  In spatial data, location-dependent variation leads to connected structures
known as features. Variations occur at different spatial scales and possibly
originate from distinct underlying processes. Each of these scales is
characterized by its own dominant features. Here we introduce a statistical
method for identifying these scales and their dominant features in data from
Gaussian processes. This identification involves credibly recognizing the
dominant features by scale-space decomposition and assessing feature attributes
by estimating covariance function parameters of the underlying processes and
their associations to potential drivers. We analyze Finnish forest inventory
data from the 1920s using this dominant-feature identification method and
identify the scales of variation in basal area estimates of most common Finnish
trees, including Scots pine, Norway spruce, birch, and other native deciduous
trees. Comparing the resulting scale-dependent features and their attributes in
these tree species, we identify the different effects of edaphic and
anthropogenic drivers on the spatial distribution of their basal areas. These
data are analyzed for the first time in terms of their scale of variation, and
the resulting scale-dependent maps and estimates are an essential contribution
to the historical forest ecology of Fennoscandia. Until now, this analysis was
not possible with conventional methods.
