Abstract

Numerical analyses of remotely sensed data may valuably contribute to
an understanding of the vegetation/land surface interface by pointing
out at which scales a given variable displays a high level of spatial
variability. Thus, there is a need of methods aimed at classifying many
one-dimensional signals, such as airborne laser profiles, on the basis
of their spatial structure. The present paper proposes a theoretical
framework ensuring a consistent combination of a multi-scale pattern
characterization, based on the Haar wavelet variance (also called in
ecology Two Terms Local Variance, TTLV), with two multivariate
techniques such as principal components analysis (PCA) and hierarchical
cluster analysis. We illustrate our approach by comparing and
classifying 257 laser profiles, with a length of 64 measurements (448
m), that were collected by the BRGM in French Guiana over three main
landscape units with distinct geomorphological and ecological
characteristics. We calculate for each profile a scalogram that
summarized the multi-scale pattern and analyze the structural
variability of profiles via a typology and a classification of
one-dimensional patterns. More than 80% of the variability between
spatial patterns of laser profiles has been summarized by two PCA axes,
while four classes of spatial patterns were identified by cluster
analysis. Each landscape unit was associated with one or two dominant
classes of spatial patterns. These results confirmed the ability of the
method to extract landscape scaling properties from complex and large
sets of remotely sensed data.

Author Keywords: Multi-scale pattern analysis; Haar wavelet variance;
Two Terms Local Variance; Classification of spatial patterns; Laser
altimeter profile; Landforms; Forest canopy; Tropical rain forest