Ahmed, Shara, Nicholson, Kate, Muto, Paul, Perry, Justin and Dean, John (2021) Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland. PLoS ONE, 16 (11). e0260056. ISSN 1932-6203
|
Text
pone.0260056.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (6MB) | Preview |
Abstract
An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.
Item Type: | Article |
---|---|
Subjects: | C100 Biology D500 Forestry F800 Physical and Terrestrial Geographical and Environmental Sciences |
Department: | Faculties > Health and Life Sciences > Applied Sciences |
Depositing User: | Elena Carlaw |
Date Deposited: | 16 Nov 2021 09:35 |
Last Modified: | 17 Nov 2021 09:45 |
URI: | http://nrl.northumbria.ac.uk/id/eprint/47736 |
Downloads
Downloads per month over past year