  This paper develops a unified and computationally efficient method for
change-point estimation along the time dimension in a non-stationary
spatio-temporal process. By modeling a non-stationary spatio-temporal process
as a piecewise stationary spatio-temporal process, we consider simultaneous
estimation of the number and locations of change-points, and model parameters
in each segment. A composite likelihood-based criterion is developed for
change-point and parameters estimation. Under the framework of increasing
domain asymptotics, theoretical results including consistency and distribution
of the estimators are derived under mild conditions. In contrast to classical
results in fixed dimensional time series that the localization error of
change-point estimator is $O_{p}(1)$, exact recovery of true change-points can
be achieved in the spatio-temporal setting. More surprisingly, the consistency
of change-point estimation can be achieved without any penalty term in the
criterion function. In addition, we further establish consistency of the number
and locations of the change-point estimator under the infill asymptotics
framework where the time domain is increasing while the spatial sampling domain
is fixed. A computationally efficient pruned dynamic programming algorithm is
developed for the challenging criterion optimization problem. Extensive
simulation studies and an application to U.S. precipitation data are provided
to demonstrate the effectiveness and practicality of the proposed method.
