Dominant-feature identification in data from Gaussian processes applied to Finnish forest inventory records

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.