Spatial analysis of airborne laser scanning point clouds for predicting forest structure

Henrike Häbel, Andras Balazs, Mari Myllymäki


The spatial structure of forest, which can be understood as the arrangement of trees with respect to each other, plays a role in various forestry decisions. In this study the spatial structure is summarized by three different indices which were compared on the example of a study site with circular field plots with 9 m radius in Central Finland. The aim was to predict the indices by airborne laser scanning (ALS) and study usefulness of spatial or horizontal summaries of the ALS point cloud. Thus, in addition to commonly used vertical summaries of the point clouds, we explored summaries of the horizontal distribution of the pulse returns through canopy height models thresholded at different height levels. We used these summaries the well-known K-nn estimation method to predict the indices. In this study, we show that quantifying the spatial structure from small sample plots is challenging. Still, we present evidence that the use of spatial metrics improved the prediction of spatial structure of forests, and has potential for improvements possibly for also other variables related to gap structures.


Airborne laser scanning, canopy height model, empty-space function, Euler number, forest resource prediction, spatial pattern of trees

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