Next Article in Journal
Land-Use and Land-Cover Mapping Using a Gradable Classification Method
Next Article in Special Issue
Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery
Previous Article in Journal
Application of Semi-Automated Filter to Improve Waveform Lidar Sub-Canopy Elevation Model
Previous Article in Special Issue
Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing
Article Menu

Export Article

Open AccessArticle

Development of a UAV-LiDAR System with Application to Forest Inventory

School of Geography and Environmental Studies, University of Tasmania, Hobart, TAS 7001, Australia
Author to whom correspondence should be addressed.
Remote Sens. 2012, 4(6), 1519-1543;
Received: 14 March 2012 / Revised: 14 May 2012 / Accepted: 17 May 2012 / Published: 25 May 2012
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
PDF [13408 KB, uploaded 19 June 2014]


We present the development of a low-cost Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) system and an accompanying workflow to produce 3D point clouds. UAV systems provide an unrivalled combination of high temporal and spatial resolution datasets. The TerraLuma UAV-LiDAR system has been developed to take advantage of these properties and in doing so overcome some of the current limitations of the use of this technology within the forestry industry. A modified processing workflow including a novel trajectory determination algorithm fusing observations from a GPS receiver, an Inertial Measurement Unit (IMU) and a High Definition (HD) video camera is presented. The advantages of this workflow are demonstrated using a rigorous assessment of the spatial accuracy of the final point clouds. It is shown that due to the inclusion of video the horizontal accuracy of the final point cloud improves from 0.61 m to 0.34 m (RMS error assessed against ground control). The effect of the very high density point clouds (up to 62 points per m2) produced by the UAV-LiDAR system on the measurement of tree location, height and crown width are also assessed by performing repeat surveys over individual isolated trees. The standard deviation of tree height is shown to reduce from 0.26 m, when using data with a density of 8 points perm2, to 0.15mwhen the higher density data was used. Improvements in the uncertainty of the measurement of tree location, 0.80 m to 0.53 m, and crown width, 0.69 m to 0.61 m are also shown. View Full-Text
Keywords: Unmanned Aerial Vehicles; LiDAR; MEMS IMU; Kalman Filter; sensor integration; forestry Unmanned Aerial Vehicles; LiDAR; MEMS IMU; Kalman Filter; sensor integration; forestry

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Wallace, L.; Lucieer, A.; Watson, C.; Turner, D. Development of a UAV-LiDAR System with Application to Forest Inventory. Remote Sens. 2012, 4, 1519-1543.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top