Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover

Wallace, L, Bellman, C, Hally, B, Hernandez, J, Jones, S and Hillman, S 2019, 'Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover', Forests, vol. 10, no. 284, pp. 1-14.


Document type: Journal Article
Collection: Journal Articles

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Title Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover
Author(s) Wallace, L
Bellman, C
Hally, B
Hernandez, J
Jones, S
Hillman, S
Year 2019
Journal name Forests
Volume number 10
Issue number 284
Start page 1
End page 14
Total pages 14
Publisher M D P I AG
Abstract Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.
Subject Forestry Management and Environment
Photogrammetry and Remote Sensing
Keyword(s) UAS
Forest measurement
Structure from motion
Image based point clouds
RPAS
Drones
DOI - identifier 10.3390/f10030284
Copyright notice © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
ISSN 1999-4907
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