Remote sensing tools are increasingly being used to survey forest structure. Most current methods rely on GPS signals, which are available in above-canopy surveys or in below-canopy surveys of open forests, but may be absent in below-canopy environments of dense forests. We trialled a technology that facilitates mobile surveys in GPS-denied below-canopy forest environments. The platform consists of a battery-powered UAV mounted with a LiDAR. It lacks a GPS or any other localisation device. The vehicle is capable of an 8 min flight duration and autonomous operation but was remotely piloted in the present study. We flew the UAV around a 20 m × 20 m patch of roadside trees and developed postprocessing software to estimate the diameter-at-breast-height (DBH) of 12 trees that were detected by the LiDAR. The method detected 73% of trees greater than 200 mm DBH within 3 m of the flight path. Smaller and more distant trees could not be detected reliably. The UAV-based DBH estimates of detected trees were positively correlated with the human-based estimates (R2 = 0.45, p = 0.017) with a median absolute error of 18.1%, a root-mean-square error of 25.1% and a bias of −1.2%. We summarise the main current limitations of this technology and outline potential solutions. The greatest gains in precision could be achieved through use of a localisation device. The long-term factor limiting the deployment of below-canopy UAV surveys is likely to be battery technology.

No abstract

No abstract

No abstract

No abstract