©2023 International Society of Arboriculture 88 Priestley S. 2017. An introduction to innovation in arboriculture. In: Williams G, editor. TREENET proceedings of the 18th national street tree symposium. 18th National Street Tree Symposium; 2017 September 7–8; Adelaide, South Australia. Adelaide (Australia): The University of Adelaide. p. 61-66. [Accessed 2022 April 30]. https://cdn.treenet.org/wp-content/ uploads/2021/10/TREENET-2017-Symposium-Proceedings _FINAL.pdf Ramalho de Oliveira LF, Lassiter HA, Wilkinson B, Whitley T, Ifju P, Logan SR, Peter GF, Vogel JG, Martin TA. 2021. Moving to automated tree inventory: Comparison of UAS- derived lidar and photogrammetric data with manual ground estimates. Remote Sensing. 13(1):72. https://doi.org/10.3390/ rs13010072 Scher CL, Griffoul E, Cannon CH. 2019. Drone-based photo- grammetry for the construction of high-resolution models of individual trees. Trees. 33:1385-1397. https://doi.org/10.1007/ s00468-019-01866-x Schmitt U, Lüer B, Dujesiefken D, Koch G. 2014. The Massaria disease of plane trees: Its wood decay mechanism. IAWA Jour- nal. 35(4):395-406. https://doi.org/10.1163/22941932-00000074 Seo J, Duque L, Wacker J. 2018. Drone-enabled bridge inspection methodology and application. Automation in Construction. 94:112-126. https://doi.org/10.1016/j.autcon.2018.06.006 Slater D. 2022. Branch junctions: A classification system for arborists. Stroud (United Kingdom): Arboricultural Association. AA Guidance Note 14. https://www.trees.org.uk/Book-Shop/ Products/GN14-Branch-Junctions-A-Classification-System -for-Arborists-en The UK Civil Aviation Authority. 2019. The Drone and Model Aircraft Code. West Sussex (United Kingdom): Civil Aviation Authority. [Updated 2022 December 1; Accessed 2022 April 30]. https://register-drones.caa.co.uk/drone-code/where -you-can-fly 3Dflow SRL. 2022. Version 8.6.0. Verona (Italy): 3Dflow SRL. [Updated 2022 March 25; Accessed 2022 April 30]. https:// www.3dflow.net/3df-zephyr-photogrammetry-software ACKNOWLEDGMENTS We thank Kerry Carpenter of Dorset Council for help with access to suitable trees for assessment, Alice Favre of Chettle Estate for access to a trial drone flying area, and Alison Turnock and Graham Cox for their support of the time needed to undertake this research. We also thank the three panellists used in this study: Andrew Osborne, Richard Salt, and Harry Doe of Poole Council. Rachel Warner assisted with proofreading an early draft of this paper. James Roberts Myerscough College Greenspace Department Bilsborrow, Lancashire, United Kingdom Duncan Slater (corresponding author) Myerscough College Greenspace Department Bilsborrow, Lancashire, United Kingdom
[email protected] EDF R&D. 2022. Los Altos (CA, USA): CloudCompare.org. [Updated 2022 March 12; Accessed 2022 April 30]. https:// www.danielgm.net/cc Ganz S, Käber Y, Adler P. 2019. Measuring tree height with remote sensing—A comparison of photogrammetric and LiDAR data with different field measurements. Forests. 10(8):694. https://doi.org/10.3390/f10080694 Gatziolis D, Lienard JF, Vogs A, Strigul NS. 2015. 3D tree dimensionality assessment using photogrammetry and small unmanned aerial vehicles. PLOS ONE. 10(9):e0137765. https://doi.org/10.1371/journal.pone.0137765 Hobart M, Pflanz M, Weltzien C, Schirrmann M. 2020. Growth height determination of tree walls for precise monitoring in apple fruit production using UAV photogrammetry. Remote Sensing. 12(10):1656. https://doi.org/10.3390/rs12101656 Koh LP, Wich SA. 2012. Dawn of drone ecology: Low-cost autonomous aerial vehicles for conservation. Tropical Con- servation Science. 5(2):121-132. https://doi.org/10.1177/ 194008291200500202 Krause S, Sanders TGM, Mund JP, Greve K. 2019. UAV-based photogrammetric tree height measurement for intensive forest monitoring. Remote Sensing. 11(7):758. https://doi.org/10.3390/ rs11070758 Lipwoni V, Watt MS, Hartley RJL, Leonardo EMC, Morgenroth J. 2022. A comparison of photogrammetric software for deriv- ing structure-from-motion 3D point clouds and estimating tree heights. New Zealand Journal of Forestry. 66(4):18-26. https://hdl.handle.net/10092/103888 Mokroš M, Liang X, Surový P, Valent P, Čerňava J, Chudý F, Tunák D, Saloň Š, Merganič J. 2018. Evaluation of close- range photogrammetry image collection methods for esti- mating tree diameters. ISPRS International Journal of Geo-Information. 7(3):93. https://doi.org/10.3390/ ijgi7030093 Moreira BM, Goyanes G, Pina P, Vassilev O, Heleno S. 2021. Assessment of the influence of survey design and processing choices on the accuracy of tree diameter at breast height (DBH) measurements using UAV-based photogrammetry. Drones. 5(2):43. https://doi.org/10.3390/drones5020043 Moyano J, Nieto-Julián JE, Bienvenido-Huertas D, Marín- García D. 2020. Validation of close-range photogrammetry for architectural and archaeological heritage: Analysis of point density and 3D mesh geometry. Remote Sensing. 12(21): 3571. https://doi.org/10.3390/rs12213571 Nezami S, Khoramshahi E, Nevalainen O, Pölönen I, Honkavaara E. 2020. Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural net- works. Remote Sensing. 12(7):1070. https://doi.org/10.3390/ rs12071070 Nitoslawski SA, Wong-Stevens K, Steenberg JWN, Wither- spoon K, Nesbitt L, Konijnendijk van den Bosch CC. 2021. The digital forest: Mapping a decade of knowledge on tech- nological applications for forest ecosystems. Earth’s Future. 9(8):e2021EF002123. https://doi.org/10.1029/2021EF002123 Pfeifer N, Falkner J, Bayr A, Eysn L, Ressl C. 2021. Test charts for evaluating imaging and point cloud quality of mobile mapping systems for urban street space acquisition. Remote Sensing. 13(2):237. https://doi.org/10.3390/rs13020237 Roberts and Slater: Miniature UAVs and Photogrammetry
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