Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Asghar, Umair
Master of Applied Science - MASc
University of British Columbia
In recent years, unmanned aerial vehicles (UAVs) equipped with digital cameras have emerged as an inexpensive alternative to light detection and ranging (LiDAR) for mapping landslides. However, mapping with UAVs typically requires a ground control point (GCP) network to achieve higher mapping accuracies. Complex natural environments often limit the number as well as the proper distribution of GCPs. In the first part of this study, aerial imagery acquired with a quadrotor UAV was processed using structure from motion (SfM) technique to produce a three-dimensional point cloud of a large landslide involving multiple steep slopes and dense tree cover. The resulting point cloud was georeferenced with six different configurations of GCPs measured with a real-time kinetic GNSS receiver to test the influence of the number and the distribution of GCPs on mapping accuracies. Horizontal and vertical mapping accuracies of 0.058 m and 0.044 m, respectively, were achieved for the most accurate GCP configuration. A separate point cloud comparison was performed on the georeferenced point clouds to assess the effect of varying topography and tree cover on mapping accuracy. The 3D change in the natural terrain measured over a 1-year period from July 2016 to July 2017 showed movements ranging from ±0.4 m to over ±1 m at the toe of the landslide. Other parts of the landslide either remained inactive or moved less than 0.1 m. The second part of this thesis involved an accuracy comparison of five different opensource algorithms, originally developed for LiDAR data, for classification of the UAV-SfM point clouds. The influences of terrain slope, vegetation and point densities, and difficult-to-filter features on classification accuracy were also evaluated. CSF and MCC algorithms produced the lowest overall errors (4%) closely followed by LASground and FUSION (5%). All algorithms suffered in areas with densely vegetated steep slopes, understory vegetation, low point density, and low objects. Although any of the tested algorithms along with careful selection of input parameters can be used to accurately classify UAV-SfM point clouds, CSF is recommended as it is computationally efficient, does not require any preprocessing, and can process very large point clouds (>50 million points).
LANDSLIDE MAPPING FROM ANALYSIS OF UAV-SFM POINT CLOUDS by Umair Asghar B.Sc.E, University of Engineering and Technology, Peshawar, 2010 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) November 2017 © Umair Asghar, 2017 ii The undersigned certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: LANDSLIDE MAPPING FROM ANALYSIS OF UAV-SFM POINT CLOUDS submitted by Umair Asghar in partial fulfilment of the requirements of the degree of Master of Applied Science . Dr. Dwayne D. Tannant, Faculty of Applied Science / School of Engineering Supervisor, Professor Dr. Sumi Suddiqua, Faculty of Applied Science / School of Engineering Supervisory Committee Member, Professor Dr. Zheng Liu, Faculty of Applied Science / School of Engineering Supervisory Committee Member, Professor Dr. Richard Klukas, Faculty of Applied Science / School of Engineering University Examiner, Professor iii Abstract In recent years, unmanned aerial vehicles (UAVs) equipped with digital cameras have emerged as an inexpensive alternative to light detection and ranging (LiDAR) for mapping landslides. However, mapping with UAVs typically requires a ground control point (GCP) network to achieve higher mapping accuracies. Complex natural environments often limit the number as well as the proper distribution of GCPs. In the first part of this study, aerial imagery acquired with a quadrotor UAV was processed using structure from motion (SfM) technique to produce a three-dimensional point cloud of a large landslide involving multiple steep slopes and dense tree cover. The resulting point cloud was georeferenced with six different configurations of GCPs measured with a real-time kinetic GNSS receiver to test the influence of the number and the distribution of GCPs on mapping accuracies. Horizontal and vertical mapping accuracies of 0.058 m and 0.044 m, respectively, were achieved for the most accurate GCP configuration. A separate point cloud comparison was performed on the georeferenced point clouds to assess the effect of varying topography and tree cover on mapping accuracy. The 3D change in the natural terrain measured over a 1-year period from July 2016 to July 2017 showed movements ranging from ±0.4 m to over ±1 m at the toe of the landslide. Other parts of the landslide either remained inactive or moved less than 0.1 m. The second part of this thesis involved an accuracy comparison of five different opensource algorithms, originally developed for LiDAR data, for classification of the UAV-SfM point clouds. The influences of terrain slope, vegetation and point densities, and difficult-to-filter features on classification accuracy were also evaluated. CSF and MCC algorithms produced the lowest overall iv errors (4%) closely followed by LASground and FUSION (5%). All algorithms suffered in areas with densely vegetated steep slopes, understory vegetation, low point density, and low objects. Although any of the tested algorithms along with careful selection of input parameters can be used to accurately classify UAV-SfM point clouds, CSF is recommended as it is computationally efficient, does not require any preprocessing, and can process very large point clouds (>50 million points). v Preface This research has been conducted in the form of the thesis by the author, Umair Asghar, under the supervision of Dr. Dwayne Tannant. The research idea was presented by Dr. Tannant. The author devised the field data acquisition plan, performed field geodetic surveys, as well as, assisted Dr. Tannant in acquiring aerial imagery using the UAV. The data was processed and analyzed by the author under the guidance and supervision of Dr. Tannant. The following publications are expected from this thesis: Asghar, U and Tannant, D (2017), “UAV-SfM based mapping of a large landslide in Western Canada using low-cost unmanned aerial vehicle (UAV)” (based on Chapters 1, 2, 3, and 4 of the thesis). Asghar, U and Tannant, D (2017), “Evaluation of open-source LiDAR filtering algorithms for classification of a UAV-SfM based point cloud of a complex environment” (based on Chapters 1, 2, 3, and 5 of the thesis). vi Table of Contents Abstract ......................................................................................................................................... iii Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Symbols ........................................................................................................................... xiv List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Background ..................................................................................................................... 1 1.2 Research statement.......................................................................................................... 4 1.2.1 Accuracy of UAV-SFM based mapping ..................................................................... 5 1.2.2 Classification of UAV-SfM based point clouds ......................................................... 7 1.3 Study site ....................................................................................................................... 11 1.4 Research objectives ....................................................................................................... 14 Chapter 2: Literature Review .....................................................................................................16 2.1 Structure from motion (SfM) photogrammetry ............................................................ 16 2.2 SfM Workflow .............................................................................................................. 17 2.2.1 Keypoint extraction ................................................................................................... 17 2.2.2 Bundle adjustment and sparse 3D point cloud generation ........................................ 18 2.2.3 Dense matching ......................................................................................................... 19 vii 2.2.4 Post-processing ......................................................................................................... 19 2.3 Suitability of UAV-SfM for mapping and monitoring purposes .................................. 20 2.4 Existing point cloud classification algorithms .............................................................. 23 2.4.1 Slope-based methods ................................................................................................ 23 2.4.2 Morphology-based methods...................................................................................... 24 2.4.3 Surface-based methods ............................................................................................. 25 Chapter 3: Resources and Methodology ....................................................................................27 3.1 UAV specifications and aerial image acquisition ......................................................... 27 3.2 Field geodetic survey .................................................................................................... 30 3.3 Image processing and three-dimensional point cloud generation ................................. 32 3.4 Ground control network configuration ......................................................................... 38 3.5 Point cloud classification and bare earth extraction ..................................................... 41 3.5.1 CANUPO - scale dependent dimensionality............................................................. 41 3.5.2 Cloth simulation filter ............................................................................................... 44 3.5.3 LASground -LAStools .............................................................................................. 45 3.5.4 MCC-LiDAR ............................................................................................................ 47 3.5.5 FUSION .................................................................................................................... 48 3.6 Classification accuracy assessment............................................................................... 49 Chapter 4: Results and Discussion – Part-I: Mapping Accuracy Assessment .......................52 4.1 Accuracy assessment .................................................................................................... 52 4.2 Effect of topography and vegetation on UAV mapping ............................................... 60 4.3 Change detection ........................................................................................................... 71 Chapter 5: Results and Discussion – Part-II: Classification Accuracy Assessment ..............77 viii 5.1 Qualitative evaluation ................................................................................................... 77 5.2 Quantitative evaluation ................................................................................................. 83 5.2.1 Terrain slope ............................................................................................................. 85 5.2.2 Vegetation density .................................................................................................... 88 5.2.3 Point cloud density .................................................................................................... 90 5.2.4 Difficult-to-filter features.......................................................................................... 91 5.3 Guidelines for input parameters .................................................................................... 94 5.4 Manual input, computing time and resources ............................................................... 97 Chapter 6: Conclusions and Recommendations .......................................................................99 6.1 Accuracy of UAV-SFM based mapping ....................................................................... 99 6.2 Classification of UAV-SfM based point clouds ......................................................... 104 Bibliography ...............................................................................................................................107 Appendices ..................................................................................................................................117 Appendix A Calculations for mapping accuracy assessment ................................................. 118 A.1 Root mean square errors (RMSEs) for GCP Configuration - A ............................. 118 A.2 Root mean square errors (RMSEs) for GCP Configuration – B ............................. 119 A.3 Root mean square errors (RMSEs) for GCP Configuration - C ............................. 120 A.4 Root mean square errors (RMSEs) for GCP Configuration - D ............................. 121 A.5 Root mean square errors (RMSEs) for GCP Configuration - E.............................. 122 A.6 Root mean square errors (RMSEs) for GCP Configuration - F .............................. 123 Appendix B Calculations for classification accuracy assessment .......................................... 124 B.1 Effect of terrain slope on filter performances ......................................................... 124 B.2 Effect of vegetation density on filter performances ................................................ 125 ix B.3 Effect of point cloud density on filter performances .............................................. 126 B.4 Effect of difficult-to-filter features on filter performances ..................................... 127 x List of Tables Table 1: Summary of the six ground control points (GCPs) configurations used for georeferencing the UAV-SfM point cloud................................................................. 39 Table 2: Root mean square error (RMSE) for the six GCP configurations used for georeferencing the UAV-SfM point cloud................................................................. 56 Table 3: Vertical error (∆Z) in RTK-GNSS measured and DSM computed positions of the GCPs/CPs, and the corresponding RMSE values ................................................ 59 Table 4: Qualitative performance of the tested filtering algorithms ........................................ 78 Table 5: Quantitative comparison of the performances of the tested algorithms for the classification of UAV-SfM point cloud ..................................................................... 84 Table 6: Computation times taken by the filters for the classification of UAV-SfM point cloud ........................................................................................................................... 98 xi List of Figures Figure 1: Location of the study site. Coordinate system: WGS 84 / UTM Zone 11N. .............. 13 Figure 2: UAV used in this study: DJI Phantom 3 Advanced. ................................................... 29 Figure 3: UAV flight grids and camera positions for July 2016 survey. Grids I, II, and III represent the UAV flights made at 70 m, and Grid IV represents the UAV flight made at 60 m above take off points. ........................................................................... 30 Figure 4: Black plywood with white retro-reflective circular targets, centered on top of ground control points (GCPs). .................................................................................... 31 Figure 5: July 2016 UAV-SfM point cloud for the Perpetual Landslide. .................................. 35 Figure 6: July 2017 UAV-SfM point cloud for the Perpetual Landslide. .................................. 36 Figure 7: (a) Orthomosaic and (b) Digital Surface Model (DSM) generated from the July 2016 UAV-SfM point cloud. ...................................................................................... 37 Figure 8: Ground control points (GCPs) configurations used for georeferencing the UAV-SfM point cloud. ......................................................................................................... 40 Figure 9: Effect of varying core points sampling distance (dx) on the accuracy of M3C2 distances (in meters) between the reference point cloud (PC-A) and the point cloud generated using GCP configuration E (PC-E): Core points obtained by subsampling PC-A at (a) dx = 0.8 m; (b) dx = 0.5 m; (c) dx = 0.3 m (d) dx = 0.2 m; and (e) dx = 0.1 m. The extent, as well as the distribution, of distances are same for dx = 0.3, 0.2, and 0.1 m................................................................................ 62 Figure 10: M3C2 distances (in meters) between the reference point cloud (PC-A) and (a) the point cloud georeferenced with GCP configuration B (PC-B); (b) the point cloud georeferenced with GCP configuration C (PC-C); (c) the point cloud georeferenced with GCP configuration D (PC-D); (d) the point cloud xii georeferenced with GCP configuration E (PC-E); and (e) the point cloud georeferenced with GCP configuration F (PC-F). Plots on the right side of scale bars represent the distribution of distances. ................................................................ 67 Figure 11: M3C2 distance uncertainties at 95% confidence interval for the distance comparison between the reference point cloud (PC-A) and (a) the point cloud georeferenced with GCP configuration B (PC-B); (b) the point cloud georeferenced with GCP configuration C (PC-C); (c) the point cloud georeferenced with GCP configuration D (PC-D); (d) the point cloud georeferenced with GCP configuration E (PC-E); and (e) the point cloud georeferenced with GCP configuration F (PC-F). ...................................................... 68 Figure 12: The M3C2 distance map indicating the 3D movement between July 2016 and July 2017 point clouds. The circular dots in pink colour are the fixed ground control points (GCPs). Also, shown are the magnitude and the direction of the displacement vectors at each GCP computed from the 2016 and 2017 RTK-GNSS measurements. All dimensions are in meters. ................................................. 74 Figure 13: Profile of the section A-A’ in Figure 12. Zoomed overview of the encircled part of the profile is shown in Figure 14. ........................................................................... 75 Figure 14: Zoomed overview of the encircled part of the profile A-A’ in Figure 13. .................. 76 Figure 15: Top: (a) Reference BE point cloud and Type-II (red) errors caused by (b) CANUPO, (c) CSF, (d) LASground, (e) MCC-LiDAR and (f) FUSION. Bottom: (g) Reference OBJ point cloud and Type-I (green) errors caused by (h) CANUPO, (i) CSF, (j) LASground, (k) MCC-LiDAR and (l) FUSION. Numerical symbols 1-5 and 6-8 point to Type-II and Type-I errors, respectively, caused by different features. ....................................................................................... 79 Figure 16: Hill shades of digital elevation models interpolated from bare-earth point clouds for (a) reference bare-earth, (b) CANUPO, (c) CSF, (d) LASground, (e) MCC-LiDAR and (f) FUSION. Green and red rectangles highlight areas with Type-I xiii and Type II errors, respectively. Blue rectangles and their corresponding zoomed overviews show the difficulty experienced by all five filtering algorithms for a densely vegetated steep slope where few ground points captured under trees resulted in increased granularity (Type-II error). ....................................................... 80 Figure 17: Effect of variation in terrain slope on performances of the filtering algorithms ......... 87 Figure 18: Effect of vegetation density on the performances of the filtering algorithms in flat to undulating (slope <15°) and steep (slope >15°) areas. ........................................... 89 Figure 19: Effect of point cloud density (i.e. spacing between points) on filter performances. ..................................................................................................................................... 91 Figure 20: Effect of difficult-to-filter features on filter performances. ........................................ 94 xiv List of Symbols a First parameter that defines the slope of the weight function in FUSION algorithm A The number of ground points wrongly classified as non-ground points B The number of non-ground points incorrectly classified as ground points b Second parameter that defines the slope of the weight function in FUSION algorithm ba Balanced accuracy g Shift value to determine which points should be assigned the maximum weight value of 1.0 in FUSION algorithm K Cohen’s Kappa index s Scale parameter in MCC-LiDAR algorithm fdr Fisher discriminant ratio t Curvature parameter in MCC-LiDAR algorithm w Above-ground offset that defines an upper limit for points to have an influence on the interpolated surface FUSION algorithm xv List of Abbreviations 1D One dimensional 2D Two dimensional 3D Three dimensional BE Bare-earth CSF Cloth simulation filter DEM Digital elevation model DSM Digital surface model DTM Digital terrain model GCP Ground control point GNSS Global navigation satellite system GPS Global positioning system InSAR Interferometric synthetic aperture radar LiDAR Light detection and ranging MCC Multiscale curvature classification MVS Multiple view stereo NDVI Normalized difference vegetation index OBJ Object PTD Progressive triangular irregular network densification RTK Real-time kinetic SfM Structure from motion TIN Triangular irregular network TLS Terrestrial laser scanning xvi TPS Thin plate spline UAV Unmanned aerial vehicle xvii Acknowledgements I would like to express my profound gratitude to my supervisor, Dr. Dwayne Tannant, for his immeasurable guidance, support, inspiration, and motivation throughout my study at UBC. Thank you for your time, energy, understanding, and support through the most difficult moments of my life. I’d also like to recognize the members of my supervisory committee, Dr. Sumi Siddiqua and Dr. Zheng Liu, for their assistance, valuable time, and guidance in our progress meetings. I am deeply indebted to Dr. Lukas Bichler for his time, counseling, and support at my low times during this degree. I am also grateful to Ms. Shannon Hohl, for guiding me through many difficult situations. I am very thankful to my fellow colleagues including Wenbo Zheng, Sathika Witharana, Kai Zhang, and Josephine Morgenroth for their assistance in field work. My utmost thanks go to Wenbo Zheng for his guidance throughout my studies. I am grateful to Mr. Saaqib Mehmood, Dr. Tahira Jabeen, Dr. Muhammad Sarfaraz, Dr. Muhammad Ali, and Mr. Waqas Mohiuddin for their support throughout my studies. I sincerely thank my mother and my siblings for all their prayers and support. Most importantly, I pay my heartfelt gratitude to The Almighty Allah for giving me the strength and the ability to complete this research. xviii Dedication DEDICATED TO MY PARENTS 1 Chapter 1: Introduction 1.1 Background Landslides are a worldwide geological phenomenon and a substantial geohazard in most mountainous terrains (Schuster, 1996; Guzzetti, 2000; Bevere et al., 2011). Approximately nine percent of the world’s natural disasters during the past three decades were caused by landslides (Al-Rawabdeh et al., 2016). Mechanisms that trigger landslides may be entirely natural (e.g., earthquakes, prolonged or intense short-term rainfall, ground freeze/thaw cycles, etc.), entirely artificial (e.g., activities like blasting, excavation, removal of vegetation), or both. (Nadim et al., 2006; Rogers et al., 2010). While artificial triggering mechanism can be fully controlled, uncertainty in the occurrence of natural triggers makes landslides very difficult to predict. As a consequence, landslides caused by extreme natural events result in substantial economic losses (infrastructure and property damages) and a significant number of human causalities. There is a wide variation in spatial and temporal extents of naturally triggered landslides (Dikau, 1996; Al-Rawabdeh et al., 2016); however, most landslides are initially small in extent and provide some indication of movement before getting broadened over time, or under repetitive triggers (Dikau, 1996; Ji-xian, 2005). Rapid identification and monitoring of such initially small landslides are vital for assessing landslide hazard, planning and implementing proper mitigation measures, predicting and monitoring future landslides, and making informed decisions (Ji-xian, 2005; Stumpf et al., 2013). Proper characterization, hazard assessment, and monitoring of a landslide require its surface topographic information (surface and elevation models) as well as an accurate assessment of its 2 dynamics (amount, extent, and rate of movement) (Dikau, 1996). Remote sensing techniques such as satellite and aerial photogrammetry, interferometric synthetic aperture radar (InSAR), terrestrial laser scanning (TLS), and airborne light detection and ranging (LiDAR) have been successfully used in the past for topographic mapping, and geohazard characterization and assessment (Kääb, 2002; Mora et al., 2003; Bitelli et al., 2004; Metternicht et al., 2005; Guzzetti et al., 2012; Jaboyedoff et al., 2012). These techniques together with semi-automated/automated computer programs allow high-frequency data collection and generation of three-dimensional (3D) dense point clouds (Ackermann, 1999; Jaboyedoff et al., 2012; Pirotti et al., 2013). However, satellite-based observations usually have limited resolution (Immerzeel et al., 2014), and are cost-prohibitive for individual landslides, whereas TLS can suffer from range and line of sight issues where oblique incidence angles and truncated data can cause occlusions (Bitelli et al., 2004; Eisenbeiß, 2009). Airborne imagery and LiDAR surveys are precise and provide a detailed top-view, but the extremely high cost attached to the equipment, the use of aircraft/helicopter and their time-consuming nature (delays associated with deployment and bad weather conditions), make these unfeasible for small-scale landslides. (Bitelli et al., 2004; Jaboyedoff et al., 2012). Synthetic Aperture Radar Interferometry (InSAR), both satellite and terrestrial, can sense ground movements with millimeter to centimeter accuracy dependent on topographic and atmospheric noise (Crosetto et al., 2002; Colesanti and Wasowski, 2006; Singhroy et al., 2007). However, InSAR is too expensive and uncommonly used in ordinary practice (Monserrat et al., 2014; Peternel et al., 2017). 