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Evaluating tree detection and segmentation routines on very high resolution UAV LiDAR data
Citation
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
Abstract
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 |
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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: | 111 |
Deposited By: | Geography and Environmental Studies |
Deposited On: | 2014-07-14 |
Last Modified: | 2017-10-24 |
Downloads: | 0 |
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