  In this work, we establish the asymptotic normality of the deconvolution
kernel density estimator in the context of strongly mixing random fields. Only
minimal conditions on the bandwidth parameter are required and a simple
criterion on the strong mixing coefficients is provided. Our approach is based
on the Lindeberg's method rather than on Bernstein's technique and coupling
arguments widely used in previous works on nonparametric estimation for spatial
processes. We deal also with nonmixing random fields which can be written as a
(nonlinear) functional of i.i.d. random fields by considering the physical
dependence measure coefficients introduced by Wu (2005).
