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

TL;DR

This paper introduces novel spatial features derived from airborne laser scanning data, such as the Euler number and empty-space function, to improve the prediction of forest variables by capturing horizontal canopy structure.

Contribution

It presents new spatial features based on the Euler number and empty-space function for ALS data, enhancing forest variable prediction accuracy.

Findings

Spatial features improve prediction accuracy of forest variables.

Proposed features capture horizontal canopy structure effectively.

Method demonstrated on a Finnish study site.

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.