3 A rather inexpensive and rapid landslide mapping and monitoring approach with little human intervention is the use of low-cost unmanned aerial vehicles (UAVs) equipped with consumer-grade digital cameras and navigation systems. Aerial imagery acquired by a UAV can be georeferenced and photogrammetrically processed using various software packages to produce highly accurate 3D dense point clouds from which high-quality digital surface models (DSMs) and digital elevation models (DEMs) can be extracted. Landslides can be identified and analyzed by comparing multi-temporal orthomosaics and DEMs (Niethammer et al., 2012; Turner et al., 2015; Peppa et al., 2016). Use of UAVs for mapping purposes is not new, but recent technological advances, such as availability of low-cost quality cameras, compact automated navigation and orientation systems, and an increase in UAV payload capacity by the use of lightweight carbon-fiber frames, allow high frequency (daily/weekly/monthly) and high-resolution data (1-10 cm) data collection without using expensive mapping-grade data acquisition systems (Lucieer et al., 2014; Al-Rawabdeh et al., 2016). UAVs also have a number of advantages over traditional aerial platforms (aircraft and helicopter) including improved work safety, operational flexibility, rapid deployment, flight repeatability, low operational costs, and fewer weather-related flying limitations. (Niethammer et al., 2012; Tannant, 2015). Because of their ability to stand still in air multi-rotor UAVs can easily map locations that are inaccessible from the ground or through a manned aircraft (e.g., steep rock faces and narrow valleys with dense tree cover). Additionally, compared to manned aircraft, a UAV can fly at slower speeds and lower altitudes allowing high image resolution and a significant reduction in blur and other motion and vibration related errors in images (Tannant, 2015; Al-Rawabdeh et al., 2016). 4 More recently, development of specialized UAV image processing software (e.g., Pix4D Mapper, Photoscan, Photomodeler, LPS, Autodesk 123D) incorporating Structure from Motion (SfM) and Multiple View Stereopsis (MVS) algorithms have significantly reduced manual intervention in the processing of UAV acquired imagery. Compared to traditional photogrammetric techniques, UAV-SFM-MVS (or UAV-SfM) workflow is fast and is capable of providing high-quality, very dense 3D point clouds. SfM, initially developed for computer vision, is an advanced photogrammetric algorithm that has the ability to compute camera pose/orientation and 3D scene geometry without internal or external control points (Snavely et al., 2008; Westoby et al., 2012). This is particularly useful in case of UAVs, which because of their lightweight, less accurate positioning (GNSS) and orientation (IMU) systems, and use of consumer-grade (non-metric) cameras, produce varied image geometry and overlap (Lucieer et al., 2014; Hugenholtz et al., 2016). SfM uses a Scale Invariant Feature Transform (SIFT), which is a feature identification scheme (Lowe, 2004) that recognizes matching features (keypoints) in overlapping images. An iterative, highly redundant bundle block adjustment (BBA) is then performed on the key points to produce a sparse 3D point cloud, which can be further densified by MVS (Furukawa and Ponce, 2010; Westoby et al., 2012). 1.2 Research statement The purpose of this research was to analyze two separate issues related to the UAV-SfM based mapping of landslides. The first issue is related to the accuracy of UAV-SfM technique for mapping and the second is associated with the post-processing of the data for further analyses. These issues have been stated in the following subsections. 5 1.2.1 Accuracy of UAV-SFM based mapping The accuracy of UAV-based mapping is important for its applicability in practice and depends on a number of factors. While SfM takes care of most of the inherent errors in consumer grade (non-metric) cameras, recent studies have shown that UAV imagery processed through SfM suffers from a “bowl” or “doming” effect (a kind of reprojection error) associated with poor photographic network geometry (linear and parallel flight lines in most automated flights) and lens distortions in non-metric cameras. Additionally, off-nadir distortions are common because the flight altitude in most cases is quite low (50-150 m AGL) compared to traditional aerial photogrammetry (James and Robson, 2014; Peppa et al., 2016). An independent and accurate calibration of the UAV-mounted camera before the flight operation helps to optimize the BBA and reduce or completely eliminate bowl-effect (James and Robson, 2014). Off-nadir and perspective distortions can be eradicated by ensuring a higher overlap (>75%) between images. However, even after eliminating the aforementioned errors, the spatial accuracy of the UAV-SfM generated point cloud still depends on the accuracy of the georeferencing, which can be direct (GNSS onboard UAV) or indirect (GCPs measured on the ground). Incorporating a highly accurate compact RTK-GNSS receiver onboard UAV is very expensive and undermines the attribute “low-cost” for UAV-based mapping. Therefore, ground control points/network that is relatively inexpensive, and established with field tachymetric or RTK-GNSS measurements, is preferred. The optimum number and spatial distribution of GCPs are important to increase the accuracy of the 3D reconstruction as well as to lessen the cost of field measurements. Only a handful of studies have evaluated the influence of the number, as well as the spatial distribution of GCPs on the precision of UAV-SFM derived orthomosaics and DSMs (Tahar, 2013; Shahbazi et al., 2015; 6 Tonkin and Midgley, 2016; Agüera-Vega et al., 2017). In these studies, the mapping accuracy generally increased with an increasing number of GCPs that are well distributed on site. However, for complex natural environments, it is not always possible to place GCPs in a well-distributed configuration. For example, in case of very steep slopes, it is usually unsafe to place GCPs directly on the slope. Similarly, areas involving dense tree cover not only limits the number and proper distribution of GCPs but also their visibility in a sufficient number of images. Also, compared to flat terrain, steep slopes usually require more GCPs to achieve equivalent mapping accuracies. Much of the published literature concerning indirect georeferencing dealt with flat to undulating or moderately sloping terrains with sparse to no vegetation cover, i.e. without any limitation on the number or the distribution of GCPs. Therefore, the first part of this study evaluated issues related to indirect georeferencing in a complex natural terrain consisting of a large landslide that involved very steep (30°-35°) to extremely steep (>70°) multiple/stepped slopes, flat to undulating benches, and tree cover varying from sparse to very dense. These features not only limited the number, but also the distribution of GCPs onsite. A low-cost rotary-wing UAV with an integrated 12.1 Megapixel camera was used to acquire aerial imagery of the site. The photographs were processed with a SfM-MVS workflow to generate a dense point cloud. Using six different configurations of GCPs measured onsite with a RTK-GNSS receiver, the influence of number as well as the distribution of GCPs was assessed. Then, using a highly accurate point cloud comparison technique, the way varied topography and vegetation affect mapping accuracy, was analyzed. 7 1.2.2 Classification of UAV-SfM based point clouds The result of the UAV-SfM workflow is a dense 3D point cloud. In almost all cases, distinguishing between ground and non-ground points in a point cloud is necessary for subsequent analyses (Meng et al., 2010). Ecologists and foresters, for instance, are usually concerned with points corresponding to vegetation for canopy structure and biomass estimation (Bendig et al., 2014; Zahawi et al., 2015). Similarly, city planners are interested in separating ground points and non-ground points for urban object classification, building extraction as well as infrastructure monitoring (Cho et al., 2004; Awrangjeb et al., 2014). Geomorphologists and geotechnical engineers, on the other hand, typically want to remove vegetation points and focus on the bare-earth (BE) for estimating erosion, mapping discontinuities, and analyzing landslide dynamics, etc. (Javernick et al., 2014; Peppa et al., 2016). In the context of landslide monitoring with UAVs, where multi-temporal point clouds or digital surface models (DSMs) are compared for change detection and cut/fill calculations, vegetation and its seasonal variation on the ground surface induce significant errors during co-registration and alignment, resulting in inaccurate assessments (Mancini et al., 2013; Turner et al., 2015; Peppa et al., 2016). Such errors in multi-temporal analysis can be eliminated by using BE point clouds, or digital elevation models (DEMs) interpolated from them. Hydrologic modeling, floodplain assessments, snowmelt and glacial monitoring, carbon sequestration, and natural habitat assessment are examples where engineers and researchers require BE models or DEMs to quantify changes in surface elevation (Tinkham et al., 2011). Point cloud classification is, therefore, a necessary step to gain maximum value from the point cloud information. 8 The majority of the available classification algorithms (or filters) have been primarily designed and used for LiDAR datasets. These algorithms can be broadly categorized into slope-based, morphology-based, and surface-based (Zhang et al., 2016). In general, all classification algorithms have a strong theoretical basis, but most of them are application specific (Meng et al., 2010). Several studies assessed and compared the performance of various algorithms for classification of LiDAR point clouds in different environments (Sithole and Vosselman, 2004; Zhang and Whitman, 2005; Montealegre et al., 2015; Polat and Uysal, 2015). In these studies, the tested algorithms performed well in flat relief terrains and simple scenes, but most of them (except for a few surface-based algorithms) suffered from large errors in steep mountainous areas. This could be due to a basic assumption common to these algorithms that the ground surface is smoother than that of the features above the ground (Meng et al., 2010). In non-vegetated or sparsely-vegetated urban areas, both LiDAR and a UAV-mounted camera provide approximately similar point clouds in terms of density. This, in theory, implies that any suitable LiDAR filter can be used with essentially the same parameters to classify both LiDAR and UAV-SfM based point clouds and yield almost the same accuracies. In contrast, for mountainous terrains covered with tall and dense vegetation, UAV-SfM generated point clouds are considerably less dense compared to LiDAR point clouds for two reasons. First, unlike laser scanner used by LiDAR sensor that has multiple returns, a UAV camera sensor only detects the scene lit by ambient/natural light. For vegetated areas, the ground under the canopy is obscured, resulting in very few (possibly no) ground points. Second, the presence of overcast skies (ideal weather condition) for acquiring aerial images with a UAV is not always likely. On a sunny or sparsely cloudy day, tall/dense vegetation will cause shadows and variant illumination of the same 9 scene in images acquired from different angles/location. This creates problems in image matching and densification of point clouds with the SfM-MVS technique, resulting in a decreased density of ground points surrounding the vegetation. Almost all LiDAR filtering algorithms suffer in steep terrains. In the case of UAV-SfM point clouds, the low point density or presence of voids under and surrounding vegetation in steeply sloping areas can induce additional errors in classification (e.g. in initial estimation of ground points in surface-based methods or computation of slope between adjacent points in slope-based methods) when filters designed for processing LiDAR data are used. In simple cases, color-based classification (Lichti, 2005) is another option for UAV-SfM-based point clouds, but it is not well suited for environments where the ground points (e.g. very fine grass/moss) and the vegetation (bushes and trees) have similar RGB values. Also, shadows, variation in exposure, surface temperature as well as humidity also limit the efficiency of color-based classification (Brodu and Lague, 2012). There are a few commercial LiDAR filters that have also been fine-tuned for UAV-SfM point clouds. However, they are proprietary systems and usually not accessible for independent evaluations (Montealegre et al., 2015). In past few years, various authors have either improved previous open-source LiDAR filters or proposed novel algorithms to suit complex environments (Brodu and Lague, 2012; Zhang and Lin, 2013). However, there is a lack of guidance in the selection of appropriate parameters in these algorithms to achieve optimum results (Montealegre et al., 2015). In the absence of reliable filters specifically designed for processing UAV-SfM based point clouds, lack of proper guidance in the literature regarding the selection of optimum parameters while using open-source algorithms, and unfamiliarity with various features onsite, achieving satisfactory 10 classification results for large and complex environments is challenging. To our knowledge, Serifoglu Yilmaz and Gungor (2016) is the only study to date that compared the performances of a few LiDAR filtering algorithms for classification of UAV-SfM-based point clouds. However, the environment classified by Serifoglu Yilmaz and Gungor (2016) was urban (a small university campus) and the filtering algorithms used were relatively old (pre-2005). The classification errors reported by Serifoglu Yilmaz and Gungor (2016) are in general above 15% and for some algorithms as high as up to 50%, which suggest two things. Either the algorithms used in their study had inherent filtering limitations to remove certain features or the filtering parameters used were not optimum, and this resulted in higher errors. Performance evaluation of various novel (post-2010) or improved filtering algorithms for classification of UAV-SfM point clouds representing complex environments (i.e. with significant variation in topography, vegetation, and above-surface features) have not been reported yet. Therefore, in this study five different open-source LiDAR filtering algorithms, namely, CANUPO, CSF, LASground, MCC-LiDAR, and FUSION were tested to classify a UAV-SfM generated point cloud for a large landslide. The landslide represents a mixture of suburban and natural environment involving significant variations in surface topography and above surface features (details presented later). Although there is a wide selection of open-source LiDAR filters, the five used in this study were carefully chosen based on certain considerations. LASground (improved after PTD algorithm), MCC (multiscale curvature classification), and FUSION are all surface-based methods that have performed well for various terrain types in previous comparison studies for LiDAR data. CANUPO and CSF (cloth simulation filter) are relatively new but have a strong theoretical background to perform well in complex natural environments. CANUPO and CSF have not been 11 independently evaluated in previous comparison studies. Also, none of these five methods were evaluated by Serifoglu Yilmaz and Gungor (2016). 1.3 Study site The site chosen for this study is a slow-moving Perpetual Landslide in the Paradise Flats community of the District of Summerland (DoS), British Columbia, Canada. The site is located on the southern boundary of DoS, just to the north of a steep canyon overlooking Trout Creek, and enclosed by Summerland Golf Club on the west and Canyon View Road on the north (Figure 1). The landslide was first noted by local settlers between 1914 and 1917, and the slide head scarp had developed through to 1925. A comparison of 1938 and 1970 topographic maps reported in Riglin (1977) and GolderAssociates (2015) showed that most of the large slide movements had occurred before 1938 where the main head scarp had progressed to its approximate present-day location. Riglin (1977) classified the slide failure mechanism as a retrogressive rotational slump with a backward tilt. Differential translational movements also occurred downslope resulting in depressed units or grabens at various locations along the slide. Riglin (1977) also mentioned the flow of both coarse and fine sediment driven by the groundwater discharging at the toe, which continues to this day. The slide area is ~ 0.28 km2. The site is currently not used for any purpose other than recreational actives like hiking and biking. However, because of its setting, any significant ground movements in the future will damage Canyon View Road and the houses located to the north, underground utility lines, and the Summerland Golf Club. Erosion at the slide toe area is also a major concern 12 for the fish habitat in Trout Creek as a large volume of sediment is being flushed into the creek each year resulting in increased water turbidity. The landslide has varied vegetation cover, ranging from short bushes at the slide toe to sparse tree cover in its upper middle portion to very thick tree cover on its sides and in the lower half. The toe of the slide is undergoing significant erosion associated with groundwater discharge, especially during the spring and summer months. The topography of the site varies from undulating (0.5-5%) at its north to very steeply sloping (30-50%) in the middle to extremely steep slopes (>70%) at Trout Creek (Riglin, 1977). The total change in elevation between the creek and the top of the slide scarp is ~128 m. Significant variations in slope and tree cover, slow deformation rate, as well as the erosion at the toe, makes this landslide an ideal environment to check the suitability of UAV-SfM technique for mapping and analyzing landslides. The variety in terrain slope and above-surface features also allow for rigorous assessment of the accuracy of the tested point cloud classification algorithms. 13 Figure 1: Location of the study site. Coordinate system: WGS 84 / UTM Zone 11N. 14 1.4 Research objectives The specific objectives of this research are summarized as follows: • Assess the viability of the latest generation of low-cost, rapidly deployable UAVs for mapping and monitoring of landslides. Special focus was placed on finding the optimum configuration of the ground control points used for the georeferencing of the UAV-SfM point clouds. • Establish the limitations and advantages of using UAVs for landslide monitoring. • Assess optimum UAV flight speed, height, and range for its applicability in practice. • Evaluate data acquisition and processing as well as sensor and data fusion requirements. • Evaluate the suitability and limitations of various point cloud classification algorithms for filtering of the UAV-SfM point clouds representing complex natural and suburban environments. • Determine the specific influences of the vegetation, terrain slope, and point density on classification; and • Provide recommendations for potential users for selection of appropriate filtering algorithms and related parameters for steep vegetated terrains. The existing literature regarding the use of UAVs together with SfM and MVs for mapping various processes on earth’s surface including landslides is reviewed in Chapter 2. Chapter 3 presents the details of the aerial survey conducted with a UAV, the field geodetic survey to establish a network of GCPs, the data processing and analysis, and the algorithms used for point cloud classification. The results of the mapping accuracy assessment are presented and discussed in Chapter 4. The 15 results of the point cloud classification accuracy assessment are presented and discussed in Chapter 5. Chapter 6 concludes this thesis. 16 Chapter 2: Literature Review 2.1 Structure from motion (SfM) photogrammetry SfM is an advanced photogrammetric technique that was initially developed for computer vision (Snavely et al., 2008). SfM works under the same basic principle as stereoscopic photogrammetry meaning that a 3-D structure can be constructed from a series of overlapping images. However, a fundamental difference between SfM and conventional photogrammetry is that it has the ability to compute camera pose/orientation and 3D scene geometry without internal or external control points (Snavely et al., 2008; Westoby et al., 2012). Instead, the scene is solved using an iterative, highly redundant BBA performed on a set of matching features (also called keypoints) automatically extracted from several overlapping images (Snavely et al., 2008). SfM is most suited to a dataset of images with a high degree of overlap that covers the full 3D geometry of the object or the scene (Westoby et al., 2012). Traditional softcopy photogrammetric techniques, require either the 3D position of the camera or the 3D location of a network of GCPs, to determine the 3D location of points within a scene. In case of the former, the scene is reconstructed using triangulation, whereas, for the later case, resectioning is performed after manually identifying GCPs in images, to determine camera positions (Westoby et al., 2012). In contrast, SfM does not need the locations of camera or GCPs prior to scene reconstruction. It simultaneously determines both camera pose and scene geometry by automatically identifying keypoints, in a series of overlapping images. Initial estimates of camera positions and object coordinates are determined by tracking these keypoints from image to image. As the computation progresses, multiple solutions become available from the wide range 17 of keypoints in the images, and the initial estimates are then iteratively refined using non-linear least-squares minimisation (Snavely et al., 2008; Westoby et al., 2012). However, the 3-D point cloud generated by SfM lack the scale and orientation provided by GCPs. As a result, the generated point cloud is in an arbitrary (or relative image-space) coordinate system. The transformation of that point cloud to a real-world (or object-space) coordinate system is then achieved by georeferencing it with GCPs measured in the field. Most commercially available UAVs are incorporated with GPS that automatically tags the camera positions in the UAV acquired aerial imagery. However, the accuracy of ordinary (i.e. single-point) GPS varies between ±1.5 to ±5 m. Positional accuracy can be increased to a few centimeters by incorporating a highly accurate compact RTK-GNSS receiver onboard UAV or by establishing a network of GCPs measured with field tachymeter or RTK-GNSS system. 2.2 SfM Workflow The SfM solution can be divided into four steps: 2.2.1 Keypoint extraction The first step is the detection of features in multiple images that can be used to describe image correlations. Although a multitude of feature detecting algorithms exist, such as Harris detector, Oriented Fast and Rotated Brief (ORB), Features from Accelerated Segment Test (FAST), Speeded Up Robust Features (SURF), etc., but the most popular of them that is also incorporated in most commercial SfM software is the Scale Invariant Feature Transform (SIFT) object recognition system (Lowe, 1999, 2004). In each image, SIFT detects those features that do not vary with image rotation or scaling but may partially vary with camera orientation and illumination 18 changes. However, the local pixel gradients are then transformed to compute a descriptor for each feature that is largely insensitive to variations in camera orientation and illumination. These unique features with descriptors (also called keypoints) are then automatically matched in subsequent images (Lowe, 2004; Westoby et al., 2012). The number of keypoints in an image depends on its resolution and texture, as well as the complexity or texture of the scene captured by it. A dense, sharp, and high-resolution image of a natural scene with a range of texture will result in the most number of kepoints. Therefore, SIFT is particularly beneficial in case of UAV aerial imagery, which is often of high to very high resolution. The minimum requirement for scene reconstruction is that these keypoints be matched in at least three images. However, it is recommended to acquire as many images as possible as it will increase the number of keypoint matches thereby optimizing system redundancy. 