A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data

TL;DR

This paper introduces a non-parametric, robust tree segmentation method using airborne LiDAR data that does not rely on prior crown shape assumptions, effectively identifying a high percentage of trees in complex deciduous forests.

Contribution

The paper presents a novel iterative segmentation approach that adapts to complex terrain and vegetation without requiring predefined crown models, improving robustness and accuracy.

Findings

94% detection of dominant and co-dominant trees

77% overall segmentation accuracy

Robustness to terrain and stand structure variations

Abstract

This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. To evaluate its performance, the approach was applied to the University of Kentucky Robinson Forest, a deciduous closed-canopy forest with complex terrain and vegetation conditions. The approach identified 94% of dominant and co-dominant trees with a false detection rate of 13%. About 62% of intermediate, overtopped, and dead trees were also detected with a false detection rate of 15%. The overall segmentation accuracy was 77%. Correlations of the segmentation scores of the proposed approach with local terrain and stand metrics was not significant, which is likely an indication of the robustness of the approach as results are not sensitive to the differences in terrain and stand structures.