Next Article in Journal
Governing Forest Landscape Restoration: Cases from Indonesia
Next Article in Special Issue
Mapping Above- and Below-Ground Biomass Components in Subtropical Forests Using Small-Footprint LiDAR
Previous Article in Journal
Field Supervisory Test of DREB-Transgenic Populus: Salt Tolerance, Long-Term Gene Stability and Horizontal Gene Transfer
Previous Article in Special Issue
Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description
Forests 2014, 5(6), 1122-1142; doi:10.3390/f5061122

Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests

1,* , 1
1 Department of Geoinformatics, University of Applied Sciences-Munich, Munich 80333, Germany 2 Bavarian Forest National Park, Grafenau 94481, Germany
* Author to whom correspondence should be addressed.
Received: 4 March 2014 / Revised: 8 April 2014 / Accepted: 14 May 2014 / Published: 28 May 2014
View Full-Text   |   Download PDF [2606 KB, 30 May 2014; original version 28 May 2014]   |   Browse Figures


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.
Keywords: tree detection; segmentation; temperate forests; sensitivity analysis; airborne LiDAR tree detection; segmentation; temperate forests; sensitivity analysis; airborne LiDAR
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
MDPI and ACS Style

Yao, W.; Krull, J.; Krzystek, P.; Heurich, M. Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests. Forests 2014, 5, 1122-1142.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert