  This paper proposes parametric and non-parametric hypothesis testing
algorithms for detecting anisotropy -- rotational variance of the covariance
function in random fields. Both algorithms are based on resampling mechanisms,
which enable avoiding relying on asymptotic assumptions, as is common in
previous algorithms. The algorithms' performance is illustrated numerically in
simulation experiments and on real datasets representing a variety of potential
challenges.
