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
Article

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

1,* , 1
, 1
 and 2
Received: 4 March 2014; in revised form: 8 April 2014 / Accepted: 14 May 2014 / Published: 28 May 2014
View Full-Text   |   Download PDF [2606 KB, updated 30 May 2014; original version uploaded 28 May 2014]   |   Browse Figures
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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

AMA 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(6):1122-1142.

Chicago/Turabian Style

Yao, Wei; Krull, Jan; Krzystek, Peter; Heurich, Marco. 2014. "Sensitivity Analysis of 3D Individual Tree Detection from LiDAR Point Clouds of Temperate Forests." Forests 5, no. 6: 1122-1142.


Forests EISSN 1999-4907 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert