Ahmed, Shara, Nicholson, Catherine E., Muto, Paul, Perry, Justin and Dean, John (2021) The Use of an Unmanned Aerial Vehicle for Tree Phenotyping Studies. Separations, 8 (9). p. 160. ISSN 2297-8739
|
Text
separations-08-00160.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (4MB) | Preview |
Abstract
A strip of 20th-century landscape woodland planted alongside a 17th to mid-18th century ancient and semi-natural woodland (ASNW) was investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with a multispectral image camera (MSI). A simple classification approach of normalized difference spectral index (NDSI), derived using principal component analysis (PCA), enabled the identification of the non-native trees within the 20th-century boundary. The tree species within this boundary, classified by NDSI, were further segmented by the machine learning segmentation method of k-means clustering. This combined innovative approach has enabled the identification of multiple tree species in the 20th-century boundary. Phenotyping of trees at canopy level using the UAV with MSI, across 8052 m2, identified black pine (23), Norway maple (19), Scots pine (12), and sycamore (19) as well as native trees (oak and silver birch, 27). This derived data was corroborated by field identification at ground-level, over an area of 6785 m2, that confirmed the presence of black pine (26), Norway maple (30), Scots pine (10), and sycamore (14) as well as other trees (oak and silver birch, 20). The benefits of using a UAV, with an MSI camera, for monitoring tree boundaries next to a new housing development are demonstrated.
Item Type: | Article |
---|---|
Additional Information: | Funding information: This research was funded by Northumbria University. |
Uncontrolled Keywords: | unmanned aerial vehicles; ancient woodland; invasive species identification; normalized difference spectral index (NDSI); k-means clustering |
Subjects: | C100 Biology C200 Botany C500 Microbiology |
Department: | Faculties > Health and Life Sciences > Applied Sciences |
Depositing User: | Elena Carlaw |
Date Deposited: | 20 Sep 2021 09:19 |
Last Modified: | 20 Sep 2021 10:49 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/47258 |
Downloads
Downloads per month over past year