2.2.2 Bundle adjustment and sparse 3D point cloud generation In the second step, SIFT uses a bundle adjustment system (Triggs et al., 1999) from photogrammetry to estimate camera pose for each frame that is then used to solve for scene reconstruction. Using approximate nearest neighbor and random sample consensus algorithms, keypoints are matched in multiple images. Keypoint matching helps to establish tracks that link specific keypoints in a group of images. For scene reconstruction, only those tracks are used that comprise a minimum of two keypoints matched in at least 3 images. The rest are automatically discarded. This process helps to automatically remove transient features like moving cars, people, birds, etc. prior to scene reconstruction (Snavely et al., 2008; Westoby et al., 2012). 19 The correspondence between kepoints applies constraints on camera pose. Using similarity transformation, extrinsic (location, scale, and orientation) and intrinsic (focal length and radial distortion) parameters of the camera are estimated for each frame. A pair of images with best feature matches is initially used, and using one at a time scheme, additional frames are added and solved for. Triangulation is then performed to estimate the 3D position for each keypoint. The combined output of SIFT and bundle adjustment is a sparse 3D point cloud along with camera pose for each image fixed into a relative coordinate system (Snavely et al., 2008; Westoby et al., 2012). 2.2.3 Dense matching While a sparse point cloud may be sufficient for some applications like virtual tours or image organization, for projects requiring high detail, further processing is needed to enhance the density of the point cloud. For this purpose, algorithms like the Clustering View for Multi-View Stereo (CMVS) and Patch-based Multi-View Stereo (PMVS2) (Furukawa and Ponce, 2010) are implemented. The camera positions computed in the bundle adjustment are used as input. Using CMVS, overlapping images are partitioned into smaller subsets or clusters. PMVS2 is then used to establish correlations between these patches and these locally correlated patches are expanded iteratively to similar neighboring pixels to reconstruct 3-D data (Furukawa and Ponce, 2010). MVS results in a significant increase in point density, usually two to three orders of magnitude higher than that of the sparse point cloud obtained in the previous step. 2.2.4 Post-processing Post processing includes the transformation of the dense point cloud from relative coordinate system to a real-world coordinate system, as well as extraction of DSM, orthomosaic, and DEM. 20 For point cloud transformation, GCPs measured in the field are manually identified in the point cloud and using a rigid body transformation, rotation plus translation matrix along with a scale factor is applied. Once the point cloud is transformed into the real-world coordinate system, orthomosaic and DSM/DEM are extracted for end use. In most commercial software transformation is usually performed after bundle adjustment where GCPs are manually marked in individual images and the solution is then re-optimized. After re-optimization, the dense matching and Ortho/DSM/DEM extraction steps can be executed automatically without the need for manual interruption. 2.3 Suitability of UAV-SfM for mapping and monitoring purposes Various researchers have assessed the suitability of UAVs together with classical photogrammetry and more advanced SfM-MVS based techniques for mapping and monitoring various processes on the earth’s surface. The most dominant application of UAV acquired data in the past decade has been in precision agriculture and land cover mapping (Shahbazi et al., 2014). Normalized difference vegetation index (NDVI) maps derived from UAV acquired hyperspectral data are used to classify vegetation/crop cover and soil surface (Bendig et al., 2012; Primicerio et al., 2012; Zarco-Tejada et al., 2012; McGwire et al., 2013). Compared to field invasive methods, UAV acquired hyperspectral and thermal imagery can also be used for detection and quantification of the deceased/dead crop, water stress as well as pesticide application without damaging the crops. (Xiang and Tian, 2011; Primicerio et al., 2012). UAV-based data help farmers and agricultural producers correctly identify problematic areas, get rapid and precise estimates of damages, and save effort, time and money by making reliable choices (Nex and Remondino, 2014; Shahbazi et al., 2014). 21 Rufino and Moccia (2005) monitored forest fire by flying a radio-controlled fixed-wing UAV equipped with thermal and hyperspectral sensor in visible near-infrared bands. Other studies for monitoring and quantification of wildfires with UAV-mounted sensors include Casbeer et al. (2006), Merino et al. (2006), and Sujit et al. (2007). Dunford et al. (2009) used UAV acquired imagery and object-based classification technique to detect and quantify deadwood in the Mediterranean riparian forest in France. Lucieer et al. (2012) analyzed Antarctic moss beds by flying multi-rotor UAV-mounted with optical and hyperspectral cameras. Oczipka et al. (2009), Brutto et al. (2012), and Li et al. (2012) highlight the applications of UAV-based photogrammetry for 3D surveying and mapping of architectural heritage and archeological sites. Farfaglia et al. (2015) presented an overview of the capabilities of various types of UAVs equipped with different sensors (optical, hyperspectral, LiDAR, Radar) for disaster management and control. Similarly, Chou et al. (2010), Shahbazi et al. (2014) and Nex and Remondino (2014) described the suitability of UAV-based photogrammetry for emergency management and rapid response. UAVs are particularly effective in rapidly acquiring aerial imagery for early impact assessment and rescue planning during and after catastrophic events. UAVs can be rapidly deployed and safely flown over hazardous areas (Nex and Remondino, 2014). On the environmental side, UAV acquired imagery has been used to periodically map ice cover, land, and water (Niethammer et al., 2009; Stumpf et al., 2013; Nex and Remondino, 2014; Siebert and Teizer, 2014). Jensen et al. (2012) monitored stream temperatures using UAV-mounted thermal imager and a hyperspectral sensor in visible-NIR bands. Immerzeel et al. (2014) used UAV acquired imagery to identify and monitor glacier movement in the Himalayan region, whereas Ryan et al. (2015) assessed the dynamics of a calving glacier in Greenland. 22 In the context of landslide detection and monitoring, Niethammer et al. (2009) introduced the concept of UAV and photogrammetry based workflow to generate high-resolution (3-8 cm) orthomosaic and digital terrain model (DTM) of the Super-Sauze Landslide in Southern France. They identified and quantified ground displacements by visually comparing UAV-generated and previously available orthomosaics. Analysis of fissure structures and soil surface moisture were also performed using UAV-generated orthomosaic. James et al. (2010), used UAV imagery for the same Super-Sauze landslide to produced DSM using a GOTCHA region-growing (patch based) algorithm. Niethammer et al. (2010) later employed SfM-MVS workflow to UAV imagery of Super-Sauze landslide for DSM generation. Their SfM-MVS based DSM was denser than GOTCHA based DSM produced by James et al. (2010). They also traced and quantified landslide movements by visual comparison of various surface features in multi-temporal UAV orthomosaics. Niethammer et al. (2012) used VMS, a close-range photogrammetry software, together with GOTCHA algorithm to produce a DTM for the Super-Sauze landslide. Rau et al. (2011) used UAV imagery and close-range photogrammetry to identify and map landslides triggered by rainfall and earthquakes in Taiwan. Carvajal et al. (2011) processed imagery acquired by a Quadrotor UAV in Photomodeler, a photogrammetry based software, to map a road embankment slide. Lucieer et al. (2014) and Turner et al. (2015) followed UAV-SfM workflow to produce DSMs and orthomosaics of a landslide in Tasmania, Australia. They quantified ground movements by comparing two multi-temporal DSMs and verified those with field geodetic measurements. They also employed COSI-Corr image correlation algorithm to DSMs to generate surface displacement maps. 23 Marek et al. (2015) successfully mapped and monitored dynamics of the Halenkovice landslide in the Czech Republic by processing UAV acquired aerial imagery following SfM-MVS workflow in Photomodeler. Torrero et al. (2015), using an octa-rotor UAV, mapped a planar landslide in Vergne and a rockfall in the Germanasca Valley, Italy. Images were acquired automatically in nadir mode for the Vergne landslide and manually in the oblique mode for the Germanasca Valley rockfall. Lately, Peternel et al. (2017) analyzed the Potoška Planina landslide in Slovenia by observing surface movement pattern in UAV-SfM produced DSMs and orthomosaics over a duration of two years. In addition to the amount and rate of surface displacements, volumes of eroded and accumulated materials were also calculated by comparing the time-series of DSMs. 2.4 Existing point cloud classification algorithms The majority of the available classification algorithms (or filters) have been primarily designed and used for LiDAR datasets. These algorithms can be broadly categorized into slope-based, morphology-based, and surface-based methods (Zhang et al., 2016). 2.4.1 Slope-based methods The slope-based algorithm pioneered by (Vosselman, 2000) and its variants (Roggero, 2001; Sithole, 2001; Meng et al., 2009; Susaki, 2012) are based on the measurement of change in slope between adjacent points, and classify a point cloud into ground points (gradual change in slope) and non-ground points (steep change in slope) by specifying a certain slope threshold. However, in case of complex environments involving different features, very steep slopes, low point density or presence of voids under dense vegetation, determining an ideal slope threshold is very difficult (Liu, 2008; Zhang et al., 2016). 24 2.4.2 Morphology-based methods Morphological algorithms (Lindenberger, 1993; Kilian et al., 1996; Lohmann et al., 2000) are based on the mathematical morphology or shapes of features. These algorithms employ suitably sized window to define a region around a point, and operators such as erosion and dilation are then used in combination to select the lowest and highest points, respectively, in ROI. Through opening (dilation after erosion) and closing (erosion after dilation) operations, these operators basically examine whether a point or set of points fits within ROI or intersects it (Sánchez-Lopera and Lerma, 2014). Specifying an optimal size window is critical for the efficiency of morphological filters. A window size on the smaller size of the optimal may not be able to filter out large objects above ground, whereas a larger window will result in smoothing (chopping off) of sharp edges (Sithole and Vosselman, 2004). To overcome this issue, Zhang et al. (2003) in their progressive morphological filter employed gradually increasing window size and height difference between the original and morphologically opened surfaces to separate non-ground points. However, issues still persist in the case of forested and mountainous terrains, since the slope value is assumed to be constant in the algorithm (Evans and Hudak, 2007). Chen et al. (2007) and Li (2013) attempted to further improve the morphological filter by taking into account the local slope. However, difficulty in the selection of numerous complex parameters to suit a specific environment may induce subjectivity in results, making these algorithms hard to use for inexperienced users (Meng et al., 2010; Montealegre et al., 2015). 25 2.4.3 Surface-based methods Surface-based algorithms generate BE by the interaction of a surface with the ground measurements of LiDAR point clouds. Depending upon the way the surface is created, they can be further classified into progressive triangular irregular network densification (PTD) and interpolation-based algorithms. In PTD algorithm (Axelsson, 1999, 2000) and its modifications (Zhang and Lin, 2013) a sparse TIN is first derived from the lowest points in a gridded point cloud, which is then iteratively densified. The two controlling parameters in the densification process are the candidate point’s distance to the TIN facet and the angle it makes with the closest node. These threshold parameters are derived from the median values of the surface data (normals, elevation difference, etc.) representing the entire site (Axelsson, 2000), which is relatively constant for flat/relief terrains but varies a lot for steep terrain and vertical cliffs. As a result, chopping off the ridges, peaks and sharp edges is common in mountainous terrain (Sithole and Vosselman, 2004; Zhang et al., 2016). Most recent improvements in PTD algorithm include Zhang and Lin (2013) that embed smoothness-constrained segmentation to handle surfaces with discontinuities and LASground (LAStools, 2016) that is fine-tuned to handle various types of environments. The PTD algorithm has been used in various commercial point cloud classification software, e.g. Terrascan (2016). Interpolation-based algorithms (Kraus and Pfeifer, 1998; Pfeifer et al., 1999) use weighted linear least-square interpolation for classification. Following an iterative approach, these filters classify the point cloud into the ground and non-ground points based on a certain height threshold value. Adaptive weight functions allow the use of interpolation based methods in both urban (Briese and 26 Pfeifer, 2001) and forested (Kraus and Rieger, 1999) settings, but limitations in their use for steep slopes and rough terrains have also been reported (Liu, 2008; Meng et al., 2010). To improve the performance of interpolation based methods in steep terrain covered with vegetation, Evans and Hudak (2007) proposed a thin-plate spline (TPS) interpolation model that employed curvature threshold across multiple scales to remove non-ground points. Other improved interpolation based methods include multi-scale hierarchical filters (Mongus and Žalik, 2012; Chen et al., 2013), combined with thin plate spline (TPS) interpolated surfaces. 27 Chapter 3: Resources and Methodology 3.1 UAV specifications and aerial image acquisition The UAV used for this study was a remotely controlled, vertical take-off and landing (VTOL), Quadcopter Phantom 3 Advanced (Figure 2) by Dà-Jiāng Innovations Science and Technology Co., Ltd. (DJI). Phantom 3 Advanced measures 35 cm diagonally and has a take-off weight of 1.28 kg (camera and battery included). The onboard integrated camera has a 12.4-megapixel, Sony EXMOR 1/2.3 inch, rolling shutter CMOS sensor, and a 20 mm (equivalent to 35mm) lens with f/2.8 and 94° Field of View. For increased stability in air, the camera is attached to the UAV frame through a 3-axis gimbal that has a 120° tilt range. The gimbal can hold the camera at a fixed orientation relative to the UAV’s nose as well as synchronize with the UAV’s motion to provide a perspective view. Phantom 3 Advanced uses GPS/GLONASS for horizontal positioning, and Vision Positioning System (VPS) along with a barometric sensor for vertical positioning. VPS consists of two ultrasonic sensors and a monocular camera attached to the base of the UAV. Reported horizontal and vertical hover accuracies of Phantom 3 Advanced are +/- 1.5 m and +/- 0.1-0.5 m (with VPS) respectively. UAV’s 3D position during flight is continuously logged on a micro-SD card with a frequency of 1 Hz. The integrated camera is synchronized with the GPS/GNSS to automatically geotag the images. The inertial measurement unit inside the UAV responsible for controlling tilt and movement consists of an accelerometer and 6-axis gyroscope. Intelligent Flight Control (IFC) is included in the UAV, which offers various modes including Course Lock, Home Lock, Point of 28 Interest, Follow Me and Waypoints. IFC supports DJI’s own DJI Go application as well as other third-party applications/software (DJI). For this study, Pix4D Capture application (Pix4D, 2016b) was used in Grid Mission (waypoints mode) with Google earth as a base map to construct a grid of waypoints for each UAV flight. For areas with steep slopes and dense tree cover, a high image overlap is recommended for use with SfM based techniques to extract maximum detail (Pix4D, 2016c). Therefore, the frontal and side overlaps were set to 90 % and 70 %, respectively. A total of four flights were made in 2016 to cover the whole landslide. The flight grids for the year 2016, showing camera positions, are shown in Figure 3. Three out of the four flights (Grids I to III) covering about 85% of the landslide were made at 70 m above take-off point to avoid trees as well as to capture maximum bare-ground in the images. Because of varied site topography, the average UAV flight height for the three grids was ~60-90 m. Flight height of 70 m was selected based on a test flight, at 60 m above take-off point, over a portion of landslide with a thick tree cover. For the test flight, because of shallow depth of field, it was noted that images covered more trees (stems and leaves) than the bare ground around trees. Also, in the case of areas with dense vegetation, increasing the flying height will significantly lower the image perspective distortions, and more detailed images will make feature matching in overlapping images easier and efficient. The fourth flight represented by Grid IV in Figure 3 was made at a lower height (60m) in order to properly map the erosion struck toe region of the landslide. Similarly, a total of five flights (four at 70 m and fifth at 60 m above take-off point) were needed in 2017 to cover the landslide. The on-site weather was mostly sunny with a couple of patches of clouds, but there were significant differences in lighting and associated shadows on the ground due to varied tree cover in different 29 parts of the slide. In order to capture optimum light, the shutter speed of the camera was set to automatic and the ISO fixed at 100 to avoid image noise. All images were acquired at an interval of 2 s. A total of 1160 and 945 images were acquired in the years 2016 and 2017, respectively. Figure 2: UAV used in this study: DJI Phantom 3 Advanced. 30 3.2 Field geodetic survey A field geodetic survey was performed in 2016 as well as in 2017, each a day prior to acquiring aerial imagery with UAV. The purpose of geodetic surveys was the establishment of a ground control network to georeference the UAV imagery with decimeter accuracy. The ground control network comprised of suitably selected fixed points (four irrigation meters, a fire hydrant, two on-road utility holes, and two slender concrete posts), temporary points spread across landslide, as well as permanent points in the form of survey pins inserted into the ground. All the points were Figure 3: UAV flight grids and camera positions for July 2016 survey. Grids I, II, and III represent the UAV flights made at 70 m, and Grid IV represents the UAV flight made at 60 m above take off points. I II IIIIV 31 measured in NAD83(CSRS) / UTM zone 11N (EPSG:2955) coordinate system with a Topcon GR-5 RTK-GNSS receiver. GR-5 has an accuracy of +/- 1-1.5 cm provided the best survey practices are employed in the field (Topcon). A base station was established at a suitable location on site to allow maximum signal between the base and rover of the GR-5. The base was then set up on the base station and allowed to run for over 5 hr to achieve maximum accuracy. All geodetic measurements were made relative to the base station with the rover in static mode. It is important to mention that while the accuracy of Figure 4: Black plywood with white retro-reflective circular targets, centered on top of ground control points (GCPs). 32 base measurements are within the 1-1.5 cm range, the accuracy of the rover measurements relative to base depends on the signal strength between the base and rover. In the case of the Perpetual Landslide, due to thick tree cover, varied slope, and higher altitude difference, the signal between the GR-5’s base and the rover was not sufficient to reach 1-1.5 cm accuracy. Therefore, on average the accuracy of the geodetic surveys was in the range of 5-10 cm. To facilitate the marking of the GCPs in photographs during photogrammetric processing, removable targets were placed and centered on top of GCPs. For clear visibility and accurate marking in images, these targets (60 cm × 60 cm each) were made of black painted plywood with a white retroreflective circular tape (7.5 cm diameter) attached to its geometric centre (Figure 4). Ideally, GCPs should be evenly distributed across the entire site. However, for current study site, such a distribution was not feasible for two reasons. Firstly, because certain areas of the slide, especially the toe area, were not safely accessible, and secondly, because of tall and dense tree cover in various parts of the slide, it would not have been possible to capture the GCPs in a sufficient number of images. Regardless, GCPs were set up to cover maximum area and to allow their visibility in as many images as possible. 3.3 Image processing and three-dimensional point cloud generation UAV acquired aerial imagery was processed in a commercial SfM-MVS based software, Pix4D Mapper Professional (Pix4D, 2016a) that can handle up to approximately 1000 images in a single workflow. The workstation used for processing had an Intel Xeon® ES 1607 V3, 3.7 GHz quad-core processor with 64GB RAM and Nvidia® Quadro K2200 GPU. Out of all the images acquired in the UAV survey, those with significant distortions and false-color were manually removed, and 33 the remaining images (1132 for 2016 and 912 for 2017) were loaded into Pix4D. GCPs measured with RTK-GNSS receiver were also loaded as a text file. Image processing in Pix4D is a 3-step process (Pix4D, 2016c). The first step is initial processing in which keypoints were computed in overlapping images using a feature recognition algorithm. An iterative, highly redundant BBA was then performed on the keypoints to compute internal and external camera parameters and to produce a sparse 3D point cloud. The targeted keypoints option was set to automatic, and full image scale was used to allow maximum feature matching and key point computation. However, this caused Pix4D to crash multiple times. In order to avoid software crashes, the project was split into two subprojects for initial processing. Pix4D can automatically read the onboard GPS geotags from EXIF data of images. An independent initial camera calibration allowed to decorrelate unstable internal orientation parameters such as lens distortions from other parameters (Rehak and Skaloud, 2015), and to optimize internal camera parameters, whereas image geotags were used to optimize camera external parameters and to register the sparse point cloud in a known real-world coordinate system. While automatic georeferencing of the images helps in initial processing, the low-cost GNSS receiver integrated into the Phantom 3 UAV is not very accurate (Sect. 3.1). Therefore, GCPs measured with the RTK-GNSS receiver (Sect. 3.2) were used to achieve higher accuracy. Nevertheless, real-world registration of the point cloud using image geotags changed the manual process of marking the GCPs in individual images to automatic with little manual intervention. This is particularly helpful for projects involving thousands of images and a large number of GCPs. After initial processing, GCPs were marked semi-automatically in individual photographs using the raycloud editor in Pix4D. Six different configurations of GCPs were used, which have been 34 explained in the next section (Sect. 3.4). GCPs were marked at a higher zoom level, thus limiting the projection error to below 0.5 pixels (~0.3 pixels on average) in all configurations to achieve higher marking accuracy. After marking GCPs in the majority of the images, both subprojects were merged, and rematch and reoptimize steps were re-run. Re-matching allowed extracting common keypoints in the images from two subprojects, whereas re-optimization involved optimization of the camera parameters, and computation of rotational and translational matrices as well as scale factor to accurately georeference the point cloud based on GCPs In the second step, the sparse point cloud from the first step was densified using the MVS approach (Furukawa and Ponce, 2010). For point cloud densification, multiple scale option was used with an upper scale limit of half of original image scale to compute a maximum number of 3D points in areas with dense tree cover while maintaining sufficient detail in areas without trees. Also, a minimum limit of 3 matches per point was set along with a matching window size of 7×7 pixels. Point density value was kept optimum, which resulted in a 4 cm resolution point cloud for July 2016 (Figure 5) covering an area of ~0.66 km2, and 3.7 cm resolution point cloud for July 2017 (Figure 6) covering an area of 0.41 km2. The July 2016 point cloud contained 53.4 million points with an average density of 28 points per m3, whereas, the July 2017 point cloud contained 52.02 million points with an average point density of 41 points per m3. The final step involved extraction and export of DSM and orthomosaic at a resolution of 5cm/pixel (Figure 7). Pix4D also includes many other options such as point cloud classification, DTM generation, index calculations and contour map generation at a resolution as high as 0.1 m. Specific details of these processes can be found in Pix4D Mapper Manual (Pix4D, 2016c). 