Greets to the statists, I want to "describe" my multispectral (Landsat5_TM) composite datasets with respect to their between vs. within heterogeneity. The idea is that a unitemporal data set exhibits less between-axes than within-axes (spectral bands) heterogeneity. The opposite "should" be in the case of a bi-temporal dataset (in my case a pre-fire and a postfire), where the between-axes (spectral bands) "should" be more contrasted.
I was looking for various multivariate tests but found nothing that works globally on the images (i.e. without the necessity to work on samples/classes extracted from the images), nothing that I can handle without the need to do my homework for hours first, something easy to understand, estimate and explain. (I also asked in r-user but due to the nature of the question I guess it correctly passed unanswered.) A friend suggested spatial autocorrelation as an option (mentioned (also) Moran's Index, Jaccart, MANOVA, Mixed effect model). I have a very basic experience on autocorrelation (reading the book Applied Spatial Analysis with R and having dome some exercises with a friend using climatic data). While I am studying potential answers, I will be eXtremely grateful for any help, advise, comment, hint on it (always doable within grass; R). Milles mercis, Nikos. _______________________________________________ grass-stats mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-stats
