{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T15:11:02Z","timestamp":1771168262904,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 (&gt;300 mm diameter) within 10 m of the UAV flight path, for medium trees (200\u2013300 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.<\/jats:p>","DOI":"10.3390\/rs13132576","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T03:19:32Z","timestamp":1625195972000},"page":"2576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR"],"prefix":"10.3390","volume":"13","author":[{"given":"Ryan","family":"Chisholm","sequence":"first","affiliation":[{"name":"Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543, Singapore"}]},{"given":"M.","family":"Rodr\u00edguez-Ronderos","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore 117543, Singapore"},{"name":"Yale-NUS College, 16 College Avenue West, Singapore 138527, Singapore"}]},{"given":"Feng","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore"},{"name":"Peng Cheng Laboratory, 2 Xingke Road, Nanshan, Shenzhen 518066, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. 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