35 Figure 5: July 2016 UAV-SfM point cloud for the Perpetual Landslide. m 36 Figure 6: July 2017 UAV-SfM point cloud for the Perpetual Landslide. m 37 (a) (b) Figure 7: (a) Orthomosaic and (b) Digital Surface Model (DSM) generated from the July 2016 UAV-SfM point cloud. 38 3.4 Ground control network configuration As often the case is for dense vegetation, it is not possible to capture the GCPs in a sufficient number of images. This is because, in all images, other than those that were taken from directly above (+/- 10°) a GCP, either tree trunks or branches/leaves will obstruct the line of sight of the camera to the GCP. During image processing in Pix4D and other SfM software, a GCP needs to be marked manually on only two images to allow software to compute the position of that GCP in all other images that captured it. However, the higher the number of manually marked images, the lower would be the projection error, and the closer will be the computed position of a GCP to its initial position in every image that captured that GCP. Also, for a landslide involving multiple stepped slopes, such as the current study site, placing GCPs on every slope and the benches between two slopes may produce more accurate results than a gridded (evenly spread) GCP configuration. In addition, it may not be safe or practically feasible to afford GCPs everywhere on such landslides. Therefore, to check the influence of the number as well as the distribution of GCPs on UAV mapping accuracy, especially for the given site conditions, six different ground control network configurations were tested for the July 2016 point cloud generated in 2016 (Table 1 and Figure 8). In Configuration A, 28 GCPs were used in a widespread distribution covering the majority of the landslide area. Configuration B is a special configuration (designed based on the topography of the study site), where for all three stepped-slopes, GCPs were placed on relatively flat benches immediately above and below them, covering the slide from almost all sides. Configuration C is a so-called optimal distribution in which 13 GCPs are evenly spread all over the slide in a grid-like distribution. In Configuration D, 16 GCPs were distributed in pairs mostly on the edges of the slide 39 with a single pair in the center. Configuration E is a modification of Configuration D, where instead of pairs, only 8 GCPs were used in single distribution. In the last configuration F, only 5 GCPs were used to limit the number of GCPs from safety and economy perspective in the case of inaccessible or very densely vegetated areas. A total of 39 points were measured on site, and those that were not used as GCPs served as the checkpoints (CPs). Due to the safety hazards associated with extremely steep slopes, it was not possible to cover some parts of the landslide with GCPs. For example, the middle right portion (dense tree cover and extremely steep slope) and bottom of the toe slope (extremely steep slopes and slippery conditions formed by mud and groundwater). Table 1: Summary of the six ground control points (GCPs) configurations used for georeferencing the UAV-SfM point cloud. Configuration Description Number of GCPs A Widespread distribution 28 B Special configuration- majority of the GCPs placed at top and bottom of slopes on relatively flat benches 20 C Evenly spread GCPs (grid-like distribution) 13 D GCPs used in pairs 16 E Modified D- only single GCPs used instead of pairs 8 F Minimum number of GCPs used for safety reasons in case of inaccessible areas 5 40 Figure 8: Ground control points (GCPs) configurations used for georeferencing the UAV-SfM point cloud. A B C D E F 41 3.5 Point cloud classification and bare earth extraction All slope stability analyses require 2D or 3D profiles of the slope, which are typically obtained from a DEM. A DEM is a surface that represents the ground or natural terrain. Therefore, from a landslide or slope perspective, in order to obtain a DEM, it is necessary to classify the point clouds into ground points (or bare-earth), and non-ground points (vegetation and other artificial structures above ground surface). Also, for landslide dynamic analysis, where multi-temporal point clouds and DSMs are compared for change detection and cut/fill calculations, vegetation and its seasonal variation on the landslide surface induce significant errors during co-registration and alignment, resulting in inaccurate comparison (Lague et al., 2013; Mancini et al., 2013; Turner et al., 2015; Peppa et al., 2016). In this study, five different classification algorithms, originally designed for LiDAR-based data, were tested to classify the July 2016 UAV-SfM point cloud generated in Sect. 3.3. A brief overview of each algorithm along with the specific settings used for the study site is presented below. 3.5.1 CANUPO - scale dependent dimensionality CANUPO, developed by Brodu and Lague (2012), is based on the 3D geometry of different feature classes (ground, vegetation, buildings) within a point cloud across multiple scales. A set of points, within an imaginary ball, centered on certain core points in a point cloud, is classified into 1D, 2D, or 3D features based on their local/scale dependent dimensionality properties. Scale-dependent dimensionality means that the geometry of the set of points representing different features within the imaginary sphere will either remain constant or change with the diameter (or 42 scale) of the sphere. As an example, consider four different features within the current study site: the transmission line, the ground surface, a garage, and the trees. At a decimeter scale (sphere diameter = 10 cm), the transmission line will be a 1D element; the ground surface and the roof and walls of the garage will be 2D surfaces; and the trees will be a combination of 3D (leaves), 2D (trunk) and 1D (branches). If the scale is increased (sphere diameter = 10 m), the electric wire and the ground surface will remain 1D and 2D respectively, but the garage and the trees will now become 3D features. Based on these dimensionality characteristics, Brodu and Lague (2012) employed linear discriminant analysis (LDA) and support vector machines (SVM) to define descriptors/signatures for each feature class. The descriptors are defined by first projecting the dimensionality characteristics of the features at the specified scales on a plane of maximal separability, and then the individual classes in that plane are separated by a separation boundary. The algorithm is available as a plugin, called CANUPO, in open-source point cloud processing software, CloudCompare (v2.8). The point cloud classification in CANUPO is a two-step process. The first step consists of training a classifier using manually separated ground points and non-ground points from a subsample of the entire point cloud. In the second step, the trained classifier is used to classify the whole point cloud. To train a classifier for the current study site, a part of the UAV-SfM point cloud was carefully segmented so that it contained points representing all varieties of features encountered onsite, such as both flat and sloping ground, small to medium bushes and trees, and artificial structures above ground (houses, cars, electric poles). The segmented part was then manually separated into the BE and OBJ classes. The most important part of the training phase is the selection of scales at which 43 the algorithm computes the dimensionality descriptors for each class. Usually, a range of scales can be specified, and the algorithm automatically determines the best combination (weights) for the specified scales to achieve maximum separability of the two classes (Brodu and Lague, 2012). However, for the study site, it was observed that if the boundaries and range of the specified scale-set were off from the actual field conditions, it significantly degraded the classification accuracy. For instance, if the minimum scale was not small enough to account for the surface roughness at the slide’s toe, the crests and troughs of the erosion gullies were cut off. Similarly, if the scale was not large enough, it did not entirely cover the artificial features (especially flat surfaces) above ground, resulting in a classification of the roofs of buildings and cars into ground points. It is also important to mention that for the study site, scales as small as 5 cm and as large as 10 m were involved. Specifying a set of scales from 0.05-10 m with an interval of 0.1 m was easy, but increasing the number of scales over 12 significantly increased the calculation time by a factor of ~6-8, and the CANUPO plugin frequently crashed during the classification step. Therefore, after trial and error, a scale set consisting of a total of 12 different scales with varied intervals was selected to cover the geometry of most features onsite, and also provided the best possible classification results. The classifier was trained using LDA, with 50,000 core points, and the training process took approximately 15 mins. To check the efficiency of the training process, Brodu and Lague (2012) incorporated two measures in CANUPO: balanced accuracy, ba, and the fisher discriminant ratio, fdr. The balanced accuracy is a measure of the percentage of points truly classified into each class. The Fisher discriminant ratio is a measure of the class separability or separation distance between the two classes. For the built classifier, ba = 97% and fdr = 9.74 indicating excellent accuracy and class separation. 44 Finally, to avoid memory and computing power issues, the entire UAV-SfM PC was segmented into three tiles, each containing under 20 million points. The tiles were classified in a batch process into ground points and non-ground points using the built classifier. Tiles were used to overcome insufficient memory problems when processing the entire point cloud as the CANUPO algorithm is computationally intensive. 3.5.2 Cloth simulation filter The cloth simulation filter (CSF) is an open-source LiDAR classification algorithm formulated by Zhang et al. (2016). CSF is based on the physical process of cloth simulation (Weil, 1986), where a virtual cloth, consisting of nodes connected by springs (mass-spring grid system), is dropped under gravity on an inverted point cloud. The position and velocity of the nodes in space and time are computed based on Newton’s second law. Zhang et al. (2016) modified the original cloth simulation process to improve its processing of LiDAR point clouds. In the modified version, the nodes of the cloth can travel only in a vertical direction, and their movement stops as they collide with the inverted point cloud. In the case of voids, the node movement is constrained by defining a suitable cloth (spring) rigidness. As the cloth reaches the point cloud, the interaction between its nodes and the point cloud results in the deformation of the cloth into a grid representing the bottom structure of the point cloud. The deformed cloth grid is then used a reference for the original point cloud to compute the cloud to grid distances. Finally, the original point cloud is split into ground points and non-ground points using a certain threshold distance from the grid. For clouds involving steep slopes, the internal constraints among nodes (cloth rigidness) does not allow the nodes above the slope margins to collide with the corresponding points in the point cloud. The movement of such nodes does not cease, and as a result, the ground points representing the 45 slope margins are falsely classified as non-ground points. To rectify this, post-processing is performed in which the position of the moving nodes above the slope margins is fixed step-by-step based on the height difference between the adjacent ground points. CSF is also available as a plugin in CloudCompare (v2.8). The plugin is easy to use and requires four simple input parameters: (i) type of terrain (steep, relief, or flat), which controls the cloth rigidness, (ii) cloth resolution, which represents the distance between the interconnected cloth nodes, (iii) number of iterations, and (iv) classification threshold distance, which is the distance from the simulated ground surface within which the points are considered as ground points. The cloth is made more rigid for flat surfaces and less rigid for steep terrains. A lower value of the cloth resolution is recommended for steep terrain and a higher value is suitable for flat terrain. In order to classify the UAV-SfM point cloud in this study, the type of terrain was selected as “steep” with post-processing option checked, and the number of iterations was set to maximum (i.e. 1000). Five values of threshold distance (0.5, 0.4, 0.3, 0.2, 0.1 m) and six values of cloth resolution (1.0, 0.8, 0.6, 0.4, 0.2, 0.1 m) were tested to check their influence on the classification accuracy. Finally, a threshold distance of 0.3 m and a cloth resolution of 0.2 m were adopted as they provided relatively better results. 3.5.3 LASground -LAStools LASground is a part of LAStools (2016) designed by rapidlasso GmbH for LiDAR data processing. The algorithm behind LASground is refined from the PTD filter by Axelsson (1999, 2000) to suit various environments, especially steep and hilly terrains. However, the exact details of the refinement have not been published. Compared to slope-based, morphological, and other surface-based algorithms, PTD filters have shown better results for all types of environments in 46 other filter comparison studies (Montealegre et al., 2015). This is because they use more context of the environment (of the features compared to their neighborhood) and thus have a better ability to separate the ground points and non-ground points (Sithole and Vosselman, 2004). LAStools is available for free for academic and research use, but the free version of the software produces some noise in the final output if the number of points exceeds a certain limit (~1.5 million). To avoid the noise in the free version of LASground, LAStile (included in LAStools) was first used to divide the entire point cloud into 36 tiles, so that the number of points in each tile was below 1.5 million (maximum allowed without noise). A suitable buffer (~5 m) was employed to avoid edge artifacts. Each tile was then classified into ground points and non-ground points using batch processing in LASground. Finally, the classified tiles were merged. There are five parameters that control the filtering process in LASground. These parameters include (i) step size, (ii) intensity for initial ground search, (iii) bulge, (iv) spike thresholds, and (v) ground offset. The step size defines the grid size and the lowest points in each grid cell are selected as seed points to form an initial TIN surface. The intensity defines the size of the sub-grid the algorithm uses to look for the probable ground (lowest points) between the chosen step size. There are five options for intensity ranging from the default, fine or extra-fine for flat terrains to ultra or hyper for mountains and steep slopes. The bulge is a unitless scale that defines how much the TIN is allowed to bulge up for densification during successive iterations. The bulging is based on the averaged normals of the TIN surface that is the current ground estimate. The spike thresholds are the distances at which the up and down spikes get cut off. The ground offset is the distance up to which the points above the current ground estimate get included. 47 LASground has various built-in classification settings to choose from. These settings are based on the type of environment, e.g. wilderness, nature, small towns, cities, metropolis, etc. Knowing the variation of features present in the study site, the “wilderness” classification setting (step = 3 m, bulge = 1, up/down spikes = 1 m, ground offset = 0.05 m) with a hyper setting for the initial point search was tested first, but it did not produce accurate results. Therefore, following a trial and error procedure, custom parameters (step = 0.75 m, bulge = 1.5, up/down spikes = 1.5 m, and ground offset = 0.3 m with hyper settings for the initial points search) were found to achieve better results. 3.5.4 MCC-LiDAR MCC-LiDAR (v2.1) is a command-line filter designed for forested environments by the United States Forest Service Rocky Mountain Research Station. It is based on the multiscale curvature classification (MCC) algorithm by Evans and Hudak (2007) that combines progressive curvature (positive local deviations from surrounding points) tolerance with thin-plate spline (TPS) interpolation. A surface is interpolated using the TPS method at three different scale domains (or resolutions). For each scale domain, non-ground points are iteratively removed based on a progressive curvature tolerance. As the scale domain increases (surface resolution decreases) progressive curvature tolerance also increases to address the slope effect as the data is generalized. The iterative process continues until the results of two successive iterations differ by less than 1%, 0.1%, and 0.01%, respectively, for the three scale domains. A major advantage of using TPS compared to other interpolation techniques is that it allows tension adjustment between two adjacent points based on input data and thus controls the distance at which sample points affect the surface (Evans and Hudak, 2007). The two parameters needed in the command-line syntax are the scale parameter, s, and the curvature tolerance, t, which depend on the resolution of the point 48 cloud and the size of various features captured in the point cloud. A series of combinations for s and t parameters were tested for the study site, and after comparison, it was determined that s = 0.8 and t = 0.7 provided the best results. 3.5.5 FUSION Ground filter in FUSION (v3.60) is another command-line tool developed for forested environments by the United States Forest Service Pacific Northwest Research Station. The filter is based on the weighted linear least-squares interpolation-based method by Kraus and Pfeifer (1998). The filtering process is iterative. The first step involves computation of a surface with equal weights for all points. Thus, the resulting surface lies somewhere between BE and OBJ surfaces with ground points more likely to lie below it. In the second step, Eq. (3-1) is used to compute the weights for the points above and below the surface using their distance and direction to the surface. 𝒑𝒊 = {𝟏 , 𝒗𝒊 < 𝒈𝟏𝟏+(𝒂(𝒗𝒊−𝒈))𝒃 , 𝒈 ≥ 𝒗𝒊 ≤ 𝒈 + 𝒘𝟎 , 𝒗𝒊 > 𝒈 + 𝒘 (3-1) The parameters a and b in Eq. (3-1) define the slope of the weight function. The default values for a and b are respectively 1.0 and 4.0, which produce satisfactory results in most situations. The parameter g represents the shift value that determines which points should be assigned the maximum weight value of 1.0. These are the points that lie below the interpolated surface by a 49 distance more than g. Parameter w is the above-ground offset that defines an upper limit for points to have an influence on the interpolated surface. Thus, points that lie above (g+w) are assigned a weight of zero. At the end of each iteration, because of the greater overall weight for the points below the surface compared to those above it, the next surface shifts down towards the ground. After the completion of the iterative process, the final surface is used to select BE points based on the first two conditions of the weight function in Eq. (3-1). From a series of analyses conducted using different combinations of g, w, a, and b parameters, it was determined that b and w have more effect on the classification results than g and a. Better results were produced using g = -0.5 m, w = 1 m, a = 1, b = 6, iterations = 10, resolution = 1 m and offset = 0.3 m. 3.6 Classification accuracy assessment 3.6.1 Reference point clouds Two types of errors can occur in point cloud classification, namely, Type I (omission) error and Type II (commission) error. The former refers to the incorrect classification of ground points into non-ground points, and the later is the inclusion of non-ground points into ground points (Congalton, 1991; Sithole and Vosselman, 2004). Correct or incorrect classification of ground points and non-ground points is decided by comparison with ground truth or reference point clouds. In this study, the UAV-SfM point cloud was manually classified into ground points and non-ground points through 3D visual observation in CloudCompare (v2.8) to generate a reference BE point cloud [Figure 15(a)] and OBJ point cloud [(Figure 15(g)]. UAV-SfM point clouds have an advantage over LiDAR point clouds as they do not require separate imagery or intensity images 50 for manual classification, as ground points and non-ground points can be visually identified from the point cloud itself. 3.6.2 Performance evaluation The accuracy of the five classification algorithms was assessed qualitatively, as well as quantitatively. Qualitative assessment included a 3D visual comparison of the classified point clouds with reference point clouds to highlight the degree of Type I and Type II errors. It is a straightforward way of identifying the difficulties experienced by the algorithms by comparing their classification behavior on different features. For quantitative assessment, Type I, Type II, and Total errors were calculated using the following equations (Sithole and Vosselman, 2004): 𝑇𝑦𝑝𝑒 𝐼 𝐸𝑟𝑟𝑜𝑟 =𝑨(𝑻𝒐𝒕𝒂𝒍 𝑮𝑷𝒔)𝒓𝒆𝒇 (3-2) 𝑇𝑦𝑝𝑒 𝐼𝐼 𝐸𝑟𝑟𝑜𝑟 =𝑩(𝑻𝒐𝒕𝒂𝒍 𝑵𝑮𝑷𝒔)𝒓𝒆𝒇 (3-3) 𝑇𝑜𝑡𝑎𝑙 𝐸𝑟𝑟𝑜𝑟 =𝑨+𝑩(𝑻𝒐𝒕𝒂𝒍 𝑮𝑷𝒔 + 𝑻𝒐𝒕𝒂𝒍 𝑵𝑮𝑷𝒔)𝒓𝒆𝒇 (4-4) Where A is the number of ground points wrongly classified as non-ground points and B is the number of non-ground points incorrectly classified as ground points in point clouds classified by the algorithms. The parameters (Total ground points)ref and (Total non-ground points)ref are the total number of ground points and non-ground points in the reference BE point cloud and OBJ point cloud respectively. In addition, Cohen’s Kappa index, K (Cohen, 1960) was also calculated for each filter. Compared to a simple error percentage, K measures overall inter-rater agreement reflecting the difference between actual agreement and that expected by chance (Congalton, 1991). 51 In general, K ranges from 0 to 1, but values below zero are also possible. In the context of remote sensing, K above 0.8 indicates excellent agreement, below 0.4 represents poor agreement, and between 0.4 and 0.8 is considered as good agreement (Montealegre et al., 2015). Two approaches were used for quantitative assessment. In the first approach Type-I error, Type-II error and K were calculated for the entire point cloud to have an overall assessment of the accuracy. However, this approach does not provide an estimate of spatial variation of accuracy metrics across the study site, e.g. terrain slope, sparse versus dense vegetation, and features that present extra difficulty in classification (i.e. very low objects, buildings, sharp/rough surfaces, and vertical walls/cliffs, etc.) For complex environments, Type-I and Type-II errors vary with the variation in topography and surface features (Sithole and Vosselman, 2004). Therefore, in a second approach, suitably sized samples, representative of afore-mentioned features within the study site were segmented from the classified clouds and compared with the corresponding samples in the reference point clouds to calculate the error metrics and K. 52 Chapter 4: Results and Discussion – Part-I: Mapping Accuracy Assessment 4.1 Accuracy assessment The accuracy of the DSM and orthomosaic derived from the UAV-SfM point cloud, are important for practical purposes, such as change detection, volume loss and accumulation measurements, slope profile generation, etc. For accuracy assessment, differences/errors (∆) between the true position (measured with RTK-GNSS) of GCPs/CPs and their projected position (computed from orthomosaic and DSM) were used to calculate the root mean square error (RMSE) in easting (X), northing (Y), horizontal (XY), and vertical (Z) directions, using Eqs. (4-1), (4-2), (4-3), and (4-4), respectively. 𝑅𝑀𝑆𝐸𝑋 = √∑ (𝑿𝑮𝑵𝑺𝑺𝒊−𝑿𝒐𝒓𝒕𝒉𝒐𝒎𝒐𝒔𝒂𝒊𝒄𝒊)𝟐𝑵𝒊=𝟏𝑵 (4-1) 𝑅𝑀𝑆𝐸𝑌 = √∑ (𝒀𝑮𝑵𝑺𝑺𝒊−𝒀𝒐𝒓𝒕𝒉𝒐𝒎𝒐𝒔𝒂𝒊𝒄𝒊)𝟐𝑵𝒊=𝟏𝑵 (4-2) 𝑅𝑀𝑆𝐸𝑋𝑌 = √∑ ⌊(𝑿𝑮𝑵𝑺𝑺𝒊−𝑿𝒐𝒓𝒕𝒉𝒐𝒎𝒐𝒔𝒂𝒊𝒄𝒊)𝟐+ (𝒀𝑮𝑵𝑺𝑺𝒊−𝒀𝒐𝒓𝒕𝒉𝒐𝒎𝒐𝒔𝒂𝒊𝒄𝒊)𝟐⌋𝑵𝒊=𝟏𝑵 (4-3) 𝑅𝑀𝑆𝐸𝑍 = √∑ (𝒁𝑮𝑵𝑺𝑺𝒊−𝒁𝑫𝑺𝑴𝒊)𝟐𝑵𝒊=𝟏𝑵 (4-4) For each GCP configuration (Sect. 4.4), to determine the projected coordinates of the GCPs /CPs in the horizontal plane, their position was manually marked on the orthomosaic in ArcGIS. The use of the circular retroreflective targets centered at the GCPs (Sect. 3.2) helped to ensure accurate 53 marking on the orthomosaic. For each GCP/CP, the orthomosaic was zoomed to an appropriate level to mark a point at the geometric center of the target. A geometry calculation tool within ArcGIS 10.0 was then used to compute the X-Y coordinates of the marked GCPs/CPs. The computed X-Y coordinates were later used to sample the corresponding Z coordinates from the DSM using a point sampling tool in Quantum GIS 2.18. Table 2 shows the RMSEs in X, Y, XY, and Z directions for each GCP configuration. The error in Z coordinate (∆Z) for all GCPs/CPs are shown in Table 3. The detailed calculations performed to get the RMSE values reported in Table 2 are included in Appendix A. Out of a total of 39 points measured in the field (Sect. 4.2), those that were used as GCPs are marked with asterisk ‘*’ sign. In Table 2, two values are mentioned for each RMSE. Top values were calculated based on all 39 GCPs/CPs, whereas in the bottom (bold) values, 4 GCPs/CPs with relatively very large ∆Z (underlined in Table 3) were omitted to calculate RMSEs based on only 35 GCP/CP measurements. There were four reasons for ignoring the 4 GCPs above. First, these GCPs were located in areas surrounded by thick tree cover that weakened the signal between the RTK-GNSS base and the receiver, thus decreasing the accuracy of the measurement, especially in the Z direction, from 0.05 m (average) to ~0.20 m. Second, in the case of dense tree cover, because of the difficulty in image matching due to inherent similarity among leaves, fewer keypoints are computed that affects the accuracy of reconstruction, especially in the vertical direction (Mancini et al., 2013; Pix4D, 2016c). Third, all of these points were at a certain distance (~1 to 1.5 m) above ground level (i.e. tops of a fire hydrant and a fence pole, and top corners of two thin concrete posts). The absence of oblique imagery (all 1132 images were taken vertically) might have affected the vertical construction of these slender elements in 3D point cloud (El Meouche et al., 2016). 