the development and application of an unmanned aerial vehicle laser scanning system for forest ...

158 Pages·2014·14.23 MB·English

by  Wallace, Luke Oliver

The development and application of a unmanned aerial
vehicle laser scanning system for forest management
by
Luke Oliver Wallace
BSurvSpSc (Hons)
School of Land & Food
Submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy
University of Tasmania
April, 2014

This thesis contains no material which has been accepted for a degree or
diploma by the University or any other institution, except by way of back-
ground information and duly acknowledged in the thesis, and to the best
of my knowledge and belief no material previously published or written by
another person except where due acknowledgement is made in the text of
the thesis, nor does the thesis contain any material that infringes copyright.
Luke Wallace
2 April 2014
Statement of Co-Authorship
This thesis may be made available for loan and limited copying in accordance with the
Copyright Act 1968
Chapter 4 (cid:13)c 2014 IEEE. Reprinted, with permission, from Wallace L., Lucieer, A. and
Watson C. Evaluating tree detection and segmentation routines on very high resolution
UAV LiDAR data, IEEE Transactions On Geoscience And Remote Sensing, Accepted for
publication.
Chapter 5 (cid:13)c 2014 IEEE. Reprinted, with permission, from Wallace L., Musk R. and
Lucieer, A. An Assessment of the Repeatability of Automatic Forest Inventory Metrics
Derived From UAV-Borne Laser Scanning Data, IEEE Transactions On Geoscience And
Remote Sensing, Accepted for publication.
Luke Wallace
2 April 2014
Abstract
Airborne Laser scanning (ALS) has emerged as an important tool for providing cost-
effective characterisation of the 3D structure of forests over large areas. As data resolu-
tionisofteninverselyproportionaltocoveragearea,laserscanningfromalternativeplat-
formshasbeenarecentsubjectofinvestigation. Thisthesisadvancesthisexplorationby
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 multi-
rotor platform is described. A novel algorithm was developed for the direct georefer-
encing 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 prac-
tice, 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
RMSE).
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
than50mabovegroundlevelandscananglerestrictedto±30◦. Asurveymethodwithin
theserestraintsandutilisingoverlappingtransectswasdesignedtoprovidecost-effective
and repeatable observations of the 3D structure of the plot sized areas (500 m2). It was
foundthatthemaximumdeviationsofplotleveldescriptivestatisticscapturedinrepeat
multiple flights were less than 3%.
Investigatingtheaccuracyandrepeatabilityofindividualtreelevelmetricsderivedfrom
the high density UAVLS point clouds (up to 300 points/m2) using five different auto-
matic tree detection and delineation methods highlighted that increased data resolution
provided more detail in the characterisation of individual trees. The best performing
method,whichutilisedboththeCHMandthepointcloud,resultedin98%oftreesbeing
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%)
extractedfromthedelineatedtreesegmentswereobservedwithhigherrepeatabilityand
betterefficiencythanthatcurrentlyachievedusingmodernfieldtechniques. Subsequent
i
analysisofchangefollowingtheapplicationofsequentialsilviculturaltreatmentsshowed
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 reso-
lution, enabling the determination of highly accurate forest inventory metrics and their
change over time. In comparison with in situ field techniques, UAVLS offers more effi-
cient and detailed characterisation of the 3D structure of forests.
ii
Acknowledgements
Firstly, I thank my supervisors Dr Arko Lucieer, Dr Christopher Watson, Dr Robert
Musk and Dr Jon Osborn. I am grateful for the opportunity under the supervision of
people with both knowledge and passion for the subject area. Both Chris and Arko
provided guidance, knowledge, enthusiasm, patience and advice at all stages of the
preparationofthisthesis. AlthoughIprobablydidnotdrawonJonandRob’sknowledge
as much as I should have, they were always available when I required any guidance or
advice.
I also thank Darren Turner and Tony Veness. I am indebted to both for providing
the technical expertise in order to make this thesis fly (well the laser scanning system
anyway). I acknowledge Forestry Tasmania for providing field support and access to the
studyareasusedinthisproject. InparticularlyDavidMcElweewhoprovidedsupportin
thecollectionofallin-situfielddata. IwouldalsoliketoaknowledgetheWinifredViolet
Scott Trust for funding support. The Australian Antarctic Division is acknowledged for
providing workshop time in constructing the sensor mount.
Thank you to my parents Stephen and Valerie, the support and privileges that you have
given me until this day can not be stressed enough as without them the opportunities
I had leading up to and undertaking this project would not have be possible. Thanks
also to my other family members, friends and colleagues.
Finally, I thank my wife Daisy for everything. You have supported and encouraged me
through all stages of my Ph.D, both good and bad. I feel greatly privileged to have
you as both a friend and a partner and I look forward to whatever is next in our lives
together.
iii
Table of Contents
Abstract i
Acknowledgements iii
Table of Contents iv
List of Tables viii
List of Figures x
1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Silviculture of Eucalyptus forests . . . . . . . . . . . . . . . . . . 1
1.1.2 Remote sensing of forested environments . . . . . . . . . . . . . . 2
1.1.3 UAV remote sensing of forested environments . . . . . . . . . . . 4
1.2 Problems and objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Thesis structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Error assessment and mitigation for hyper-temporal UAV laser scan-
ner surveys of forest inventory 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.1 Laser scanning: state of the art forest measurement . . . . . . . 12
2.1.2 Mini UAV Laser Scanning technology . . . . . . . . . . . . . . . 13
2.1.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Airborne laser scanner error propagation. . . . . . . . . . . . . . 16
2.2.3 Structure from Motion . . . . . . . . . . . . . . . . . . . . . . . 19
2.2.4 UAVLS / ALS comparison . . . . . . . . . . . . . . . . . . . . . 21
2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.1 UAVLS error constraining strategies . . . . . . . . . . . . . . . . 22
2.3.2 The effect of flying height . . . . . . . . . . . . . . . . . . . . . . 25
2.3.3 ALS/UAVLS Comparison . . . . . . . . . . . . . . . . . . . . . . 25
2.3.4 Sensor Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . 27
iv
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5 Thesis Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3 Development of a UAV-laser scanning system with application to for-
est inventory 29
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.1 UAVLS workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.2 Trajectory determination . . . . . . . . . . . . . . . . . . . . . . 36
3.3.3 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.3.4 Point cloud generation and accuracy assessment . . . . . . . . . 42
3.3.5 Assessing flying parameters for data collection . . . . . . . . . . 43
3.4 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4.1 Trajectory generation . . . . . . . . . . . . . . . . . . . . . . . . 45
3.4.2 Point cloud properties . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4.3 Point cloud accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.4.4 Survey constraints for inventory capture . . . . . . . . . . . . . . 51
3.5 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . 54
3.6 Thesis context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4 Evaluating tree detection and segmentation routines on very high res-
olution UAV laser scanning data 56
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Study area and data collection . . . . . . . . . . . . . . . . . . . . . . . 59
4.3 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3.1 Point cloud pre-processing . . . . . . . . . . . . . . . . . . . . . 60
4.3.2 Individual tree detection algorithms . . . . . . . . . . . . . . . . 62
4.3.3 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . 65
4.4 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.1 Tree detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.4.2 Tree location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4.3 Crown width . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.7 Thesis context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
v