  The problem of testing for the presence of epidemic changes in random fields
is investigated. In order to be able to deal with general changes in the
marginal distribution, a Cram\'er-von Mises type test is introduced which is
based on Hilbert space theory. A functional central limit theorem for
$\rho$-mixing Hilbert space valued random fields is proven. In order to avoid
the estimation of the long-run variance and obtain critical values, Shao's
dependent wild bootstrap method is adapted to this context. For this, a joint
functional central limit theorem for the original and the bootstrap sample is
shown. Finally, the theoretic results are supplemented by a short simulation
study.
