Hi Jonathan,
I agree with the similar response that spatial autocorrelation is not really a concern for the question you are asking. However, it seems you need to define your question a little better. Are you happy predicting that a pixel next to your signature pixel is classified with an accuracy of x? To me that doesn't really mean that much, especially for vegetation where the spectral signature you are using is directly deterimed by your classification variable (the shrub species). For indirect classification (e.g., birds) its a little more impressive because two contiguous correctly classified presence pixels may not have the same vegetation (e.g., the bird uses two different types of vegetation).
If I was a reviewer for the manuscript you submit from this work, I would want to know how well the classification worked for pixels outside of your signature plot.
Another thing to keep in mind is to use kappa or some other measure of accuracy other than pcc, especially if you have an unequal number of signature pixels per class. If your classes are really lopsided (e.g., 200 absence and 20 presence), you can achieve very high accuracy without really doing any better than a random assignment of classes to pixels.
If you want, you can write me off list at laurente@msu.edu to discuss this more. I'm also working on a beta version of some software for this type of classification that you might be interested in.
Also, I can send you a copy of the following proceedings paper that might be of interest:
Laurent, E.J., J.P. LeBouton, M.B. Walters and J. Liu. 2002. Integrating human, satellite and avian perspectives of the landscape for analysis of forest bird distribution patterns. In D. Chamberlain and A. Wilson (eds.) Avian Landscape Ecology: Pure and Applied Issues in the Large-Scale Ecology of Birds. Proceedings of the 11th Annual IALE(UK) Conference. Colin Cross Printers Ltd., Garstang, Great Britain.
-Ed
********************************************
Edward J. Laurent
Ph.D. Candidate, Landscape/Wildlife Ecology
Department of Fisheries and Wildlife
13 Natural Resources Building
Michigan State University
East Lansing, MI 48824-1222
Ph: (517) 353-5468
Fax: (517) 432-1699
http://www.msu.edu/user/laurente
********************************************
--- Jonathan Greenberg <greenberg@ucdavis.edu> wrote:
Remote Sensors:
Me and a colleague (who shall remain unnamed... We will refer to him as
Solomon D.) are having a lively discussion about training/test data with
remote sensing and I was hoping to get some additional feedback on this
problem. We created a species map with maximum likelihood (using 1m IKONOS
imagery), and here's how we created training data (and how we are
approaching, in one case, the testing):
We have mostly USFS plot data with a known center location and plot
boundary, and that has cover values for each species we are after in our
classification. We choose pixels from plots with a high percentage of a
single species, that are readily identifiable as the species in question
(e.g. If we know a plot only contains red fir trees, we manually choose each
pixel belonging to a tree within the boundary of the plots). This, of
course, is not an optimal way of doing this -- in theory we should have
collected individual species in the field, but this was our curse with the
data we had.
Ok, so now we have a bunch of pixels per class, taken from a limited
number of plots (e.g. We may have 1000 red fir pixels, but we took them from
10 plots). The questions is, is it "legitimate" to subdivide the 1000
pixels into two randomly chosen training and test groups (say 60% train and
40% test), and use the 60% to create the map, and validate it with the
remaining 40%, OR do we have a problem with spatial autocorrelation problem
because, while we have 1000s of pixels, the training and test pixels are all
right next to each other in the 10 plots.
In my mind the issue is muddled, because we are training based on color,
and is does the color (within a class) have a strong enough spatial pattern
to warrant a very different training/test setup (e.g. Taking the pixels from
6/10 plots for training and 4/10 for testing?) Thoughts?
--j
-- Jonathan Greenberg Graduate Group in Ecology, U.C. Davis http://www.cstars.ucdavis.edu/~jongreen http://www.cstars.ucdavis.edu AIM: jgrn307 or jgrn3007 MSN: jgrn307@msn.com or jgrn3007@msn.com_____________________________________________________________ Conserve wilderness with a click (free!) and get your own EcologyFund.net email (free!) at http://www.ecologyfund.com.
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