Estimating tree diameters from an autonomous below-canopy uav with mounted lidar

Abstract

Below-canopy UAVs hold promise for automated forest surveys because their sensors can provide detailed information on below-canopy forest structures, especially in dense forests, which may be inaccessible to above-canopy UAVs, aircraft, and satellites. We present an end-to-end autonomous system for estimating tree diameters using a below-canopy UAV in parklands. We used simultaneous localization and mapping (SLAM) and LiDAR data produced at flight time as inputs to diameter-estimation algorithms in post-processing. The SLAM path was used for initial compilation of horizontal LiDAR scans into a 2D cross-sectional map, and then optimization algorithms aligned the scans for each tree within the 2D map to achieve a precision suitable for diameter measurement. The algorithms successfully identified 12 objects, 11 of which were trees and one a lamppost. For these, the estimated diameters from the autonomous survey were highly correlated with manual ground-truthed diameters (R2 = 0.92, root mean squared error = 30.6%, bias = 18.4%). Autonomous measurement was most effective for larger trees (>300 mm diameter) within 10 m of the UAV flight path, for medium trees (200–300 mm diameter) within 5 m, and for trees with regular cross sections. We conclude that fully automated below-canopy forest surveys are a promising, but still nascent, technology and suggest directions for future research.