54 Fourth, although the noise reduction option for DSM generation from point cloud was set to sharp instead of smooth, such slender/thin elements could suffer from the effect of noise reduction and interpolation errors in areas with lower point density (Song and Nan, 2009; Mancini et al., 2013). This latter point was confirmed by sampling the Z values for the top of fence poles from the DSM, which did not represent the actual height of these features in the field. In Table 2, the top values have only been mentioned to highlight the effect of both dense tree cover and the absence of oblique imagery on the accuracy of the 3D point cloud. The RMSE values in bold are more appropriate to represent the accuracy of the constructed 3D model for the aforementioned reasons. Therefore, RMSE values in bold will be discussed in the following paragraphs. A number of findings can be deduced from Table 2. Both horizontal and vertical accuracies increase with increasing number of GCPs, except for configuration B where horizontal accuracy is slightly lower than that for configuration A. RMSE in X, Y, and Z directions increase from 0.042, 0.040, 0.044 m for configuration A (28 GCPs) to 0.140, 0.085, 0.245 m for configuration F (5 GCPs), respectively. In the horizontal plane, RMSE in the X direction is higher than that in the Y direction. This could be associated with the presence of rolling shutter camera and the East-West direction of the UAV flights. Although the shutter speed was kept very high (~1/600-to1/1000 s) to avoid image blurring, relatively high UAV speed (~2-5 m.s-1) caused some blurring in the direction it was flown. Reduction in the number of GCPs affected the accuracy in the vertical direction more than the horizontal direction, which is in agreement with the study conducted by Agüera-Vega et al. (2017). RMSE values for configurations A and B in all three directions are close. As mentioned in Sect. 3.4, configuration B is a special configuration that was designed taking into account the topography of the slide (i.e. stepped slopes and dense vegetation). Eight 55 GCPs mostly covering flat/relief areas were removed from configuration A, and the remaining 20 GCPs were placed on relatively flat benches immediately above and below the three slopes onsite. From economy and safety perspectives, the number of GCPs in Configuration B were lowered by ~30% compared to Configuration A, while maintaining a minimum accuracy of 0.057 and 0.051 in horizontal and vertical directions, respectively. This careful reduction in the number of GCPs without significantly affecting the mapping accuracy is in agreement with Mancini et al. (2013) and Tonkin and Midgley (2016). In configuration C, 13 GCPs were used such that the distance between the GCPs was approximately the same, and the GCPs were distributed in an arc-like grid. It is important to mention here that, a proper gridded distribution of GCPs was not possible for the study site because of certain inaccessible areas, and/or lack of visibility of GCPs in aerial images due to the dense tree cover. Therefore, Configuration C was the closest possible to a proper gridded distribution. Nevertheless, this grid-like distribution of GCPs did not result in the highest accuracy for the current study site, which is due to the huge variation in topography between GCPs. As suspected in Sect. 3.4. a gridded distribution produces best results for relatively flat, undulating, or relief slopes, but with multiple/stepped slopes and valleys, a configuration that takes into account all slopes (e.g. configuration B) is more likely to provide better results. 56 Table 2: Root mean square error (RMSE) for the six GCP configurations used for georeferencing the UAV-SfM point cloud Configuration Number of GCPs RMSEX (m) RMSEY (m) RMSEXY (m) RMSEZ (m) A 28 0.044 0.038 0.058 0.187 0.042 0.040 0.058 0.044 B 20 0.047 0.031 0.056 0.200 0.047 0.033 0.057 0.051 C 13 0.065 0.045 0.079 0.207 0.066 0.046 0.080 0.093 D 16 0.070 0.053 0.088 0.210 0.070 0.053 0.088 0.115 E 8 0.068 0.051 0.086 0.250 0.066 0.053 0.085 0.111 F 5 0.137 0.093 0.166 0.322 0.140 0.085 0.164 0.241 RMSE values for Configuration E surprisingly came out to be slightly lower than Configuration D with double the number of GCPs. The difference in these two configurations is that in configuration D, 8 pairs of GCPs positioned relatively close to each other were used (7 pairs on slide edges and 1 pair in the central area), whereas in Configuration E, instead of pairs, only 8 57 GCPs in single distribution were used. The shapes of both configurations were kept approximately similar. Finally, configuration F (5 GCPs) that was used keeping in view the cost of the field geodetic survey and the hazard associated with inaccessible areas resulted in the highest RMSE in all directions. This may be suitable for earth flows or mudflows where an error of 0.25 cm may not be significant compared to very large overall displacements. However, for slow-moving landslides, like the current study site, it will not serve the purpose of accurate assessment or monitoring of landslide dynamics. Before comparing the results of this study with other studies, it should be emphasized again that in addition to the number of GCPs, mapping accuracy also depends on the distribution of GCPs with respect to the complexity of the environment, and the environment can vary from study to study. Nevertheless, the results obtained in this study are in general agreement with other studies that reported the effect of the number of GCPs on mapping accuracy. For example, Tahar (2013) increased the number of GCPs from four to nine in a gridded configuration and concluded that both horizontal and vertical RMSE decreased from 0.049 and 0.83 m for 4 GCPs to 0.45 and 0.77 m for 9 GCPs, respectively. Agüera-Vega et al. (2017) evaluated the variation in RMSE by using different configurations of three, five and ten GCPs on five different terrains. On average, both horizontal and vertical accuracies increased from 0.225 and 0.10 m for three GCPs to 0.053 and 0.049 m for ten GCPs. Kršák et al. (2016) measured the height difference between UAV based and total station derived DSMs. Out of 237 total measurements, only 3 points had a height difference greater than 0.12 cm, with an overall RMSE of 0.048 m. Lindner et al. (2016) tested different configuration of GCPs and achieved RMSE in the vertical direction of 0.111 m for 15 GCPs to 0.125 m for 6 GCPs. Tonkin and Midgley (2016) conducted a comprehensive study in which 16 58 different distributions of GCPs were tested. Horizontal and vertical RMSE varied from 0.059 and 0.045 m for 101 GCPs to 0.156 m and 0.126 m for 3 GCPs. Compared to other studies that mapped landslides with UAV acquired imagery, the results obtained in this study (0.058 m in XY and 0.044 m in Z for configuration A, and 0.057 m in XY and 0.051m in Z for configuration B), are an improvement over those studies. For example, Carvajal et al. (2011) achieved horizontal and vertical accuracies of 0.049 and 0.108 m, respectively; Niethammer et al. (2012) achieved RMSE values of 0.5 m in horizontal and 0.310 m in vertical direction; Lucieer et al. (2014) reported accuracies of 0.07 m in horizontal and 0.06 m in vertical direction; Lindner et al. (2016) achieved RMSE in vertical direction of 0.111 m. Accuracies obtained by Turner et al. (2015), i.e. 4–5 cm in horizontal and 3–4 cm in vertical directions, are better than the current study. However, they used a higher number of GCPs on a smaller area and flew their UAV at a lower elevation (i.e. 40 m compared to 70m in this study). Also, the topography was not as complex as the current study site. Peternel et al. (2017) achieved accuracies of 0.03-0.04 m in horizontal and 0.02-0.03m in vertical directions, which are also better than that obtained in this study. 59 Table 3: Vertical error (∆Z) in RTK-GNSS measured and DSM computed positions of the GCPs/CPs, and the corresponding RMSE values Point ID ∆ZA (m) ∆ZB (m) ∆ZC (m) ∆ZD (m) ∆ZE (m) ∆ZF (m) 1 -0.001 0.009 0.124 0.001 -0.033 0.200 2 0.172 0.397 0.310 0.251 0.351 0.398 3 1.051 1.059 1.099 0.922 1.071 0.862 4 -0.009 -0.003 0.006 -0.124 0.010 -0.091 5 0.005 -0.008 -0.010 -0.048 -0.011 -0.002 6 -0.027 0.059 0.137 0.073 0.204 0.645 7 0.034 -0.012 -0.031 0.079 0.059 0.460 8 -0.029 -0.033 0.114 0.058 -0.019 0.428 9 -0.001 -0.002 -0.078 0.029 -0.102 0.249 10 0.081 0.104 0.137 -0.048 0.111 0.319 11 -0.051 0.026 -0.185 -0.018 0.000 0.123 12 0.022 0.007 0.031 0.021 -0.005 0.200 13 -0.055 -0.061 -0.082 -0.089 -0.084 -0.077 14 -0.018 -0.015 0.049 -0.045 -0.037 -0.043 15 -0.066 -0.090 -0.051 -0.088 -0.088 -0.081 16 0.006 -0.026 0.009 -0.046 0.031 -0.035 17 0.011 0.016 -0.068 -0.069 -0.123 0.033 18 0.031 0.039 0.098 0.292 0.281 0.474 19 -0.082 -0.072 -0.244 -0.237 -0.294 -0.219 20 0.054 0.076 0.112 0.041 0.014 0.217 21 0.044 0.039 -0.003 0.073 0.024 -0.023 22 -0.005 0.061 0.027 0.043 0.055 0.089 23 0.072 0.024 0.004 0.048 0.063 0.057 24 -0.046 -0.028 -0.043 -0.022 -0.014 -0.013 25 0.016 0.008 -0.008 0.063 0.019 0.059 26 0.081 0.023 0.062 0.093 -0.009 0.073 27 0.112 0.170 0.371 0.550 0.806 0.735 28 0.116 0.174 0.142 0.237 0.142 0.299 29 0.379 0.397 0.208 0.219 0.304 0.757 30 0.043 0.066 0.020 -0.026 -0.055 0.083 31 -0.001 0.009 -0.005 -0.064 -0.113 -0.078 32 0.041 0.052 0.125 0.009 0.054 0.043 33 -0.033 0.008 0.140 0.191 0.014 0.045 34 -0.013 -0.007 0.121 0.191 0.014 0.054 35 -0.006 0.016 0.061 0.035 0.030 0.099 36 0.003 0.021 0.036 0.034 0.010 0.138 37 0.029 0.052 0.023 0.243 0.236 0.317 38 0.027 0.002 0.104 0.183 0.235 0.605 39 -0.017 0.003 -0.051 0.057 0.030 0.006 RMSEz 0.187 0.200 0.207 0.210 0.250 0.322 *RMSEz 0.044 0.051 0.093 0.115 0.11.1 0.241 *RMSEz calculated by omitting underlined ∆Z values 60 4.2 Effect of topography and vegetation on UAV mapping Computation of errors between the GCPs/CPs position in Section 4.1 provided an assessment of the combined, i.e. absolute (between GCPs) plus relative (between CPs), accuracies of the UAV-SfM derived orthomosaics and DSMs. However, since the GCPs/CPs were not equally distributed in the most accurate GCP configuration (configuration-A), and the evenly distributed GCPs (configuration-C) did not result in the highest mapping accuracy for the study site, RMSEs or the errors in GCPs/CPs positions did not provide comprehensive answers to some important questions. For example: which part(s) of the landslide suffered more mapping error? How did the varied topography and vegetation cover affect the mapping accuracy? In addition, due to limited resources and safety concerns on site, some areas of the landslide were not properly covered with a sufficient number of GCPs or CPs (see Sect. 4.4), which means, RMSE values did not provide information about absolute or relative accuracies in those areas. To answer these questions, we used a comparison technique that involved measurement of the overall distances (i.e. differences) directly between a reference point cloud and the comparison point clouds using the Multiscale Model to Model Cloud Comparison (M3C2) algorithm (Lague et al., 2013) in CloudCompare (2016). M3C2 has a number of advantages over other techniques for determining differences between the two point clouds. Compared to DSM of difference technique (Milan et al., 2011) that works on 2.5 D surfaces, the M3C2 algorithm works in 3D and directly on the point clouds. Thus, the projection and smoothing errors involved in DSM/DEM generation are avoided. In contrast to the widely used C2C algorithm (Girardeau-Montaut et al., 2005) that measures the distance between the closest points in two point clouds; M3C2 tracks 3D surface variations (roughness) by determining 61 the local distances between two point clouds along a surface normal that takes into account the cloud roughness as well as the registration error. In addition, for each measured distance, M3C2 provides a spatially variable confidence interval based on registration error and roughness of the point clouds (Lague et al., 2013). In M3C2, normals are first computed based on a specified normal scale, D, for one of the point clouds that is usually the reference point cloud. Normals can be computed either for the entire point cloud or subsampled core points. Suitably selected core points, in the case of very large point clouds, help to save memory and speed up the calculation without affecting the accuracy. For each core point, the computed normal is then projected on each cloud at a projection scale, d. The average position of each cloud in the vicinity of the core point is determined by defining a projection cylinder of length L, and diameter d. The cylinder is centered on the core point, with its axis oriented along the surface normal, and intersects both clouds (Lague et al., 2013). 62 Figure 9: Effect of varying core points sampling distance (dx) on the accuracy of M3C2 distances (in meters) between the reference point cloud (PC-A) and the point cloud generated using GCP configuration E (PC-E): Core points obtained by subsampling PC-A at (a) dx = 0.8 m; (b) dx = 0.5 m; (c) dx = 0.3 m (d) dx = 0.2 m; and (e) dx = 0.1 m. The extent, as well as the distribution, of distances are same for dx = 0.3, 0.2, and 0.1 m. 63 For accuracy assessment, usually a more accurate point cloud, preferably generated by another technique (e.g. LiDAR or InSAR), is used as a reference. To our knowledge, a reference point cloud as dense and accurate as that produced in this study is not available for the study site. Therefore, the bare-earth point cloud (generated in Sect. 4.3) for the most accurate reconstruction (configuration-A) was used as a reference to which the point clouds generated using other GCP configurations were compared. Vegetation causes alignment and registration errors (Sect. 4.5), and in the absence of oblique imagery, it could also cause errors in the vertical construction of the 3D point cloud (Sect. 5.1). Therefore, the point clouds from all GCP configurations were first classified to generate bare-earth (BE) point clouds, and M3C2 comparison was performed on these BE point clouds. In the following text, for simplicity, we call the reference BE point cloud georeferenced with GCP configuration A as PC-A, and the BE point clouds generated using configurations B, C, D, E and F as PC-B, PC-C, PC-D, PC-E, and PC-F, respectively. Each BE point cloud consisted of over 41 million points, which required processing power greater than that available (Sect. 4.3), as the software continuously crashed on multiple attempts. Therefore, first core points were defined by subsampling PC-A with an average spacing, dx, of 0.2m. This spacing was chosen based on a trial and error procedure in which the effect on cloud distances of the variation in dx (0.1, 0.2, 0.3, 0.5 and 0.8 m) was checked (see Figure 9). Although the variation in M3C2 distances for dx ≤ 0.5 m was negligible (>98% of the distances were consistent), dx = 0.2 m was used to reduce computation time by ~3 times without compromising the accuracy. The accuracy of the distance computation in M3C2 depends on the input parameters D, d, and L. D depends on the local roughness of the point cloud, which for the study site varies a lot due to the presence of the green and the dead surface grass, sand, gravel, and roads, etc. D smaller than 64 local roughness resulted in an overestimation of the computed distance, whereas larger D values caused a significant increase in computation time. Therefore, following a trial and error procedure, instead of using a single value for D, a range of values (0.5-10 m with 1 m interval) was selected that provided consistent results without causing a significant increase in computation time. The M3C2 algorithm computes normals at all scales in the range, but only uses that scale for distance computation at which the surface in the vicinity of the core point looks more planar. Selection of d depends on the resolution (i.e. dx) of the two point clouds. Lague et al. (2013) recommend d ≥ 10 dx of the lower resolution point cloud to produce accurate results. Therefore, d = 2m was used. To account for point cloud alignment and co-registration errors, all the point clouds (PC-B to PC-F) were aligned separately to the reference point cloud (PC-A) by picking at least 10 points in those areas of the landslide that had RMSE lower than 0.02m. These points represented corners or the centers of various features on the golf course and Canyon View road. The corresponding registration error in each case was used in distance computations. The M3C2 distance comparisons between PC-A and PC-B to PC-F are shown in Figure 10(a) to (e), respectively, where red squares represent the GCPs used for the respective configurations. Figure 11 shows the spatially variable distance uncertainty for all five cloud comparisons, where all five M3C2 distances (Figure 10) were computed with a confidence interval of over 95%. Both uncertainties, as well as their spatial variation, is very small that confirms the correct selection of parameters used for distance calculations. In all five cloud comparisons in Figure 10, there exists tiny noise (as yellow, dark blue and red dots), which is caused by the non-uniform edges of the holes left in BE point clouds after removing the vegetation. 65 The comparison between PC-A and PC-B is shown in Figure 10(a). RMSE for these two configurations (Sect. 4.1) were close (i.e. ≤ 0.051 m), which is also highlighted by the distances between the two point clouds. Over 95% of the points are within ±0.06 m, as shown by the distribution of the distances on the right side of the scale bar in Figure 10(a). It is important to mention here that RMSE values calculated in Sect. 4.1 were based on the GCPs/CPs position error in orthomosaics and DSMs generated from the point clouds, which do suffer from some projection and smoothing errors (Song and Nan, 2009; Kršák et al., 2016). Therefore, minor differences between the GCP/CP errors and cloud distances is expected. For the flat or slightly undulating terrain on site, increasing the distance between GCPs did not cause an increase in mapping error. There are four light blue patches in Figure 10(a) that correspond to distances greater than -0.063 m between the two point clouds. Ignoring the two patches within the Trout Creek canyon that are outside the main body of the landslide and far from the nearest GCP, only the patches in the center and in the north, are discussed below. In case of the central patch in Figure 10(a), the area is steeply sloping (>25°), has dense tree cover, and a GCP included within that patch for PC-A was not used for PC-B. Although similar conditions exist in other parts of landslides, in all those cases at least two GCPs, one close to the top and the other close to the bottom, of each major slope change were used. The tree cover in this part of the landslide is also very dense (see Figure 8). Also, for this area of the landslide the height of the UAV was more than any other part of the landslide (see Sect. 3.1 and Figure 3), which may have affected the accuracy, especially in the vertical direction, as suggested by Agüera-Vega et al. (2017). It is important to mention here that omitting this GCP in PC-B highlights the effect of slope as well as vegetation on mapping accuracy, which was not predicted by the RMSE values. 66 Therefore, for areas involving multiples slopes covered with vegetation, it is important to cover all slopes with GCPs. The exact reason for relatively higher M3C2 distance for the northern patch is not known. However, the two UAV flight grids (I and II) were not properly overlapped (Figure 3). This northern patch corresponds to the area covered by the top right corner of flight grid I. The two pictures in the top right corner of flight grid-I (Figure 3) suffered more maneuver-related image blurs compared to other flight lines due to unknown reasons. Also, due to some instrumental glitches, the majority of the pictures in flight grid-II (topmost flight line) were not taken by the drone. In addition, compared to PC-A where a GCP was used between flight grids I and II (i.e. within the blue patch), the absence of that GCP in PC-B (along with blurred/disoriented/missing images and relatively low overlap between the grids) might have resulted in higher distances between the two clouds. We support this argument further in the last paragraph of this section (i.e. comparison between PC-A and PC-F). Nevertheless, PC-A and PC-B match well over ~90% of the area they covered. 67 Figure 10: M3C2 distances (in meters) between the reference point cloud (PC-A) and (a) the point cloud georeferenced with GCP configuration B (PC-B); (b) the point cloud georeferenced with GCP configuration C (PC-C); (c) the point cloud georeferenced with GCP configuration D (PC-D); (d) the point cloud georeferenced with GCP configuration E (PC-E); and (e) the point cloud georeferenced with GCP configuration F (PC-F). Plots on the right side of scale bars represent the distribution of distances. 68 Figure 11: M3C2 distance uncertainties at 95% confidence interval for the distance comparison between the reference point cloud (PC-A) and (a) the point cloud georeferenced with GCP configuration B (PC-B); (b) the point cloud georeferenced with GCP configuration C (PC-C); (c) the point cloud georeferenced with GCP configuration D (PC-D); (d) the point cloud georeferenced with GCP configuration E (PC-E); and (e) the point cloud georeferenced with GCP configuration F (PC-F). 69 Figure 10(b) represents the M3C2 distance comparison between PC-A and PC-C. PC-C as mentioned in Sect. 3.4 is georeferenced with a grid-like GCP configuration, which for the current study site was the closest possible to a proper gridded distribution. The distance between the GCPs, indicated by the red squares in Figure 10(b), is more or less uniform. However, in contrast to PC-B, the GCPs in PC-C do not properly cover the multiple slopes onsite, which resulted in higher distances between PC-A and PC-C (i.e. up to +0.16 m and -0.12 m) as indicated by the light blue, yellow, and orange patches. The orange patch corresponds to the highest distance between the two point clouds (i.e. >0.125 m) for which there can be two reasons. First, the altitude of the UAV for this part of the landslide was the highest (Sect. 3.1), and second, this patch represents a valley (two slopes in opposing directions). Thus, in the absence of proper GCP coverage, steeply sloping areas and areas for which the altitude of the UAV was highest, suffered more mapping errors. Figure 10(c) and (d) show the distances between PC-A and PC-D, and PC-A and PC-E, respectively. The shapes of the two GCP configurations are similar; the only difference is one of the GCP from each pair in PC-D is omitted in PC-E. The lateral extent of the distance distributions indicated to the right of the scale bars are almost equal (i.e. over 95% of the points are within -0.10 to +0.27 m). However, surprisingly, compared to PC-E, PC-D with double the number of GCPs suffered from a higher error in certain parts of the landslide. This is, however, in agreement with the RMSE calculated in Sect. 4.1, where RMSE values for PC-E (i.e. 0.066, 0.053, 0.111 m) were a little lower than for PC-D (i.e. 0.070, 0.053, 0.115). Nonetheless, as it was the case with PC-C, in both PC-D and PC-E the slopes were not properly covered with GCPs, which resulted in highest mapping errors indicated by the blue, yellow and orange patches in Figure 10(c). A key point to mention here is that even though PC-E suffered from a lesser overall error compared to 70 PC-D, but this is due to the relatively lower errors in the flat to undulating parts of the landslide. In case of steeply to extremely steeply sloping areas of the landslide, PC-E suffered more error than PC-D. Finally, the distance comparison between PC-A and PC-F is shown in Figure 10(e). In case of flat or slightly undulating areas, 5 GCPs might have been enough to provide an accuracy of under ± 0.125 m. But for sloping areas, PC-F suffered from the highest mapping errors (i.e. -0.5 to +0.375 m) of all five distance comparisons. This type of configuration may be enough for initial reconnaissance during or after a disastrous landslide event. However, for monitoring purposes, especially in the case of slow-moving landslides, such a configuration of GCPs will not serve the purpose. If a detailed field geodetic survey that would properly cover the landslide is not possible due to safety concerns, direct georeferencing (Turner et al., 2014; Hugenholtz et al., 2016) is advised. Another important point also discussed above in relation to Figure 10(a), is the presence of dark blue elongated patch in the upper middle portion of Figure 10(e). This is the area between flight grids I and II that do not properly overlap (Figure 3). Although the topography of the area varies, in the absence of GCPs on the edges of (or in between) the two adjacent flight grids, PC-F suffered from highest error than other four cloud comparisons in Figure 10. This problem does not exist at other edges of the flight grids where they are overlapped by at least 1.5-2 flight strips. Therefore, if for any unforeseen reasons in the field (low battery power, high wind speeds, GCP signal error, etc.), the adjacent flight grids do not properly overlap, it is recommended to include a sufficient number (dependent upon the topography) of GCPs at the edges of the flight grids to reduce mapping errors. 