Hi folks,

I've got a consulting client who has high-resolution (0.4 m) raster data
from remote sensing that covers an area about 5 km x 5 km, which naturally
yields a very large dataset (~ 15.625 million pixels) at each point in time.
They have repeated measurements at 4 time points for this area on a
continuous variable that essentially represents which kind of vegetation is
most dominant (forage plants vs. weeds) within the pixel. They want to use
things like land use type, precipitation, soil type, and the slope and
aspect of the ground in each pixel to predict the changes over time in the
outcome variable.

My initial thought about how to analyze the data was to use a hierarchical
linear (mixed effects) model with time points nested within pixels to model
the typical longitudinal trajectory of the outcome and how the predictors
affect that trajectory. My dilemma is that they want to use the entire
dataset to do their models, which means the dataset is so large that most of
the analysis tools I'm used to using are simply going to choke on it. In
addition, using a random effect for each pixel might account for temporal
autocorrelation, but I suspect there would still be substantial spatial
autocorrelation not modeled with that approach. 

So, I thought I'd ask here to see what suggestions you have on software
tools and/or statistical models that might be able to handle this. The
client mentioned IDL & ENVI having good tools for handling large raster
datasets, but I'm not familiar with them and what they can do in terms of
estimating formal statistical models. 

Steven J. Pierce, M.S., Ph.D. Candidate
Associate Director
Center for Statistical Training & Consulting (CSTAT)
Michigan State University
178 Giltner Hall
East Lansing, MI 48824

Office Phone: (517) 353-9288 
Office Fax: (517) 353-9307
E-mail: pierces1@msu.edu
Web: http://www.cstat.msu.edu



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