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Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data


Wallace, L and Lucieer, A and Watson, CS, Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data, IEEE Transactions on Geoscience and Remote Sensing, 52, (12) pp. 7619-7628. ISSN 0196-2892 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 IEEE

DOI: doi:10.1109/TGRS.2014.2315649


Light detection and Ranging (LiDAR) is becoming an increasingly used tool to support decision-making processes within forest operations. Area-based methods that derive information on the condition of a forest based on the distribution of points within the canopy have been proven to produce reliable and consistent results. Individual tree-based methods, however, are not yet used operationally in the industry. This is due to problems in detecting and delineating individual trees under varying forest conditions resulting in an underestimation of the stem count and biases toward larger trees. The aim of this paper is to use high-resolution LiDAR data captured from a small multirotor unmanned aerial vehicle platform to determine the influence of the detection algorithm and point density on the accuracy of tree detection and delineation. The study was conducted in a four-year-old Eucalyptus globulus stand representing an important stage of growth for forest management decision-making process. Five different tree detection routines were implemented, which delineate trees directly from the point cloud, voxel space, and the canopy height model (CHM). The results suggest that both algorithm and point density are important considerations in the accuracy of the detection and delineation of individual trees. The best performing method that utilized both the CHM and the original point cloud was able to correctly detect 98% of the trees in the study area. Increases in point density (from 5 to 50 points/m2) lead to significant improvements (of up to 8%) in the rate of omission for algorithms that made use of the high density of the data.

Item Details

Item Type:Refereed Article
Keywords:UAV, LiDAR, tree detection, forestry, lasers, remote sensing, remotely piloted aircraft
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Management
Objective Group:Fresh, ground and surface water systems and management
Objective Field:Assessment and management of freshwater ecosystems
UTAS Author:Wallace, L (Dr Luke Wallace)
UTAS Author:Lucieer, A (Professor Arko Lucieer)
UTAS Author:Watson, CS (Dr Christopher Watson)
ID Code:93118
Year Published:2014
Web of Science® Times Cited:152
Deposited By:Geography and Environmental Studies
Deposited On:2014-07-14
Last Modified:2017-10-24

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