Title: Spatial analysis of airborne laser scanning point clouds for predicting forest variables,
Abstract: With recent developments in remote sensing technologies, plot-level forest
resources can be predicted utilizing airborne laser scanning (ALS). The
prediction is often assisted by mostly vertical summaries of the ALS point
clouds. We present a spatial analysis of the point cloud by studying the
horizontal distribution of the pulse returns through canopy height models
thresholded at different height levels. The resulting patterns of patches of
vegetation and gabs on each layer are summarized to spatial ALS features. We
propose new features based on the Euler number, which is the number of patches
minus the number of gaps, and the empty-space function, which is a spatial
summary function of the gab space. The empty-space function is also used to
describe differences in the gab structure between two different layers. We
illustrate usefulness of the proposed spatial features for predicting different
forest variables that summarize the spatial structure of forests or their
breast height diameter distribution. We employ the proposed spatial features,
in addition to commonly used features from literature, in the well-known k-nn
estimation method to predict the forest variables. We present the methodology
on the example of a study site in Central Finland.