  Dominant features of spatial data are connected structures or patterns that
emerge from location-based variation and manifest at specific scales or
resolutions. To identify dominant features, we propose a sequential application
of multiresolution decomposition and variogram function estimation.
Multiresolution decomposition separates data into additive components, and in
this way enables the recognition of their dominant features. A dedicated
multiresolution decomposition method is developed for arbitrary gridded spatial
data, where the underlying model includes a precision and spatial-weight matrix
to capture spatial correlation. The data are separated into their components by
smoothing on different scales, such that larger scales have longer spatial
correlation ranges. Moreover, our model can handle missing values, which is
often useful in applications. Variogram function estimation can be used to
describe properties in spatial data. Such functions are therefore estimated for
each component to determine its effective range, which assesses the
width-extent of the dominant feature. Finally, Bayesian analysis enables the
inference of identified dominant features and to judge whether these are
credibly different. The efficient implementation of the method relies mainly on
a sparse-matrix data structure and algorithms. By applying the method to
simulated data we demonstrate its applicability and theoretical soundness. In
disciplines that use spatial data, this method can lead to new insights, as we
exemplify by identifying the dominant features in a forest dataset. In that
application, the width-extents of the dominant features have an ecological
interpretation, namely the species interaction range, and their estimates
support the derivation of ecosystem properties such as biodiversity indices.