71 PC-D, PC-E and PC-F (Figure 10(c), (d) and (e), respectively) also suffered from varying degrees of the ‘bowl’ effect where the edges of the two point clouds are warped up. This kind of effect can occur when the images are captured in nadir (near-parallel) mode and if the number (i.e. density) of GCPs is not enough (James and Robson, 2014; Peppa et al., 2016). Both of these reasons are valid in the case of PC-D, PC-E, and PC-F. All UAV images were taken in nadir mode in parallel flight lines and as the distance between the GCPs increased, the bowl effect also increased. Therefore, based on these results, it is either recommended to place a sufficient number of GCPs for UAV surveys or if, for safety or economy reasons it is not possible to place enough GCPs onsite, the UAV flight lines should not be kept parallel. 4.3 Change detection The UAV images obtained in July 2017 was processed using the same GCP configuration that provided the highest accuracy for the July 2016 point cloud (i.e. Configuration-A; see Sect. 4.1). The RMSE values for the July 2017 point cloud were calculated using the same workflow as described in Sect. 4.1. The RMSE values in X, Y, and Z directions, respectively, were 0.059, 0.042, and 0.062 m for the July 2017 point cloud compared to 0.042, 0.040, and 0.044 m for July 2016 point cloud. Thus, the accuracy of July 2017 point cloud was 1 to 2 cm poorer than the July 2016 point cloud. In order to detect the 3D changes in natural terrain between July 2016 and July 2017, the point clouds from the two years were first filtered using Cloth Simulation Filter (Zhang et al., 2016) to obtain BE points clouds. A ground offset distance of 0.3 m was used to cut off the vegetation from the ground points. The purpose of classification was to avoid errors in co-registration induced by the variation in surface vegetation. Since the two point clouds were constructed and georeferenced 72 using separate workflows, co-registration is a necessary step prior to cloud comparison to avoid mis-alignments and rotational or scale differences. In CloudCompare (v2.8), the two point clouds were first masked to ensure they fully overlapped. Second, those parts that fall outside the active landslide area were segmented from both point clouds. These non-active parts were then co-registered using the Iterative Closest Point (ICP) algorithm to obtain a transformation matrix. Finally, this transformation matrix was applied to the July 2017 point cloud (aligned) to co-register it with the July 2016 point cloud (reference). Once the point clouds were co-registered, they were compared using the M3C2 algorithm with the same workflow as described in Sect. 4.2 to compute the 3D distances between them. The result of the M3C2 cloud comparison is shown in Figure 12. The 3D displacement vectors at each of the fixed GCP (survey pins and permanent onsite features; see Sect. 3.2) computed from its 2016 and 2017 positions measured with RTK-GNSS receiver are also shown on top of the cloud comparison diagram. Figure 13 shows the profile transected at section A-A’ in Figure 12. For better understanding, a zoomed overview of the toe part of this profile is shown in Figure 14. It can be seen from Figure 12, Figure 13 and Figure 14 that the majority of the landslide movement over the 1-year period occurred at the toe region (black dashed-line polygon in Figure 12), where the cloud-distances vary from ±0.4 m to over ±1.0 m. This movement is verified by the corresponding displacement vectors at the GCPs. However, there are certain parts in the west and north-west of the landslide (encircled with red dashed-line polygons in Figure 12) where the M3C2 distances show a movement in the range of +0.25 to over +0.50 m (light to bright yellow color pixels) and -0.25 to -0.50 m (marine to aqua blue color pixels). However, this part of the landslide did not actually move by this much amount 73 in the 1-year period from July 2016 to July 2017. The reason for differing displacement vectors and the higher M3C2 is that in July 2017 the surface grass on the golf course in the west as well as the crops in the fields in the north-west were taller and denser in some areas (light to bright yellow pixels) and shorter in others (marine to aqua blue) than July 2016. The UAV camera can only capture the scene lit by ambient light (i.e. it cannot see the ground below dense grass or the crops). Thus, in the absence of any actual ground points below the grass/crops in the point cloud, the point cloud classification algorithm included all points within 0.3 m (ground offset distance) from the tops of the grass/crops in these parts in BE point clouds by incorrectly classifying them as ground points. Therefore, areas where the natural terrain is covered by dense surface vegetation, the UAV-SfM technique will not record the actual ground, and the variation in surface vegetation will induce errors in change detection even after vegetation filtering. Another important point to mention here is that in Figure 12, some of the displacement vectors/values other than the toe region of the landslide indicate movements above 0.10 m. However, as mentioned in Sect. 3.2, the average accuracy of GCPs measured with RTK GNSS receiver varied between 5-10 cm depending upon the signal between the receiver's base and the rover. Therefore, looking at the other displacement vectors/values in the northern and western parts of the landslide, it is safe to assume that these areas did not move by more than 0.05 m. In contrast, the higher movement at the toe of the landslide could be related to two things: first, the groundwater table in the toe region is high as indicated by the discharging groundwater table, and second, the depth to the bedrock at the toe is shallow and slip surface could be at the soil/rock interface, which is also mentioned by Riglin (1977). 74 Figure 12: The M3C2 distance map indicating the 3D movement between July 2016 and July 2017 point clouds. The circular dots in pink colour are the fixed ground control points (GCPs). Also, shown are the magnitude and the direction of the displacement vectors at each GCP computed from the 2016 and 2017 RTK-GNSS measurements. All dimensions are in meters. 75 Figure 13: Profile of the section A-A’ in Figure 12. Zoomed overview of the encircled part of the profile is shown in Figure 14. 76 Figure 14: Zoomed overview of the encircled part of the profile A-A’ in Figure 13. 77 Chapter 5: Results and Discussion – Part-II: Classification Accuracy Assessment 5.1 Qualitative evaluation Qualitative performance evaluation of the filtering algorithms was done based on the approach used by Sithole and Vosselman (2004). BE and OBJ point clouds generated by each algorithm were visually compared with the reference point clouds to check overall agreement, as well as locate Type-I and Type-II errors associated with difficult-to-filter features. The results of the qualitative assessment for difficult-to-filter features are summarized in Table 4. Figure 15 shows BE point clouds (b-f) and OBJ point clouds (h-l) from the five tested algorithms illustrating overall Type-II (red) and Type-I (green) errors, respectively. Hill-shades of the DEMs interpolated from BE point cloud from each algorithm are also shown in Figure 16 to highlight the smoothness (green rectangles) and granularity (red rectangles) effects caused by Type-I and Type-II errors, respectively. It should be noted that these effects occurred for all algorithms at the same locations but to varying degrees. To avoid congestion in Figure 16, the smoothness/granularity highlighted in one hill-shade by a green/red rectangle is not highlighted in other hill-shades. 78 Table 4: Qualitative performance of the tested filtering algorithms Feature Associated error CANUPO CSF LASground MCC FUSION Vegetation Very low veg (on slope) Type-II * * * * * Dense veg (overall) Type-II *** *** *** *** *** Dense veg (on slope) Type-II *** ** ** ** *** Object Complexity Very low objects (car/container) Type-II * * * * *** Large objects (building/house) Type-II * * * * * Discontinuity Sharp ridges (erosion gullies/road dips) Type-I *** ** ** ** * Steep slopes Type-I *** *** *** *** * Cliffs Type-I ** * * * * Surface roughness Eroded surfaces (toe) Type-I *** ** ** ** * Fine grassy surfaces (golf course) Type-I *** *** *** ** * *Poor (feature filtered <50%); **Fair (feature filtered 50-90%); ***Good (feature filtered >90%). 79 Figure 15: Top: (a) Reference BE point cloud and Type-II (red) errors caused by (b) CANUPO, (c) CSF, (d) LASground, (e) MCC-LiDAR and (f) FUSION. Bottom: (g) Reference OBJ point cloud and Type-I (green) errors caused by (h) CANUPO, (i) CSF, (j) LASground, (k) MCC-LiDAR and (l) FUSION. Numerical symbols 1-5 and 6-8 point to Type-II and Type-I errors, respectively, caused by different features. 80 It can be observed from Figure 15 and Figure 16 that all five algorithms removed most of the non-ground points, but there exist a few discrepancies in classification. For example, in all classifications except CANUPO [Figure 15(c-f)], the walls (vertical features) of the houses at the northern and western parts of the landslide have been correctly separated into non-ground points, but the roofs (flat/horizontal features) of the houses (marked by numerical symbol ‘1’ in Figure 2 2 2 4 3 4 Figure 16: Hill shades of digital elevation models interpolated from bare-earth point clouds for (a) reference bare-earth, (b) CANUPO, (c) CSF, (d) LASground, (e) MCC-LiDAR and (f) FUSION. Green and red rectangles highlight areas with Type-I and Type II errors, respectively. Blue rectangles and their corresponding zoomed overviews show the difficulty experienced by all five filtering algorithms for a densely vegetated steep slope where few ground points captured under trees resulted in increased granularity (Type-II error). 81 15(c-f) and corresponding red dashed-line rectangles in Figure 16) were not. For CSF and LASground, this is due to the relatively small value of the resolution parameter chosen for an initial ground estimate to account for steep slopes that covered most of the study site. A lower resolution allows the surface to bulge into the buildings from the bottom and contact the roofs. Since the roofs are mostly flat, the threshold parameters used for classification fail to filter them out. For MCC, features with abrupt edge changes (e.g. buildings, discontinuities) do not surpass the positive curvature threshold parameter that separates non-ground points. For CANUPO (Figure 15(b) and corresponding symbol ‘1’ in Figure 16.), neither the walls nor the roofs of the houses were separated from the ground points. The OBJ class used for training the classifier contained two buildings, and larger scales of 5, 10 and 15 m were specifically chosen to remove buildings. However, since the geometry of the roof/walls is very similar to the ground (i.e. 2D), CANUPO failed to differentiate between the two. Similarly, all filters failed to properly remove very low objects (i.e. cars, boats, storage containers, logs) and very low vegetation (grass/shrubs under 0.3 m in height), marked by ‘2’ and ‘3’ in Figure 15. In addition, filters also failed to remove a few outlier points corresponding to dense vegetation (mainly disconnected/isolated leaves) in the middle and upper middle parts of the site (refer to ‘4’ in Figure 15), resulting in scattered granularity (raised bumps) in the DEM hill-shades (Figure 16). All five filters also struggled in densely vegetated extremely steep slopes in the southern part, where incorrectly classified vegetation points resulted in huge bumps in the DEM hill-shades (see ‘5’ in Figure 15; blue rectangles and their overviews in Figure 16). The most significant difference between the five classification results occurs at the very steep (~70°) toe region of the landslide (see ‘6’ in Figure 15), where the ground surface roughness is 82 also highest due to erosion by water. Comparing Type-I errors in Figure 15(h-l), it can be said that CANUPO produced best results. In this region, the density of Type-I error varies from lowest for CANUPO [Figure 15(h)] to highest for FUSION [Figure 15(l)]. This is because CANUPO is based on scale-dependent dimensionality that is insensitive to the local cloud roughness, provided a set of scales covering the roughness is used. Similarly, CANUPO also managed to preserve the discontinuities to a great extent (~99%) in other steep parts of the site (marked by ‘7’ in Figure 15). For CSF and LASground, sharp spikes (crests and troughs) of the erosion gullies do not allow the cloth or the TIN surface to come into proper contact with the ground points resulting in their incorrect classification into non-ground points. Increasing the threshold distance from 0.3 to 0.5 m can considerably improve the results in the toe part but will also include very low vegetation in other parts of the site in ground points. MCC produced slightly better results than FUSION because of its TPS interpolation. However, threshold parameters (curvature in MCC and weighting function in FUSION) resulted in chopping off of the sharp erosion spikes. Additionally, in the northern and western parts of the site, MCC caused trimming of ground points (refer to ‘8’ in Figure 15) representing the grass on the golf course and in the backyards of houses. The number of ground points incorrectly classified as non-ground points by these algorithms is only a small fraction of the total number of ground points in the reference point cloud because of very high density (i.e. average point spacing = 4 cm) of the original UAV-SfM point cloud. Therefore, the over-smoothing caused by the reduction of ground points (Type-I error) is not noticeable in the DEM hill-shade at scales shown in Figure 16. 83 5.2 Quantitative evaluation For each filtering algorithm, several combinations of parameters were tested, and those that provided optimum classification results (i.e. lowest overall error and highest K values) were used in this study. The results of the quantitative performance of each filtering algorithm for classification of UAV-SfM point cloud are summarized in Table 5, along with optimum parameters. The percentage of Type-I, Type-II, and total errors range from 1 to 23, 2 to 15, and 4 to 18, respectively. MCC and CSF provided lowest overall error (4% each) followed by LASground and FUSION (5% each). CANUPO resulted in the highest total error (6%). All surface-based methods (i.e. MCC, CSF LASground, and FUSION) provided the lowest Type-I errors of 1%, 2%, 3% and 2% but also gave the highest Type-II errors of 15%, 10%, 14% and 12%, respectively. This strong bias towards Type-II error is due to two reasons. First, although the filter parameters were selected to produce the lowest total error, due to a high proportion of ground points to non-ground points (i.e. 42:12), a relatively small increase in the number of non-ground points wrongly classified into ground points resulted in a significant increase in Type-II error. Second, most surface-based methods tend to minimize Type-I errors rather than Type-II errors since they are less blind to the context of BE relative to above-surface features (Sithole and Vosselman, 2004). 84 Table 5: Quantitative comparison of the performances of the tested algorithms for the classification of UAV-SfM point cloud In terms of avoiding a significant bias towards Type-I or Type-II errors, as well as keeping the total error low, CANUPO provided relatively balanced results with Type-I, Type-II, and Total errors of 6%, 8%, and 6%, respectively. However, a higher Type-I error is associated with the edge artifacts (see the circumferential and horizontal lines in Figure 15(h)) experienced by the algorithm at the perimeter of the tiles (see Sect. 3.5.1). These incorrectly classified ground points are associated with low point density near edges and resulting lack of dimensionality characterization at small scales (Brodu and Lague, 2012). Although the cost of each error is application specific, minimizing Type-I error is potentially advantageous for post handling since in most cases manual treatment of Type-II errors is a lot easier than Type-I errors (Sithole and Vosselman, 2004; Zhang and Lin, 2013). The values of K for all algorithms are above 0.8 indicating high inter-rater agreement, thus eliminating the agreement by chance. Considering, both the kappa index and overall error, the CSF filter provided the most accurate classification closely followed by MCC. Filtering algorithm Theoretical basis Parameters used Type-I Error (%) Type-II Error (%) Total Error (%) Cohen's K CANUPO Multiscale dimensionality Scales: 0.05 0.1 0.15 0.2 0.25 0.5 0.75 1 1.25 2 3 4 5 15 m; ba-98, fdr-13 6 8 6 0.82 CSF Cloth simulation Cloth resolution = 0.2 m; offset = 0.3 m; iterations = 1000 2 10 4 0.88 LASground Progressive TIN densification TIN resolution/step = 0.75 m; bulge = 1.5, spike = 1.5 m; offset = 0.3 m 3 14 5 0.84 MCC Multiscale curvature and spline interpolation Scale, s = 0.8 m; curvature, t = 0.7 m 1 15 4 0.87 FUSION Weighted linear least-square interpolation g = -0.5; w = 1; offset = 0.3 m; resolution/cell size = 1 m; iterations = 10 2 12 5 0.87 85 The results prove that surface-based methods provide lowest errors as concluded by Sithole and Vosselman (2004) and Meng et al. (2010). However, it is vital to mention that LASground, MCC, and FUSION were highly sensitive to the parameters. A small increment/decrement in any single parameter can significantly change the classification results. Therefore, finding an optimum combination of parameters in these algorithms can be very strenuous for a complex site. In contrast, CSF is the simplest to use; four out of five required parameters (i.e. terrain type, steep slope processing, the number of iterations, and ground offset) are subjective and are very easy to define; and the optimum value of the fifth parameter, namely cloth resolution, can be found relatively easily. In other words, CSF not only provided the best classification results, it is also very easy to use. It is also worth mentioning that the errors (particularly Type-I) obtained in this study are lower than those reported in previous comparison studies on LiDAR data for mountainous terrains (Sithole and Vosselman, 2004; Zhang and Whitman, 2005; Montealegre et al., 2015; Polat and Uysal, 2015) and on UAV-SfM PC for flat areas (Serifoglu Yilmaz and Gungor, 2016). The lower errors not only justify our selection of the filtering algorithms but also highlight the importance of the selection of the optimum input parameters for each algorithm. The influence of terrain slope, vegetation density, point cloud density, and difficult-to-filter features on accuracy are presented in the following subsections. 5.2.1 Terrain slope The fundamental assumption employed in most filtering algorithms is that non-ground points are significantly above nearby ground points. This assumption is applicable in flat to moderately sloping terrains (slope < 15°), but it does not work well for steep slopes (Sithole and Vosselman, 2004). For complex scenes involving multiple slopes, the factors that control the selection of the 86 optimum filter parameters are the maximum gradient and the spatial extent of steep terrain relative to flat or moderately sloping areas. Based on these factors, three slope categories were used to evaluate filter performances: flat to moderate slope (0-15°), steep slope (15°-30°) and very steep slope (>30°). The effect of terrain slope on accuracy metrics is shown in Figure 17. Both Type-I and Type-II errors increase with increasing slope. However, the increase in Type-II error is more pronounced. While total error for very steep slopes is lower than that for steep slopes, this is due to the lower ratio of non-ground points to ground points on very steep slopes. For CANUPO, the errors for flat to moderate slope areas are higher than that for steep slopes. However, this is due to high proportion of edge artifacts experienced by the algorithm (see Sect. 5.2) in flat areas. Overall, the results obtained in this study are similar to those reported by Montealegre et al. (2015). The detailed calculations for classification errors in Figure 17 are included in Appendix B.1. 87 Figure 17: Effect of variation in terrain slope on performances of the filtering algorithms 88 5.2.2 Vegetation density One speculation for this study was that the relatively lower density of ground points from a UAV-SfM PC representing steep slopes covered with dense vegetation may create additional problems during classification. Figure 18 shows the effect of vegetation density on filter performances. Type-I, Type-II, and Total errors are all higher in areas with dense tree cover (>40 trees per hectare) compared to areas with sparse tree cover (<20 trees per hectare). Also, the increase in errors due to increased vegetation density is more pronounced for steep terrain (slope >15°) than for flat/undulating terrain (slope <15°). In addition, the increase in Type-I error is slightly more than that in Type-II errors. For CANUPO, fewer ground points mean more discontinuities or offsets in the ground surface and lack of dimensionality characteristics at smaller scales (Brodu and Lague, 2012). In other words, it means that because of wider spaced ground points, small scales do not contribute to classifying them into a 2D plane (i.e., the ground surface). At large scales these ground points appear more like a 3D feature (non-ground points), especially in steep slopes, leading to high Type-I errors. Surface-based methods suffer from errors in two stages: First, depending on the step/resolution parameter used for initial surface estimation, fewer ground points under vegetation on slopes can lead to over-smoothing or granularity based on the interpolation type. Second, lower context due to low GP count relative to non-ground points makes the parameters controlling the densification (i.e. inclusion of further ground points) practically less efficient (Sithole and Vosselman, 2004). 89 Figure 18 shows that MCC is least affected by the effect of vegetation density. A possible reason could be the use of TPS interpolation that allows tension adjustment between points and thus controls the distance at which sample points affect the estimated surface (Evans and Hudak, 2007). FUSION and LASground suffered from relatively higher Type-I errors under dense vegetation. FUSION is based on linear least-squares interpolation, which is less accurate and unlike MCC does not take into account the tension between points, thus leading to excessive over-smoothing. For LASground, TIN can handle the variation in point density very well in flat or relief terrain but not in steep areas as the triangles are planar, thus leading to increased Type-I error (Zhang and Lin, 2013). The detailed calculations for classification errors in Figure 18 are included in Appendix B.2. Figure 18: Effect of vegetation density on the performances of the filtering algorithms in flat to undulating (slope <15°) and steep (slope >15°) areas. 90 5.2.3 Point cloud density The original point cloud with 4 cm average spacing between the points was thinned to 10, 20 and 50 cm point spacing in order to assess the effect of variation in point density on classification accuracy. For each point spacing, different parameters were selected through sensitivity analysis to achieve optimum results. The results are shown in Figure 19. In general, Type-I errors and Total errors for all algorithms increased with decreasing point density (or increasing point spacing). Type-II errors, however, decreased with decreasing point density except for 50 cm point spacing where CANUPO, CSF, and LASground resulted in slightly higher Type-II errors than those obtained for 30 cm point spacing. This is in contrast with the results reported by Evans and Hudak (2007) and Montealegre et al. (2015) for LiDAR data where Type-II errors increased with decreasing point density. However, the density of LiDAR datasets in these studies was lower (>1 m point spacing) than the lowest density (i.e. 50 cm point spacing) used in this study. An increase in Type-II errors with a decreasing point density of a UAV-SfM PC is, however, possible for steep areas covered with dense vegetation (see Figure 18). A significantly lower GP density under vegetation not only resulted in higher Type-I and Type-II errors (Figure 18) but also caused a more pronounced effect of Type-II errors on the DEM hillshades (see increased granularity in zoomed overviews in Figure 16). The smoothness effect of high Type-I errors exists but is not very noticeable in DEM hillshades at the scale shown in Figure 16. Overall, as the point density decreased, distinguishing between ground points and non-ground points became difficult for the filters, which is in accordance with the comparison study performed on LiDAR point clouds by Sithole and Vosselman (2004). The results in Figure 19 are, however, in contrast with those obtained by Serifoglu Yilmaz and Gungor (2016) where the performance of 91 their filters increased with the decreasing point density of the UAV-SfM PCs. The detailed calculations for classification errors in Figure 19 are included in Appendix B.3. 