Accuracy assessment and spatial autocorrelation

From: Jonathan Greenberg (greenberg@ucdavis.edu)
Date: Wed May 05 2004 - 15:34:45 PDT

  • Next message: Phil Nott: "Re: Accuracy assessment and spatial autocorrelation"

    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
    



    This archive was generated by hypermail 2b29 : Wed May 05 2004 - 15:37:44 PDT