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Development and application of a unmanned aerial vehicle laser scanning system for forest management


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Wallace, LO (2014) Development and application of a unmanned aerial vehicle laser scanning system for forest management. PhD thesis, University of Tasmania.

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Airborne Laser scanning (ALS) has emerged as an important tool for providing costeffective
characterisation of the 3D structure of forests over large areas. As data resolution
is often inversely proportional to coverage area, laser scanning from alternative platforms
has been a recent subject of investigation. This thesis advances this exploration by
investigating the use of Unmanned Aerial Vehicles (UAVs) as a laser scanning platform
(UAVLS) for forest inventory purposes. The design of a small laser scanning system
consisting of an automotive laser scanner, a Micro-Electro-Mechanical Systems based
Inertial Measurement Unit (IMU), a dual frequency Global Positioning System (GPS)
receiver and a downward pointing video camera for use on-board an Oktokopter multirotor
platform is described. A novel algorithm was developed for the direct georeferencing
of laser returns utilising a vision aided GPS-IMU sigma-point Kalman smoother.
Evaluating improvements due to the inclusion of vision, both stochastically and in practice,
it is demonstrated that an accuracy similar to modern ALS systems and adequate
for forest inventory measurements can be achieved (34 cm horizontal, 14 cm vertical
Two 4 year old Eucalyptus plantations in south east Tasmania were selected as the
primary study area in order to assess the utility of the UAVLS system to map and
assess change in key inventory metrics. Analysis of the point clouds captured with
different flying parameters indicated that the flying height should be restricted to less
than 50 m above ground level and scan angle restricted to ±30. A survey method within
these restraints and utilising overlapping transects was designed to provide cost-effective
and repeatable observations of the 3D structure of the plot sized areas (500 m2). It was
found that the maximum deviations of plot level descriptive statistics captured in repeat
multiple flights were less than 3%.
Investigating the accuracy and repeatability of individual tree level metrics derived from
the high density UAVLS point clouds (up to 300 points/m2) using five different automatic
tree detection and delineation methods highlighted that increased data resolution
provided more detail in the characterisation of individual trees. The best performing
method, which utilised both the CHM and the point cloud, resulted in 98% of trees being
repeatedly and correctly delineated from the point cloud. Tree height (absolute mean
deviation of 0.35 m), location (0.48 m), crown area (3.3 m2) and canopy closure (2.3%)
extracted from the delineated tree segments were observed with higher repeatability and
better efficiency than that currently achieved using modern field techniques. Subsequent analysis of change following the application of sequential silvicultural treatments showed
that UAVLS is capable of detecting pruning rates of between 96 and 125% of the true
pruning rate.
This thesis demonstrates that UAVLS offers unprecedented temporal and spatial resolution,
enabling the determination of highly accurate forest inventory metrics and their
change over time. In comparison with in situ field techniques, UAVLS offers more efficient
and detailed characterisation of the 3D structure of forests.

Item Type: Thesis (PhD)
Keywords: unmanned aerial vehicle, laser scanning, forestry, remote sensing, change detection, high resolution
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Date Deposited: 12 Nov 2014 03:00
Last Modified: 11 Mar 2016 05:52
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