The Use of an Unmanned Aerial Vehicle for Tree Phenotyping Studies

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.

Keywords

• Unmanned aerial vehicles
• ancient woodland
• invasive species identification
• normalized difference spectral index (NDSI)
• k-means clustering