Abstract
We consider classification of a realization of the multivariate spatial-temporal Gaussian random field into one of two populations with different regression mean models and factorized covariance matrices. Unknown means and common feature vector covariance matrix are estimated from training samples with observations correlated in space and time, assuming spatial-temporal correlations to be known. We present the first-order asymptotic expansion of the expected error rate associated with linear plug-in discriminant function. Our results are applied to (ecological) data collected from the Lithuanian economical zone in the Baltic sea.