Arctic sea-ice extent has been of considerable interest to scientists in recent years, mainly due to its decreasing trend over the past 20 years. In this talk, we propose a hierarchical spatio-temporal generalized linear model (GLM) for binary Arctic-sea-ice data, where data dependencies are introduced through a latent spatio-temporal linear mixed effects model. By using a fixed number of spatial basis functions, the resulting model achieves both dimension reduction and nonstationarity for spatial fields at different time points. An EM algorithm is proposed to estimate model parameters, and a (empirical) hierarchical statistical modeling approach is used to obtain the predictive distribution of the latent spatio-temporal process. This approach is applied to spatial binary Arctic-sea-ice data in the month of September for the past 20 years, where several posterior summaries are obtained to detect changes of Arctic sea-ice cover. This is joint work with Dr Bohai Zhang of the University of Wollongong.