5.2.4 Difficult-to-filter features Figure 20 shows the error and kappa values for the features that pose the greatest difficulty in filtering for any algorithm, as highlighted by Sithole and Vosselman (2004). In cases of low objects and very low vegetation (<0.5 m high), all algorithms except FUSION produced very high (>50%) Type-II errors. This can be for two reasons. First, distinguishing between very low features, Figure 19: Effect of point cloud density (i.e. spacing between points) on filter performances. 92 especially on slopes, is difficult for any algorithm because of their lower height difference (see Sect. 5.2.1). Second, in the case of complex terrains, differentiation between very low features and the ground is further complicated by the requirement to preserve sharp changes in the natural terrain (Sithole and Vosselman, 2004). For instance, in the case of CSF and LASground, this requirement was satisfied by specifying the higher surface resolution and ground offset (see Table 5). However, very low density under vegetation or absolute lack of points under solid objects allowed the interpolated surface to bulge up and come near or in contact with non-ground points. With a ground offset of 0.3 m, all non-ground points within this distance of the interpolated surface were incorrectly classified as ground points, resulting in very large Type-II errors. For the study site, it did not produce a significant effect on Total error as the number of points corresponding to low vegetation and low objects were fewer compared to ground points. However, for complex sites involving both flat and steep terrains and with a significant proportion of low-lying features, large Type-II error and hence large Total error can occur. In such cases, a lower surface resolution and ground offset will help to reduce Type-II error and its effect on Total error. The purpose of multiple scales (i.e. s - 0.5 m, s, s + 0.5 m) in the MCC algorithm is to account for the variation in the topology of non-ground points features in a point cloud (Evans and Hudak, 2007). However, just like CSF and LASground, the value of the scale (or resolution) and curvature used for the study site was kept low (s = 0.8, t = 0.7) to preserve the sharp features and discontinuities in natural terrain, which led to high Type-II errors. End users can choose the values of resolution/scale and offset/curvature parameters to reduce either Type-I or Type-II errors based on their needs. When trying to minimize Type-I error in mountainous terrains, some modulations are to be expected in DEMs due to understory vegetation 93 and ground debris (Zhang et al., 2003). CANUPO, which uses the geometric shape of features at multiple scales, successfully removed most low objects of small (storage boxes) to medium scale (cars) but failed to filter low vegetation. A plausible reason for this could be that the points corresponding to very low vegetation were sparse compared to the ground surface (i.e., the density of ground points was an order of magnitude higher than non-ground points representing sparse low vegetation). At large scales, these sparse non-ground points may be considered a part of the 2D plane that is the ground surface. None of the algorithms were successful in filtering buildings/houses (Figure 15, Figure 16 and Figure 20). The geometry of roofs and walls of the buildings was similar to the ground surface (i.e. 2D) for CANUPO. Whereas, for surface-based methods, the absence of ground points under the buildings in UAV-SfM PC, upward bulging of the interpolated surface caused by its low resolution/scale along with nearly flat roofs led to high Type-II errors. Further explanation can be found in Sect. 5.1. In addition, all algorithms failed in preserving the discontinuities in the case of vertical/near-vertical natural terrain leading to high Type-I and Total errors (Figure 20). The detailed calculations for classification errors in Figure 20 are included in Appendix B.4. 94 5.3 Guidelines for input parameters The accuracy of classification depends on the input parameters. In general, all input parameters affect the filtering. However, it was noted that some filters are more sensitive to input parameters than others. Also, for each algorithm, the filtering process is more sensitive to one or two controlling parameters than the others. The way these parameters affect Type-I or Type-II errors is described in previous sections. A small change in the input parameter can cause a significant change in overall classification accuracy. The optimum values of these parameters for the study site are included in Table 5. Figure 20: Effect of difficult-to-filter features on filter performances. 95 The classification accuracy of CANUPO depends upon the robustness of the trained classifier. When the manually separated BE and OBJ classes are accurate for various features, then the class separability of actual point cloud will perform better. Accuracy is equally dependent on the range of scales used. Usually, a range of scale can be provided but the computation time increases as the number and values of scales increase (Brodu and Lague, 2012). Therefore, users are advised to get a sense of the geometry of different features (min and max lengths) encountered on site through a georeferenced photograph or the actual point cloud and specify the scale range accordingly. In CSF, the most sensitive parameters are the cloth resolution and the type of terrain. The type of terrain is subjective and easy to specify. The cloth resolution should be below 0.3 for steeply sloping terrain, 0.3 to 0.7 for moderately sloping terrain, and above 0.7 for flat to gentle areas. For complex sites, increasing cloth resolution in CSF helps to significantly reduce Type-I error related to very steep slopes, eroded surfaces, sharp crests, and cliffs. However, at the same time, it leads to increasing Type-II error in planar to undulating areas by accepting flat object (e.g. cars and building roofs) as the ground points. The LASground classification is greatly affected by the step and bulge parameters. For steep terrain, a general recommendation for the step parameter is to start with twice the average resolution of the point cloud (in this study, optimum step = 0.75 m for 0.4 m resolution point cloud) and move up or down by half of the point cloud resolution (i.e. 0.2 m for current point cloud). A value of bulge between 1 and 2 is usually adequate to preserve most sharp edges and discontinuities. For flat terrain, the built-in options like “city,” “metropolis,” or “town” produce satisfactory results. 96 The MCC classification requires only two parameters, and both are highly sensitive. Like LASground, it is recommended to start with a scale at twice the resolution of the point cloud and move up or down by half of the point cloud resolution. The value of curvature depends on the topology of features onsite, and for novice users, it can be hard to come up with a curvature value that covers all features. For complex sites, like the study site, a starting curvature value of 0.5 is recommended as it will filter most vegetation points without chopping off the discontinuities. However, features with abrupt edge change (i.e. buildings and cars) do not surpass this curvature value and will not get separated (Evans and Hudak, 2007). Therefore, there will be a trade-off between preserving discontinuities or separating buildings/cars. In the case of FUSION, the filtering is more influenced by the w-parameter than the g-parameter. The values of g = -2.5 and w = 2 recommended in the FUSION manual might be adequate for flat to undulating terrains but not for the very steep slopes encountered in the current study site. For steep terrain, as a general rule, the w-parameter should be kept small so that in each iteration, only the points lying in the proximity of interpolated surface can influence its shape. Starting values of g = -0.5 and w = 1 are recommended, and users can move up in increments of 0.25. Default values of a = 1 and b = 4 will be adequate for most sites. However, in some complex cases the b-parameter can be increased to 6 or 8 to make the weight function slightly steep (see Eq. 3-1). Doing so will compensate the requirement to preserve the discontinuities without affecting the separation of non-ground points. Just like LASground and MCC, a surface resolution (or cell size) double the average point cloud resolution will provide satisfactory results. Determining the optimum combination of parameters for classification of complex environments in FUSION is hardest compared to the other four algorithms. 97 5.4 Manual input, computing time and resources The time taken by each filter to classify the UAV-SfM PC are shown in Table 6. For each filter, the classification time was recorded once the optimum input parameters were finalized. CANUPO, MCC, LASground, as well as FUSION, required manual preprocessing to avoid memory and computing power issues. The computer system used in this study consisted of Intel® Xeon® E5-1607 v3 CPU with 16 cores @ 3.1 GHz, 64GB RAM, and Nvidia Quadro K2200 graphics card. On this computer, none of the four algorithms could handle more than 25 million points. Therefore, for CANUPO, MCC and FUSION, the entire UAV-SfM PC (~53.4 million points) was tiled into four parts (each with <15 million points) before classifying them in a batch process. In CANUPO, a significant amount of time was also spent on the training phase to build an appropriate classifier., This involved manual separation of BE and Veg from a part of the entire UAV-SfM PC. In order to avoid noise due to an upper limit on the number of points (i.e. 1.5 million) in the free version of LASground, the entire point cloud was first subdivided into 51 tiles with a suitable buffer using LAStile (included in LAStools (2016)). The tiles were then classified in LASground in a batch process and finally merged together. In contrast, CSF did not require any preprocessing (tiling or classifier training) and was successfully employed on the entire point cloud. CSF was also the quickest of the three algorithms in terms of computation time. It took just 35 mins (0.6 hrs) to classify the entire point cloud. In contrast, CANUPO took over 13 hrs, LASground and MCC took 1.4 hrs each, and FUSION required 1.65 hrs. 98 Table 6: Computation times taken by the filters for the classification of UAV-SfM point cloud Filtering Algorithm Time (hrs) taken for Pre-processing Classification Total CANUPO Training = 3 Tiling= 0.15 10.00 13.15 CSF 0 0.60 0.60 LASground 0.40 1.00 1.40 MCC 0.15 1.25 1.40 FUSION 0.15 1.50 1.65 99 Chapter 6: Conclusions and Recommendations 6.1 Accuracy of UAV-SFM based mapping In this study, a low-cost off-the-shelf, quadrotor UAV equipped with a 12.4-megapixel digital camera was used to map and monitor the slow-moving Perpetual landslide in Summerland, BC, Canada. Two campaigns of UAV aerial surveys along with field geodetic measurements were conducted, first in July 2016 and second in July 2017. For each campaign, the UAV acquired aerial imagery was processed in Pix4D Mapper, a commercial SfM-MVS based software package, to generate 3D point clouds. The point cloud produced from the July 2016 campaign contained ~53.4 million points at a resolution of 4 cm covering a total area of 0.66 km2, whereas that from the July 2017 campaign contained ~52.02 million points at 3.7 cm resolution and covered a total area of 0.41 km2. GCPs measured during the field geodetic surveys with RTK-GNSS were used to georeference the point cloud to a real-world coordinate system. In order to check the effect of the number and distribution of GCPs on the mapping accuracy, six different configurations of the GCPs were tested on the point cloud constructed from the July 2016 UAV survey. The RMSE errors in X, Y, XY, and Z directions were calculated by subtracting the 3D coordinates of the GCPs read from the orthomosaic (for X and Y) and the DEM (for Z) from their location measured in the field by RTK GNSS. The accuracy analysis revealed that the mapping accuracies are significantly affected by both the number and distribution of GCPs. Both horizontal and vertical accuracies increased with increasing number of GCPs. However, if some of the GCPs are placed near major breaks in the slope to account for the topography of the site, the 100 number of GCPs can be reduced by ~30% while maintaining minimum horizontal and vertical accuracies of 0.057 m and 0.051m, respectively. A cloud to cloud distance comparison technique, called M3C2, was used to check the relative accuracy between the point clouds georeferenced with the six GCP configurations, as well as to comprehensively assess the effect of topography and vegetation on the mapping error. The M3C2 comparison revealed that a reduction in the number of GCPs induced more errors in steeply sloping areas compared to the flat or slightly undulating terrains. Also, in the absence of GCPs, sloping areas farthest from the camera (UAV at the highest elevation) are likely to suffer from the greatest mapping error, especially in the vertical direction. Cloud comparison also revealed the importance of properly overlapped flight grids. Gaps between the two adjacent flight grids will induce significant errors in reconstruction. In the absence of a proper overlap between two adjacent flight grids, GCPs at or close to non-overlapping edges should be included to preserve mapping accuracy. M3C2 distance comparison also highlighted the bowl effect caused by an insufficient number of GCPs in three out of the six GCP configurations. Since all UAV images were taken in near- parallel (nadir) mode, as the distance between the GCPs increased, the bowl effect also increased. Therefore, based on the results of this research, it is either recommended to place a sufficient number of GCPs for UAV surveys or if, for safety or economy reasons it is not possible to place enough GCPs onsite, the UAV flight lines should not be kept parallel. It is also important to highlight that it was not possible to place GCPs in certain parts of the landslide (middle-eastern portion due to dense tree cover and the very steep slope at the toe). Since 101 the accuracy of the mapping with UAV-SfM depends on the number as well as the configuration of the GCPs, these parts of the landslide may have suffered from larger errors. However, in the absence of a point cloud or DEM from another accurate technique such as LiDAR or InSAR, it was not possible to verify the accuracy of the UAV-SfM construction specifically for these parts of the landslide. Therefore, in cases where indirect georeferencing (GCPs) is not possible, it is recommended to use a UAV with onboard RTK GNSS capability to ensure higher mapping accuracies without compromising the safety of the personnel. Considering the RMSE values of 0.047 m, 0.033 m, 0.057 m and 0.051 m in X, Y, XY, and Z directions obtained using 20 GCPs (configuration B), the horizontal and vertical accuracies at the 95% confidence level as per the American Society for Photogrammetry and Remote Sensing (ASPRS) latest positional accuracy standards for digital geospatial data (ASPRS, 2014) are 0.122 m and 0.098 m, respectively. To our knowledge, the perpetual landslide has not been mapped with an accuracy as high as obtained in this study. As per ASPRS 1990 map standards, the equivalent Class 1 and Class 2 map scales will be 1:200 and 1:00 and the equivalent Class 1 and Class 2 contour intervals will be 0.15 m and 0.075 m, respectively. In the context of practical application and suitability of UAV for mapping purposes, the results obtained in this study show that UAV-SfM derived point clouds and associated products (orthomosaics, DSMs, and DEMs) can be used to accurately map large landslides at a cost significantly lower than conventional techniques, which is in agreement with the findings and recommendations of previous UAV studies (Turner et al., 2015; Peppa et al., 2016; Peternel et al., 2017). The 3D change in the natural terrain over a 1-year period from July 2016 to July 2017 was also measured by comparing two multi-temporal point clouds of the landslide using the M3C2 cloud 102 comparison technique. The cloud comparison showed movements in the range of ±0.4 m to over 1 m at the toe of the landslide. These values were confirmed through the displacement vectors computed using the July 2016 and July 2017 positions of the permanent GCPs installed onsite. The 3D positions of the GCPs were measured with an RTK GNSS system. For accurate detection of the 3D movement, filtering of vegetation and other non-ground points from the point clouds, as well as, the proper co-registration and alignment of the two point clouds, is necessary. An inherent limitation using UAV-SfM technique for mapping is that the camera sensor cannot penetrate vegetation. Therefore, for areas where the natural ground surface is covered with thick vegetation (such as dense grass, bush or crop cover), the UAV-SfM point cloud does not produce a true DEM (surface representing the natural terrain), even when the vegetation is filtered using a point cloud classification algorithm. In such cases, variation in surface vegetation can cause errors in the cloud or DEM comparisons. Such vegetation points can be manually removed. However, this will result in large holes in the BE point clouds. The cloud comparison will not provide any information about the change corresponding to those holes. The DEMs generated using such BE point clouds will also suffer from interpolation errors in case the natural ground is not flat. Therefore, the use of a LiDAR system is recommended for areas with dense vegetation. A significant achievement of this study was to prove that the UAV-SfM technique can be used to produce highly accurate (decimeter level) and multi-temporal point clouds, DSMs, DEMs, and orthomosaics for a complex terrain containing steep slopes covered with dense vegetation. Although in this study, because of the very slow rate of movement of the landslide, only two sets of the data were collected over a 1-year period, the UAV-SfM technique offers the robustness and the repeatability to collect extensive and more frequent time series of datasets even for fast-moving 103 landslides, rockfalls and earth flows. Another finding of this study was to demonstrate that multi-temporal point clouds obtained using the UAV-SfM technique can be used to map landslide movements with decimeter accuracy. This is different than other landslide monitoring studies for the reason that instead of comparing DSMs/DEMs (that provide elevation change) or orthomosaics (that provide horizontal translations), the 3D movement was mapped directly using the point clouds. Future research could include mapping of the landslide with LiDAR and compare the resulting point cloud with the UAV-SfM point cloud generated in this study. Similarly, a separate point cloud for the landslide could be obtained using a UAV equipped with an onboard RTK GNSS system, and compared with the one obtained in this study to document the advantages and limitations of direct georeferencing. Also, in case the rate of landslide movement increases in future, a more frequent time-series of the UAV-SfM generated point clouds could be acquired to better understand the landslide dynamics. 104 6.2 Classification of UAV-SfM based point clouds UAV-SfM based remote sensing is now recognized as a relatively inexpensive and an equally accurate alternative to LiDAR for mapping of various processes on the earth’s surface, such as landslides and rockfalls, canopy modeling, floods, pipeline hazards, etc. Separation of ground points from non-ground points is necessary for subsequent analyses of these processes. While various proprietary software packages exist for the classification of UAV-SfM based point clouds, the majority of the open-source classification algorithms have been primarily designed for LiDAR datasets. Several studies assessed and compared the performance of various open-source algorithms for classification of LiDAR data from different environments. However, no comprehensive study evaluated the performance of freely available LiDAR filtering algorithms for classification of UAV-SfM based point clouds representing a complex environment involving significant variation in natural terrain and above-surface features. Therefore, there is a lack of guidelines for potential users of UAV-SfM based point clouds both in selecting appropriate classification tools as well as specifying appropriate input parameters. This study tested the performance of five open-source LiDAR filtering algorithms for bare-earth extraction from a complex UAV-SfM based point cloud from Western Canada. The primary goal was to provide assistance to potential users in the selection of the most suitable method and its input parameters for the classification of natural and suburban environments involving a large variation in terrain slope and vegetation. The tested algorithms include the multiscale dimensionality algorithm (CANUPO), the cloth simulated filter (CSF), the PTD filter (LASground), the multiscale curvature classification (MCC), and the weighted linear least-squares interpolation filter (FUSION). In general, all five algorithms produced satisfactory results with 105 overall classification errors lower than 6%. CSF and MCC produced the lowest Total errors (4%), followed by LASground and FUSION (5% each); these four algorithms minimized Type-I error (average value of 4.5%) compared to Type-II error (average value of 13%). CANUPO resulted in the highest Total error of 6%. However, compared to other four algorithms it did not cause a noticeable bias towards Type-I (6%) or Type-II (8%) errors. The choice of minimizing Type-I or Type-II error is application specific, however, the accuracy of canopy height estimation for vegetation modeling is directly affected by the accuracy of the DEM interpolated from the BE point cloud. CSF is simple to use and proved to be the fastest in terms of computation speed. It is the only algorithm out of the five that can handle large point clouds (>50 million points) without the need for preprocessing/tiling. CANUPO provided the best results in steep slopes and for rough/eroded surfaces; it preserved discontinuities and sharp spikes in the ground. However, it is computationally intensive and required a strenuous trial and error procedure to build a classifier best suited for the study site. Fortunately, the trained classifier may be used for other sites involving similar topography and vegetation cover. MCC is least affected by the low density of ground points under dense vegetation in UAV-SfM based point clouds. Because of the maximum point limit of 1.5 million points, the free version of LASground is not ideal for classifying large point clouds because of the tiling and merge procedure involved. In general, the classification accuracy of the algorithms decreased with decreasing density of the point cloud. However, the resolution should not be a concern in most cases as UAV-SfM data is of high (~5-10 cm) resolution because of the low flying height of UAV compared to traditionally used aircraft. All algorithms produced large errors in classification of difficult-to-filter features, such as low objects/vegetation, buildings, and 106 cliffs. The need to preserve discontinuities and sharp features in the natural terrain resulted in the incorrect classification of these features into ground points. A possible solution is the use of a hierarchical approach where two or more algorithms are combined and features incorrectly classified by one algorithm are handled by the other. The classification errors obtained in this study (1-6% Type-I error and 8-15% Type II error) are significantly lower than previous comparison studies on LiDAR and UAV-SfM based point clouds (10-40% Type I and Type-II errors), which reflect the choice of the five algorithms used in this study as well as the selection of appropriate/optimum input parameters. However, it should be acknowledged that in addition to the algorithms tested here, other novel or improved filtering algorithms have been presented by various authors. These algorithms (or their codes) were not readily accessible for testing. In future, such algorithms might become available as a separate tool or as add-ins in open-source point cloud handling software. It is possible that those algorithms will result in lower errors than those produced in this study. A major issue experienced by all five algorithms tested was caused by a very low ground point density under dense vegetation on steep slopes. 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Zhang, K., and Whitman, D.: Comparison of three algorithms for filtering airborne lidar data, Photogrammetric Engineering & Remote Sensing, 71, 313-324, 2005. Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G.: An easy-to-use airborne LiDAR data filtering method based on cloth simulation, Remote Sensing, 8, 501, 2016. 