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Title: Estimating tree diameters from an autonomous below-canopy uav with mounted lidar
Authors: Chisholm, Ryan A. 
Rodríguez-Ronderos, M. Elizabeth
Lin, Feng 
Keywords: Basel
Below-canopy survey
Simultaneous localization and mapping
Tree diameter estimation
UAV-mounted LiDAR
© 2021 by the authors. Licensee MDPI
Issue Date: 2-Jul-2021
Publisher: MDPI AG
Citation: Chisholm, Ryan A., Rodríguez-Ronderos, M. Elizabeth, Lin, Feng (2021-07-02). Estimating tree diameters from an autonomous below-canopy uav with mounted lidar. Remote Sensing 13 (13) : 2576. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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. © 2021, MDPI AG. All rights reserved.
Source Title: Remote Sensing
ISSN: 2072-4292
DOI: 10.3390/rs13132576
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

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