DeepAI AI Chat
Log In Sign Up

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

by   Hamid Hamraz, et al.

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 cost of introducing a fair number of over-segmented understory trees (increased from 1 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.


page 6

page 11


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

This paper presents a non-parametric approach for segmenting trees from ...

Remote sensing of forests using discrete return airborne LiDAR

Airborne discrete return light detection and ranging (LiDAR) point cloud...

A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery

Recognising individual trees within remotely sensed imagery has importan...

Graph-based methods for analyzing orchard tree structure using noisy point cloud data

Digitisation of fruit trees using LiDAR enables analysis which can be us...

Tree Reconstruction using Topology Optimisation

Generating accurate digital tree models from scanned environments is inv...

Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees

The purpose of this study was to investigate the use of deep learning fo...

Forest Tree Detection and Segmentation using High Resolution Airborne LiDAR

This paper presents an autonomous approach to tree detection and segment...