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Detecting pruning of individual stems using Airborne Laser Scanning data captured from an Unmanned Aerial Vehicle

Citation

Wallace, L and Watson, C and Lucieer, A, Detecting pruning of individual stems using Airborne Laser Scanning data captured from an Unmanned Aerial Vehicle, International Journal of Applied Earth Observation and Geoinformation, 30 pp. 76-85. ISSN 0303-2434 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Elsevier B.V.

DOI: doi:10.1016/j.jag.2014.01.010

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.

Item Details

Item Type:Refereed Article
Keywords:UAV, LiDAR, pruning, change detection, laser scanning, unmanned aerial vehicle, forest management
Research Division:Engineering
Research Group:Geomatic Engineering
Research Field:Photogrammetry and Remote Sensing
Objective Division:Environment
Objective Group:Ecosystem Assessment and Management
Objective Field:Ecosystem Assessment and Management of Forest and Woodlands Environments
Author:Wallace, L (Dr Luke Wallace)
Author:Watson, C (Dr Christopher Watson)
Author:Lucieer, A (Associate Professor Arko Lucieer)
ID Code:93116
Year Published:2014
Web of Science® Times Cited:13
Deposited By:Geography and Environmental Studies
Deposited On:2014-07-14
Last Modified:2017-10-30
Downloads:0

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