Subsampling and bootstrap methods have been suggested in the literature to nonparametrically estimate the variance and distribution of statistics computed from spatial data. Usually stationary data are required to ensure that the methods work. However, in empirical applications the assumption of stationarity often must be rejected. This talk presents consistent bootstrap and subsampling methods to estimate the variance and distributions of statistics based on non-stationary spatial lattice data. Applications to forestry are also discussed.