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

arXiv: https://arxiv.org/abs/1701.00198 · HTML: https://mlsanity.com/paper/1701.00198/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans](https://mlsanity.com/paper/2204.11620/) (2022)
- [Predicting Vegetation Stratum Occupancy from Airborne LiDAR Data with Deep Learning](https://mlsanity.com/paper/2201.08051/) (2022)
- [Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset](https://mlsanity.com/paper/2511.00653/) (2025)

## Abstract (arXiv)

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.