the raw data in a way that
might be inconsistent between years. So if you have any insight into
this, I would love to know more.
Is it possible to retrieve a nugget, sill, and range from a sample
variogram? Ideally, I want to take the parameters for each image and add
them as the last row in an existing table. I notice that in the gstat
program (non-R version) the user can tweak with an interactive interface,
but what I'm really after is a way to automate all of this as much as
possible.
Is it possible to include distance tolerances for the point pairs so that
you can subsample the data to be evaluated, playing around with using
points at varying distance lengths away from one another in the
calculations? For instance, if I have regularly spaced points that are
about 1-1.5 m apart, but want to select pairs as if only points 4 - 5 m
apart were present, is there a way to do that? I could do this by
fiddling with the input shapefile (deleting intervening points that I
didn't want evaluated) before I import it, but it would be much nicer to
do it here if it's possible.
If it's of any help, this is the code I have produced, so far:
library(maptools)
library(gstat)
baseCRS<-CRS("+proj=utm+zone=17+datum=NAD83")
test <- readShapePoints("C:\\89802_test.shp", proj4string = baseCRS,
verbose = TRUE)
summary(test)
test.v <- variogram(GRID_CODE~coords.x1+coords.x2, data=test, alpha =
c(0,45,90,135),
cutoff =41, width = 2)
Thanks,
Annie
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
Annette H. Elmore
Land Cover Dynamics & Environmental Processes
Eastern Geographic Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Email: aelm...@usgs.gov
Voice: 703 648 4805
Fax: 703 648 4603
* * * * * * * * * * * * * *
If I don't know I don't know
I think I know
If I don't know I know
I think I don't know
-- Knots
R.D. Laing
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