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

Hamid Hamraz, Marco A. Contreras, Jun Zhang

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

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.

Original languageEnglish
Pages (from-to)532-541
Number of pages10
JournalInternational Journal of Applied Earth Observation and Geoinformation
Volume52
DOIs
StatePublished - Oct 1 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.

Keywords

  • Crown delineation
  • Remote forest inventory
  • Remote sensing
  • Tree detection evaluation
  • Tree-level forest data

ASJC Scopus subject areas

  • Global and Planetary Change
  • Earth-Surface Processes
  • Computers in Earth Sciences
  • Management, Monitoring, Policy and Law

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