Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds

TL;DR

This paper introduces a novel vertical stratification method for airborne LiDAR data that improves the detection of understory trees in complex forests by separating canopy layers without prior shape assumptions.

Contribution

The study presents a new stratification procedure that enhances understory tree detection in LiDAR point clouds, independent of the segmentation method used.

Findings

Improved understory tree detection from 46% to 68%.

Over-segmentation of understory trees increased from 1% to 16%.

Vertical stratification's effectiveness depends on point cloud density.

Abstract

Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies. We applied the proposed approach to the University of Kentucky Robinson Forest - a natural deciduous forest with complex and highly variable terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1% to 16%), while barely affecting the overall segmentation quality of overstory trees. Results of vertical stratification of the canopy showed that the point density of understory canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting understory trees. As shown by inspecting correlations of the results with forest structure, the segmentation approach is applicable to a variety of forest types.