Sensitivity analysis of 3D individual tree detection from LiDAR point clouds of temperate forests

Wei Yao, Jan Krull, Peter Krzystek, Marco Heurich

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)

Abstract

Light detection and ranging (LiDAR) sampling or full-area coverage is deemed as favorable means to achieve timely and robust characterizations of forests. Recently, a 3D segmentation approach was developed for extracting single trees from LiDAR data. However, key parameters for modules used in the strategy had to be empirically determined. This paper highlights a comprehensive study for the sensitivity analysis of 3D single tree detection from airborne LiDAR data. By varying key parameters, their influences on results are to be quantified. The aim of the study is to enlighten the optimal combination of parameter values towards new applications. For the experiment, a number of sample plots from two temperate forest sites in Europe were selected. LiDAR data with a point density of 25 pts/m2 over the first site in the Bavarian forest national park were captured with under both leaf-on and leaf-off conditions. Moreover, a Riegl scanner was used to acquire data over the Austrian Alps forest with four-fold point densities of 5 pts/m2, 10 pts/m2, 15 pts/m2 and 20 pts/m2, respectively, under leaf-off conditions. The study results proved the robustness and efficiency of the 3D segmentation approach. Point densities larger than 10 pts/m2 did not seem to significantly contribute to the improvement in the performance of 3D tree detection. The performance of the approach can be further examined and improved by optimizing the parameter settings with respect to different data properties and forest structures.
Original languageEnglish
Pages (from-to)1122-1142
Number of pages21
JournalForests
Volume5
Issue number6
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Airborne LiDAR
  • Segmentation
  • Sensitivity analysis
  • Temperate forests
  • Tree detection

ASJC Scopus subject areas

  • Forestry

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