Detecting pruning of individual stems using Airborne Laser Scanning data captured from an Unmanned Aerial Vehicle |
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| Institution: | 1. CREA Research Centre for Engineering and Agro-Food Processing, Monterotondo, RM, Italy;2. CIRCE Research Centre for Energy Resources and Consumption, Zaragoza, Spain;1. School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476V, Melbourne, VIC 3001, Australia;2. Cooperative Research Centre for Spatial Information, Carlton, VIC 3053, Australia;3. Department of Geography and NERC National Centre for Earth Observation, University College London, Gower Street, London WC1E 6BT, UK;4. Remote Sensing Centre, Science Delivery, Department of Science, Information Technology, and Innovation, 41 Boggo Road, QLD 4102, Australia;5. Joint Remote Sensing Research Program, School of Geography, Planning and Environmental Management, University of Queensland, St Lucia, QLD 4072, Australia;6. Department of Earth System Science and Policy, University of North Dakota, Clifford Hall, 4149 University Drive, Grand Forks, ND 58202, USA;7. ITC, University of Twente, PO Box 217, NL-7000 AE Enschede, The Netherlands;8. Department of Environment, Land, Water and Planning, East Melbourne, VIC 3002, Australia;1. School of Engineering, Newcastle University, Newcastle, NE1 7RU, United Kingdom;2. Environmental Change Institute, University of Oxford, South Parks Road, Oxford OX1 3QY, United Kingdom;1. Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2, FI-00791 Helsinki, Finland;2. Laboratory of Mathematics, Tampere University of Technology, P.O. Box 527, FI-33101 Tampere, Finland;3. Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 68, FI-80101 Joensuu, Finland;1. School of Science, RMIT University, Melbourne, Victoria, 3000, Australia;2. Universidad de Chile, Forestry and Nature Conservation Faculty, GEP, Santa Rosa, 11315, Santiago de Chile, Chile;3. School of Geography, Planning and Spatial Science, University of Tasmania, Hobart, Tasmania, Australia, 7001;4. Centre for Urban Research, School of Global, Urban and Social Studies, RMIT University, Melbourne, VIC, Australia;5. Natural Hazards Research Australia, East Melbourne, VIC, Australia;1. Departamento de Ingeniería, Universidad Nacional del Sur (UNS), Av. Alem 1253, Bahía Blanca B8000CPB, Argentina;2. INMABB, Universidad Nacional del Sur (UNS)-CONICET, Av. Alem 1253, Bahía Blanca B8000CPB, Argentina;3. Universidad de la República, Ruta 5, km 386, 5, Tacuarembó C.P. 45000, Uruguay;4. UNaM CONICET, Facultad de Ciencias Forestales Bertoni 124, Eldorado N3382GDD, Misiones, Argentina |
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| Abstract: | Modern forest management involves implementing optimal pruning regimes. These regimes aim to achieve the highest quality timber in the shortest possible rotation period. Although a valuable addition to forest management activities, tracking the application of these treatments in the field to ensure best practice management is not economically viable. This paper describes the use of Airborne Laser Scanner (ALS) data to track the rate of pruning in a Eucalyptus globulus stand. Data is obtained from an Unmanned Aerial Vehicle (UAV) and we describe automated processing routines that provide a cost-effective alternative to field sampling. We manually prune a 500 m2 plot to 2.5 m above the ground at rates of between 160 and 660 stems/ha. Utilising the high density ALS data, we first derived crown base height (CBH) with an RMSE of 0.60 m at each stage of pruning. Variability in the measurement of CBH resulted in both false positive (mean rate of 11%) and false negative detection (3.5%), however, detected rates of pruning of between 96% and 125% of the actual rate of pruning were achieved. The successful automated detection of pruning within this study highlights the suitability of UAV laser scanning as a cost-effective tool for monitoring forest management activities. |
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| Keywords: | Laser Scanning Unmanned Aerial Vehicle Forest management Pruning Change detection |
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