117 Appendices 118 Appendix A Calculations for mapping accuracy assessment A.1 Root mean square errors (RMSEs) for GCP Configuration - A GCP/ CP RTK GNSS Computed Error Easting (X) Northing (Y) Elev. (Z) Easting (X) Northing (Y) Elev. (Z) ∆X ∆Y ∆Z 1* 305625.209 5493442.257 582.139 305625.262 5493442.334 582.139 -0.053 -0.077 0.000 2* 305434.811 5493604.978 593.729 305434.730 5493604.943 593.557 0.081 0.035 0.172 3 305445.311 5493682.423 594.779 305445.243 5493682.403 593.728 0.068 0.020 1.051 4 305445.283 5493682.677 593.713 305445.247 5493682.647 593.722 0.036 0.030 -0.009 5* 305507.992 5493683.134 592.452 305507.943 5493683.091 592.447 0.049 0.043 0.005 6* 305722.981 5493687.120 592.682 305722.976 5493687.079 592.709 0.005 0.041 -0.027 7 305693.886 5493688.682 591.806 305693.883 5493688.678 591.772 0.003 0.004 0.034 8* 305759.014 5493686.171 592.670 305759.050 5493686.139 592.699 -0.036 0.032 -0.029 9* 305835.391 5493684.349 591.170 305835.420 5493684.332 591.171 -0.029 0.017 -0.001 10 305694.374 5493748.770 592.630 305694.380 5493748.778 592.549 -0.006 -0.008 0.081 11 305669.728 5493748.872 592.676 305669.693 5493748.836 592.727 0.035 0.036 -0.051 12 305622.936 5493689.157 592.406 305622.953 5493689.136 592.384 -0.017 0.021 0.022 13 305518.436 5493719.698 592.986 305518.443 5493719.721 593.041 -0.007 -0.023 -0.055 14 305378.814 5493462.035 594.437 305378.817 5493462.040 594.455 -0.003 -0.005 -0.018 15* 305355.925 5493484.180 594.534 305355.876 5493484.193 594.600 0.049 -0.013 -0.066 16* 305506.987 5493179.651 592.633 305506.996 5493179.689 592.627 -0.009 -0.038 0.006 17* 305589.697 5493571.138 576.470 305589.764 5493571.189 576.459 -0.067 -0.051 0.011 18* 305748.186 5493592.093 581.439 305748.101 5493592.120 581.408 0.085 -0.027 0.031 19* 305633.001 5493344.631 553.535 305633.023 5493344.709 553.617 -0.022 -0.078 -0.082 20* 305640.151 5493479.212 577.667 305640.273 5493479.176 577.613 -0.122 0.036 0.054 21 305767.852 5493330.137 534.246 305767.869 5493330.139 534.202 -0.017 -0.002 0.044 22 305772.315 5493285.685 521.628 305772.286 5493285.653 521.633 0.029 0.032 -0.005 23* 305716.205 5493224.735 523.635 305716.210 5493224.745 523.563 -0.005 -0.010 0.072 24* 305680.145 5493226.715 540.551 305680.151 5493226.710 540.597 -0.006 0.005 -0.046 25* 305821.195 5493333.107 526.184 305821.227 5493333.105 526.168 -0.032 0.002 0.016 26* 305872.922 5493344.099 523.270 305872.904 5493344.133 523.189 0.018 -0.034 0.081 27* 305717.262 5493385.696 547.851 305717.300 5493385.694 547.739 -0.038 0.002 0.112 28* 305650.541 5493611.606 578.675 305650.580 5493611.581 578.559 -0.039 0.025 0.116 29* 305692.050 5493430.215 548.442 305692.044 5493430.235 548.063 0.006 -0.020 0.379 30* 305561.758 5493493.043 580.540 305561.711 5493493.056 580.497 0.047 -0.013 0.043 31* 305513.883 5493359.816 581.251 305513.826 5493359.863 581.252 0.057 -0.047 -0.001 32* 305454.978 5493292.020 592.686 305454.933 5493292.032 592.645 0.045 -0.012 0.041 33* 305433.484 5493485.816 593.848 305433.413 5493485.834 593.881 0.071 -0.018 -0.033 34 305433.375 5493485.824 593.511 305433.366 5493485.825 593.524 0.009 -0.001 -0.013 35* 305543.405 5493630.241 592.731 305543.365 5493630.169 592.737 0.040 0.072 -0.006 36* 305579.733 5493639.671 592.636 305579.744 5493639.585 592.633 -0.011 0.086 0.003 37* 305811.609 5493562.685 581.755 305811.639 5493562.746 581.726 -0.030 -0.061 0.029 38* 305734.730 5493658.599 589.057 305734.749 5493658.520 589.030 -0.019 0.079 0.027 39* 305937.040 5493619.281 591.753 305937.087 5493619.279 591.770 -0.047 0.002 -0.017 * Points used as GCPs **RMSE calculated by omitting underlined error values RSME = 0.044 0.038 0.187 **RMSE= 0.042 0.040 0.044 119 A.2 Root mean square errors (RMSEs) for GCP Configuration – B GCP/ CP RTK GNSS Computed Error Easting (X) Northing (Y) Elev. (Z) Easting (X) Northing (Y) Elev. (Z) ∆X ∆Y ∆Z 1* 305625.209 5493442.257 582.139 305625.270 5493442.321 582.130 -0.061 -0.064 0.009 2* 305434.811 5493604.978 593.729 305434.755 5493604.951 593.332 0.056 0.027 0.397 3 305445.311 5493682.423 594.779 305445.253 5493682.402 593.720 0.058 0.021 1.059 4 305445.283 5493682.677 593.713 305445.238 5493682.656 593.716 0.045 0.021 -0.003 5* 305507.992 5493683.134 592.452 305507.969 5493683.110 592.460 0.023 0.024 -0.008 6 305722.981 5493687.120 592.682 305722.920 5493687.094 592.623 0.061 0.026 0.059 7 305693.886 5493688.682 591.806 305693.877 5493688.672 591.818 0.009 0.010 -0.012 8 305759.014 5493686.171 592.670 305759.029 5493686.132 592.703 -0.015 0.039 -0.033 9* 305835.391 5493684.349 591.170 305835.414 5493684.337 591.172 -0.023 0.012 -0.002 10 305694.374 5493748.770 592.630 305694.343 5493748.780 592.526 0.031 -0.010 0.104 11 305669.728 5493748.872 592.676 305669.705 5493748.856 592.650 0.023 0.016 0.026 12 305622.936 5493689.157 592.406 305622.943 5493689.152 592.399 -0.007 0.005 0.007 13 305518.436 5493719.698 592.986 305518.449 5493719.707 593.047 -0.013 -0.009 -0.061 14 305378.814 5493462.035 594.437 305378.805 5493462.031 594.452 0.009 0.004 -0.015 15* 305355.925 5493484.180 594.534 305355.883 5493484.194 594.624 0.042 -0.014 -0.090 16* 305506.987 5493179.651 592.633 305506.978 5493179.658 592.659 0.009 -0.007 -0.026 17* 305589.697 5493571.138 576.470 305589.790 5493571.195 576.454 -0.093 -0.057 0.016 18* 305748.186 5493592.093 581.439 305748.115 5493592.140 581.400 0.071 -0.047 0.039 19* 305633.001 5493344.631 553.535 305633.067 5493344.708 553.607 -0.066 -0.077 -0.072 20 305640.151 5493479.212 577.667 305640.285 5493479.155 577.591 -0.134 0.057 0.076 21 305767.852 5493330.137 534.246 305767.875 5493330.146 534.207 -0.023 -0.009 0.039 22 305772.315 5493285.685 521.628 305772.280 5493285.634 521.567 0.035 0.051 0.061 23* 305716.205 5493224.735 523.635 305716.201 5493224.729 523.611 0.004 0.006 0.024 24* 305680.145 5493226.715 540.551 305680.150 5493226.711 540.579 -0.005 0.004 -0.028 25* 305821.195 5493333.107 526.184 305821.226 5493333.104 526.176 -0.031 0.003 0.008 26* 305872.922 5493344.099 523.270 305872.904 5493344.129 523.247 0.018 -0.030 0.023 27 305717.262 5493385.696 547.851 305717.300 5493385.694 547.681 -0.038 0.002 0.170 28 305650.541 5493611.606 578.675 305650.580 5493611.581 578.501 -0.039 0.025 0.174 29* 305692.050 5493430.215 548.442 305692.068 5493430.211 548.045 -0.018 0.004 0.397 30 305561.758 5493493.043 580.540 305561.747 5493493.039 580.474 0.011 0.004 0.066 31* 305513.883 5493359.816 581.251 305513.824 5493359.851 581.242 0.059 -0.035 0.009 32 305454.978 5493292.020 592.686 305454.886 5493292.044 592.634 0.092 -0.024 0.052 33* 305433.484 5493485.816 593.848 305433.420 5493485.830 593.840 0.064 -0.014 0.008 34 305433.375 5493485.824 593.511 305433.362 5493485.828 593.518 0.013 -0.004 -0.007 35* 305543.405 5493630.241 592.731 305543.361 5493630.200 592.715 0.044 0.041 0.016 36 305579.733 5493639.671 592.636 305579.778 5493639.644 592.615 -0.045 0.027 0.021 37* 305811.609 5493562.685 581.755 305811.635 5493562.737 581.703 -0.026 -0.052 0.052 38* 305734.730 5493658.599 589.057 305734.715 5493658.551 589.055 0.015 0.048 0.002 39* 305937.040 5493619.281 591.753 305937.070 5493619.291 591.750 -0.030 -0.010 0.003 * Points used as GCPs **RMSE calculated by omitting underlined error values RSME = 0.047 0.031 0.200 **RMSE= 0.047 0.033 0.051 120 A.3 Root mean square errors (RMSEs) for GCP Configuration - C GCP/ CP RTK GNSS Computed Error Easting (X) Northing (Y) Elev. (Z) Easting (X) Northing (Y) Elev. (Z) ∆X ∆Y ∆Z 1 305625.209 5493442.257 582.139 305625.306 5493442.387 582.015 -0.097 -0.130 0.124 2* 305434.811 5493604.978 593.729 305434.748 5493604.939 593.419 0.063 0.039 0.310 3 305445.311 5493682.423 594.779 305445.225 5493682.378 593.746 0.086 0.045 1.033 4 305445.283 5493682.677 593.713 305445.227 5493682.657 593.707 0.056 0.020 0.006 5* 305507.992 5493683.134 592.452 305507.977 5493683.088 592.462 0.015 0.046 -0.010 6 305722.981 5493687.120 592.682 305722.878 5493687.109 592.545 0.103 0.011 0.137 7 305693.886 5493688.682 591.806 305693.907 5493688.707 591.837 -0.021 -0.025 -0.031 8* 305759.014 5493686.171 592.670 305759.000 5493686.122 592.556 0.014 0.049 0.114 9 305835.391 5493684.349 591.170 305835.438 5493684.350 591.248 -0.047 -0.001 -0.078 10 305694.374 5493748.770 592.630 305694.340 5493748.744 592.493 0.034 0.026 0.137 11 305669.728 5493748.872 592.676 305669.700 5493748.845 592.861 0.028 0.027 -0.185 12 305622.936 5493689.157 592.406 305622.948 5493689.150 592.375 -0.012 0.007 0.031 13 305518.436 5493719.698 592.986 305518.391 5493719.684 593.068 0.045 0.014 -0.082 14 305378.814 5493462.035 594.437 305378.808 5493462.032 594.388 0.006 0.003 0.049 15* 305355.925 5493484.180 594.534 305355.888 5493484.194 594.585 0.037 -0.014 -0.051 16* 305506.987 5493179.651 592.633 305506.997 5493179.683 592.624 -0.010 -0.032 0.009 17 305589.697 5493571.138 576.470 305589.815 5493571.242 576.538 -0.118 -0.104 -0.068 18 305748.186 5493592.093 581.439 305748.024 5493592.137 581.341 0.162 -0.044 0.098 19 305633.001 5493344.631 553.535 305633.070 5493344.755 553.779 -0.069 -0.124 -0.244 20 305640.151 5493479.212 577.667 305640.335 5493479.182 577.555 -0.184 0.030 0.112 21 305767.852 5493330.137 534.246 305767.884 5493330.149 534.249 -0.032 -0.012 -0.003 22 305772.315 5493285.685 521.628 305772.258 5493285.646 521.601 0.057 0.039 0.027 23 305716.205 5493224.735 523.635 305716.179 5493224.720 523.631 0.026 0.015 0.004 24* 305680.145 5493226.715 540.551 305680.152 5493226.709 540.594 -0.007 0.006 -0.043 25 305821.195 5493333.107 526.184 305821.231 5493333.096 526.192 -0.036 0.011 -0.008 26* 305872.922 5493344.099 523.270 305872.939 5493344.119 523.208 -0.017 -0.020 0.062 27* 305717.262 5493385.696 547.851 305717.217 5493385.744 547.480 0.045 -0.048 0.371 28* 305650.541 5493611.606 578.675 305650.625 5493611.562 578.533 -0.084 0.044 0.142 29 305692.050 5493430.215 548.442 305692.034 5493430.221 548.181 0.016 -0.006 0.261 30* 305561.758 5493493.043 580.540 305561.749 5493493.045 580.520 0.009 -0.002 0.020 31* 305513.883 5493359.816 581.251 305513.813 5493359.865 581.256 0.070 -0.049 -0.005 32 305454.978 5493292.020 592.686 305454.899 5493292.045 592.561 0.079 -0.025 0.125 33 305433.484 5493485.816 593.848 305433.440 5493485.827 593.708 0.044 -0.011 0.140 34 305433.375 5493485.824 593.511 305433.368 5493485.830 593.390 0.007 -0.006 0.121 35 305543.405 5493630.241 592.731 305543.371 5493630.196 592.670 0.034 0.045 0.061 36 305579.733 5493639.671 592.636 305579.816 5493639.616 592.600 -0.083 0.055 0.036 37* 305811.609 5493562.685 581.755 305811.594 5493562.735 581.732 0.015 -0.050 0.023 38 305734.730 5493658.599 589.057 305734.646 5493658.539 588.953 0.084 0.060 0.104 39* 305937.040 5493619.281 591.753 305937.024 5493619.301 591.804 0.016 -0.020 -0.051 * Points used as GCPs **RMSE calculated by omitting underlined error values RMSE= 0.065 0.045 0.207 **RMSE= 0.066 0.046 0.093 121 A.4 Root mean square errors (RMSEs) for GCP Configuration - D GCP/ CP RTK GNSS Computed Error Easting (X) Northing (Y) Elev. (Z) Easting (X) Northing (Y) Elev. (Z) ∆X ∆Y ∆Z 1* 305625.209 5493442.257 582.139 305625.318 5493442.329 582.138 -0.109 -0.072 0.001 2 305434.811 5493604.978 593.729 305434.721 5493604.933 593.478 0.090 0.045 0.251 3* 305445.311 5493682.423 594.779 305445.218 5493682.357 593.857 0.093 0.066 0.922 4 305445.283 5493682.677 593.713 305445.224 5493682.646 593.837 0.059 0.031 -0.124 5* 305507.992 5493683.134 592.452 305508.068 5493683.075 592.500 -0.076 0.059 -0.048 6* 305722.981 5493687.120 592.682 305722.940 5493687.158 592.609 0.041 -0.038 0.073 7 305693.886 5493688.682 591.806 305693.914 5493688.717 591.727 -0.028 -0.035 0.079 8* 305759.014 5493686.171 592.670 305759.027 5493686.151 592.612 -0.013 0.020 0.058 9* 305835.391 5493684.349 591.170 305835.395 5493684.377 591.141 -0.004 -0.028 0.029 10 305694.374 5493748.770 592.630 305694.340 5493748.732 592.678 0.034 0.038 -0.048 11 305669.728 5493748.872 592.676 305669.687 5493748.829 592.694 0.041 0.043 -0.018 12 305622.936 5493689.157 592.406 305622.951 5493689.151 592.385 -0.015 0.006 0.021 13 305518.436 5493719.698 592.986 305518.407 5493719.670 593.075 0.029 0.028 -0.089 14 305378.814 5493462.035 594.437 305378.811 5493462.029 594.482 0.003 0.006 -0.045 15* 305356.033 5493484.180 594.534 305355.897 5493484.170 594.622 0.136 0.010 -0.088 16* 305506.987 5493179.651 592.633 305506.974 5493179.678 592.679 0.013 -0.027 -0.046 17 305589.697 5493571.138 576.470 305589.848 5493571.228 576.539 -0.151 -0.090 -0.069 18 305748.186 5493592.093 581.439 305748.010 5493592.234 581.147 0.176 -0.141 0.292 19 305633.001 5493344.631 553.535 305633.070 5493344.742 553.772 -0.069 -0.111 -0.237 20 305640.151 5493479.212 577.667 305640.334 5493479.143 577.626 -0.183 0.069 0.041 21 305767.852 5493330.137 534.246 305767.866 5493330.145 534.173 -0.014 -0.008 0.073 22 305772.315 5493285.685 521.628 305772.279 5493285.649 521.585 0.036 0.036 0.043 23* 305716.205 5493224.735 523.635 305716.196 5493224.728 523.587 0.009 0.007 0.048 24* 305680.145 5493226.715 540.551 305680.154 5493226.712 540.573 -0.009 0.003 -0.022 25* 305821.195 5493333.107 526.184 305821.225 5493333.101 526.121 -0.030 0.006 0.063 26* 305872.922 5493344.099 523.270 305872.903 5493344.067 523.177 0.019 0.032 0.093 27 305717.262 5493385.696 547.851 305717.217 5493385.744 547.301 0.045 -0.048 0.550 28 305650.541 5493611.606 578.675 305650.601 5493611.579 578.438 -0.060 0.027 0.237 29* 305692.050 5493430.215 548.442 305692.041 5493430.241 548.223 0.009 -0.026 0.219 30 305561.758 5493493.043 580.540 305561.770 5493493.036 580.566 -0.012 0.007 -0.026 31 305513.883 5493359.816 581.251 305513.822 5493359.885 581.315 0.061 -0.069 -0.064 32* 305454.978 5493292.020 592.686 305454.936 5493292.034 592.677 0.042 -0.014 0.009 33* 305433.484 5493485.816 593.848 305433.434 5493485.832 593.657 0.050 -0.016 0.191 34 305433.375 5493485.824 593.511 305433.368 5493485.827 593.320 0.007 -0.003 0.191 35 305543.405 5493630.241 592.731 305543.426 5493630.187 592.696 -0.021 0.054 0.035 36 305579.733 5493639.671 592.636 305579.825 5493639.641 592.602 -0.092 0.030 0.034 37 305811.609 5493562.685 581.755 305811.525 5493562.834 581.512 0.084 -0.149 0.243 38 305734.730 5493658.599 589.057 305734.665 5493658.564 588.874 0.065 0.035 0.183 39* 305937.040 5493619.281 591.753 305937.035 5493619.304 591.696 0.005 -0.023 0.057 * Points used as GCPs **RMSE calculated by omitting underlined error values RMSE= 0.070 0.053 0.210 **RMSE= 0.070 0.053 0.115 122 A.5 Root mean square errors (RMSEs) for GCP Configuration - E GCP/ CP RTK GNSS Computed Error Easting (X) Northing (Y) Elev. (Z) Easting (X) Northing (Y) Elev. (Z) ∆X ∆Y ∆Z 1* 305625.209 5493442.257 582.139 305625.268 5493442.329 582.172 -0.059 -0.072 -0.033 2 305434.811 5493604.978 593.729 305434.739 5493604.944 593.378 0.072 0.034 0.351 3 305445.311 5493682.423 594.779 305445.162 5493682.391 593.708 0.149 0.032 1.071 4 305445.283 5493682.677 593.713 305445.208 5493682.652 593.703 0.075 0.025 0.010 5* 305507.992 5493683.134 592.452 305507.941 5493683.082 592.463 0.051 0.052 -0.011 6* 305722.981 5493687.120 592.682 305722.921 5493687.138 592.478 0.060 -0.018 0.204 7 305693.886 5493688.682 591.806 305693.862 5493688.660 591.747 0.024 0.022 0.059 8 305759.014 5493686.171 592.670 305759.000 5493686.152 592.689 0.014 0.019 -0.019 9 305835.391 5493684.349 591.170 305835.398 5493684.339 591.272 -0.007 0.010 -0.102 10 305694.374 5493748.770 592.630 305694.341 5493748.742 592.519 0.033 0.028 0.111 11 305669.728 5493748.872 592.676 305669.699 5493748.848 592.676 0.029 0.024 0.000 12 305622.936 5493689.157 592.406 305622.947 5493689.145 592.411 -0.011 0.012 -0.005 13 305518.436 5493719.698 592.986 305518.465 5493719.717 593.070 -0.029 -0.019 -0.084 14 305378.814 5493462.035 594.437 305378.803 5493462.038 594.474 0.011 -0.003 -0.037 15* 305355.925 5493484.180 594.534 305355.880 5493484.202 594.622 0.045 -0.022 -0.088 16* 305506.987 5493179.651 592.633 305506.949 5493179.660 592.602 0.038 -0.009 0.031 17 305589.697 5493571.138 576.470 305589.792 5493571.212 576.593 -0.095 -0.074 -0.123 18 305748.186 5493592.093 581.439 305748.020 5493592.213 581.158 0.166 -0.120 0.281 19 305633.001 5493344.631 553.535 305633.047 5493344.745 553.829 -0.046 -0.114 -0.294 20 305640.151 5493479.212 577.667 305640.292 5493479.146 577.653 -0.141 0.066 0.014 21 305767.852 5493330.137 534.246 305767.901 5493330.168 534.222 -0.049 -0.031 0.024 22 305772.315 5493285.685 521.628 305772.283 5493285.650 521.573 0.032 0.035 0.055 23 305716.205 5493224.735 523.635 305716.192 5493224.727 523.572 0.013 0.008 0.063 24* 305680.145 5493226.715 540.551 305680.157 5493226.709 540.565 -0.012 0.006 -0.014 25 305821.195 5493333.107 526.184 305821.236 5493333.093 526.165 -0.041 0.014 0.019 26* 305872.922 5493344.099 523.270 305872.864 5493344.119 523.279 0.058 -0.020 -0.009 27 305717.262 5493385.696 547.851 305717.276 5493385.743 547.045 -0.014 -0.047 0.806 28 305650.541 5493611.606 578.675 305650.618 5493611.577 578.533 -0.077 0.029 0.142 29 305692.050 5493430.215 548.442 305692.017 5493430.217 548.138 0.033 -0.002 0.304 30 305561.758 5493493.043 580.540 305561.725 5493493.029 580.595 0.033 0.014 -0.055 31 305513.883 5493359.816 581.251 305513.752 5493359.888 581.364 0.131 -0.072 -0.113 32 305454.978 5493292.020 592.686 305454.841 5493292.074 592.632 0.137 -0.054 0.054 33 305433.484 5493485.816 593.848 305433.392 5493485.835 593.834 0.092 -0.019 0.014 34 305433.375 5493485.824 593.511 305433.358 5493485.833 593.497 0.017 -0.009 0.014 35 305543.405 5493630.241 592.731 305543.374 5493630.163 592.701 0.031 0.078 0.030 36 305579.733 5493639.671 592.636 305579.790 5493639.605 592.626 -0.057 0.066 0.010 37 305811.609 5493562.685 581.755 305811.542 5493562.838 581.519 0.067 -0.153 0.236 38 305734.730 5493658.599 589.057 305734.668 5493658.551 588.822 0.062 0.048 0.235 39* 305937.040 5493619.281 591.753 305937.023 5493619.305 591.723 0.017 -0.024 0.030 * Points used as GCPs **RMSE calculated by omitting underlined error values RSME= 0.068 0.051 0.250 **RMSE= 0.066 0.053 0.111 123 A.6 Root mean square errors (RMSEs) for GCP Configuration - F GCP/ CP RTK GNSS Computed Error Easting (X) Northing (Y) Elevation (Z) Easting (X) Northing (Y) Elevation (Z) ∆X ∆Y ∆Z 1 305625.209 5493442.257 582.139 305625.410 5493442.375 581.939 -0.201 -0.118 0.200 2 305434.811 5493604.978 593.729 305434.754 5493604.897 593.331 0.057 0.081 0.398 3 305445.311 5493682.423 594.779 305445.135 5493682.366 593.917 0.176 0.057 0.862 4 305445.283 5493682.677 593.713 305445.134 5493682.624 593.804 0.149 0.053 -0.091 5* 305507.992 5493683.134 592.452 305508.005 5493683.123 592.454 -0.013 0.011 -0.002 6 305722.981 5493687.120 592.682 305722.953 5493686.955 592.037 0.028 0.165 0.645 7 305693.886 5493688.682 591.806 305694.123 5493688.565 591.346 -0.237 0.117 0.460 8 305759.014 5493686.171 592.670 305759.035 5493686.048 592.242 -0.021 0.123 0.428 9 305835.391 5493684.349 591.170 305835.352 5493684.267 590.921 0.039 0.082 0.249 10 305694.374 5493748.770 592.630 305694.607 5493748.862 592.311 -0.233 -0.092 0.319 11 305669.728 5493748.872 592.676 305670.009 5493748.972 592.553 -0.281 -0.100 0.123 12 305622.936 5493689.157 592.406 305622.939 5493689.145 592.206 -0.003 0.012 0.200 13 305518.436 5493719.698 592.986 305518.481 5493719.821 593.063 -0.045 -0.123 -0.077 14 305378.814 5493462.035 594.437 305378.772 5493462.016 594.480 0.042 0.019 -0.043 15* 305355.925 5493484.180 594.534 305355.638 5493484.115 594.615 0.287 0.065 -0.081 16* 305506.987 5493179.651 592.633 305506.881 5493179.717 592.668 0.106 -0.066 -0.035 17 305589.697 5493571.138 576.470 305589.897 5493571.241 576.437 -0.200 -0.103 0.033 18 305748.186 5493592.093 581.439 305748.116 5493592.106 580.965 0.070 -0.013 0.474 19 305633.001 5493344.631 553.535 305633.151 5493344.703 553.754 -0.150 -0.072 -0.219 20 305640.151 5493479.212 577.667 305640.428 5493479.176 577.450 -0.277 0.036 0.217 21 305767.852 5493330.137 534.246 305767.947 5493330.195 534.269 -0.095 -0.058 -0.023 22 305772.315 5493285.685 521.628 305772.435 5493285.509 521.539 -0.120 0.176 0.089 23 305716.205 5493224.735 523.635 305716.434 5493224.630 523.578 -0.229 0.105 0.057 24 305680.145 5493226.715 540.551 305680.079 5493226.604 540.564 0.066 0.111 -0.013 25 305821.195 5493333.107 526.184 305821.320 5493333.064 526.125 -0.125 0.043 0.059 26* 305872.922 5493344.099 523.270 305873.026 5493344.064 523.197 -0.104 0.035 0.073 27 305717.262 5493385.696 547.851 305717.182 5493385.872 547.116 0.080 -0.176 0.735 28 305650.541 5493611.606 578.675 305650.721 5493611.607 578.376 -0.180 -0.001 0.299 29 305692.050 5493430.215 548.442 305692.156 5493430.018 547.685 -0.106 0.197 0.757 30 305561.758 5493493.043 580.540 305561.831 5493493.035 580.457 -0.073 0.008 0.083 31 305513.883 5493359.816 581.251 305513.859 5493359.861 581.329 0.024 -0.045 -0.078 32 305454.978 5493292.020 592.686 305454.890 5493292.062 592.643 0.088 -0.042 0.043 33 305433.484 5493485.816 593.848 305433.437 5493485.802 593.803 0.047 0.014 0.045 34 305433.375 5493485.824 593.511 305433.352 5493485.816 593.457 0.023 0.008 0.054 35 305543.405 5493630.241 592.731 305543.422 5493630.191 592.632 -0.017 0.050 0.099 36 305579.733 5493639.671 592.636 305579.902 5493639.645 592.498 -0.169 0.026 0.138 37 305811.609 5493562.685 581.755 305811.611 5493562.741 581.438 -0.002 -0.056 0.317 38 305734.730 5493658.599 589.057 305734.695 5493658.391 588.452 0.035 0.208 0.605 39* 305937.040 5493619.281 591.753 305937.036 5493619.308 591.747 0.004 -0.027 0.006 * Points used as GCPs **RMSE calculated by omitting underlined error values RSME= 0.137 0.093 0.322 **RMSE= 0.140 0.085 0.241 124 Appendix B Calculations for classification accuracy assessment B.1 Effect of terrain slope on filter performances Classification Algorithm Points Incorrectly classified points Error Total Ground Object Ground Object Type-I Type-II Total Kappa N BE OBJ A B A/BE B/OBJ (A+B)/N K Slope < 15°) Manual 1143867 949198 194669 CANUPO 920776 223091 35066 6605 4 3 4 0.88 CSF 948533 195344 8198 7528 1 4 1 0.95 LASground 950301 193571 11911 12926 1 7 2 0.92 MCC 962299 181568 4695 17700 0 9 2 0.93 FUSION 952164 191657 9574 12489 1 6 2 0.93 Slope (15-30°) Manual 1995093 826443 1168650 CANUPO 743689 1236028 19995 31253 2 3 3 0.93 CSF 866589 1113128 21690 65849 3 6 4 0.90 LASground 922427 1057290 38019 137972 5 12 9 0.81 MCC 930603 1049114 16843 124952 2 11 7 0.84 FUSION 915082 1075800 22423 119777 3 10 7 0.85 Slope (>30°) Manual 8562563 7885288 677275 CANUPO 7652659 909903 290498 57889 4 9 4 0.76 CSF 7688167 874395 288144 90809 4 13 4 0.73 LASground 7657096 905469 347358 118732 4 18 5 0.68 MCC 7850429 712133 198188 162562 3 24 4 0.72 FUSION 7640880 913695 347298 124688 4 18 6 0.67 125 B.2 Effect of vegetation density on filter performances Classification Algorithm Points Incorrectly classified points Error Total Ground Object Ground Object Type-I Type-II Total Kappa N BE OBJ A B A/BE B/OBJ (A+B)/N K Dense Vegetation (Slope <15) Manual 5408275 3061642 2346633 CANUPO 2690094 2718180 414419 42923 14 2 8 0.83 CSF 3039537 2368737 99569 77440 3 3 3 0.93 LASground 3024486 2383781 154775 117559 5 5 5 0.90 MCC 3163055 2245219 40165 141506 1 6 3 0.93 FUSION 3057981 2348875 108709 106034 4 5 4 0.92 Sparse Vegetation (Slope < 15) Manual 2308042 1624864 683178 CANUPO 1594383 713660 57644 27144 4 4 4 0.91 CSF 1637981 670062 6655 19759 0 3 1 0.97 LASground 1647736 660304 13710 36560 1 5 2 0.95 MCC 1649421 658622 14296 38822 1 6 2 0.94 FUSION 1639705 668177 17485 32313 1 5 2 0.95 Dense Vegetation (Slope >30) Manual 2237241 801822 1435419 CANUPO 604598 1619101 242423 47038 30 3 13 0.69 CSF 845358 1378341 75856 121231 9 8 9 0.80 LASground 872878 1350819 104616 177509 13 12 13 0.72 MCC 954541 1269158 26056 180581 3 13 9 0.80 FUSION 899010 1334347 60554 159444 8 11 10 0.79 Sparse Vegetation (Slope >30) Manual 2650877 1873714 777163 CANUPO 1788831 862044 119939 35003 6 5 6 0.86 CSF 1920476 730399 10859 57586 1 7 3 0.94 LASground 1932971 717904 15878 75042 1 10 3 0.92 MCC 1948511 702364 13614 88314 1 11 4 0.90 FUSION 1919155 731515 20175 65354 1 8 3 0.92 126 B.3 Effect of point cloud density on filter performances Classification Algorithm Points Incorrectly classified points Error Total Ground Object Ground Object Type-I Type-II Total Kappa N BE OBJ A B A/BE B/OBJ (A+B)/N K Original (4cm spacing) Manual 53136825 41495471 11666257 CANUPO 40004810 13132015 2461758 974348 6 8 6 0.82 CSF 41779059 11357766 961313 1248512 2 11 4 0.88 LASground 41879152 11257673 1240828 1627550 3 14 5 0.84 MCC 42653503 10483322 565549 1726268 1 15 4 0.87 FUSION 41837007 11302889 1021150 1374912 2 12 5 0.87 10cm spacing Manual 36944837 26840699 10104138 CANUPO 25303994 11640843 2239158 702018 8 7 8 0.81 CSF 27021815 9923022 795162 976278 3 10 5 0.88 LASground 26256979 10687858 1561709 978876 6 10 7 0.83 MCC 28157102 8787735 318677 1635080 1 16 5 0.86 FUSION 27165064 9779773 809297 1133662 3 11 5 0.87 30cm spacing Manual 8080112 4722908 3357204 CANUPO 4097208 3982904 789812 74254 17 2 11 0.79 CSF 4688907 3391205 326512 292511 7 9 8 0.84 LASground 4699942 3380170 327647 304284 7 9 8 0.84 MCC 5077431 3002681 125012 479535 3 14 7 0.84 FUSION 4801274 3278838 252487 330395 5 10 7 0.85 50cm spacing Manual 3256857 1743128 1513729 CANUPO 1516854 1740003 301878 75604 17 5 12 0.77 CSF 1656145 1600712 192320 105337 11 7 9 0.82 LASground 1253696 2003161 111462 147136 6 10 8 0.84 MCC 1882877 1373980 74756 214505 4 14 9 0.82 FUSION 1792234 1464623 104672 153683 6 10 8 0.84 127 B.4 Effect of difficult-to-filter features on filter performances Classification Algorithm Points Incorrectly classified points Error Total Ground Object Ground Object Type-I Type-II Total Kappa N BE OBJ A B A/BE B/OBJ (A+B)/N K Low Objects Manual 26935 18747 8188 87941 79853 8088 30123 23573 6550 118064 103426 14638 CANUPO 23243 3692 124 4607 1 56 18 0.51 83921 4020 1008 5068 1 63 7 0.47 25106 5017 593 2123 3 32 9 0.71 1 50 11 0.56 CSF 24495 2440 58 5796 0 71 22 0.36 84944 2997 749 5833 1 72 7 0.38 27548 2575 111 4089 0 62 14 0.48 1 68 14 0.40 LASground 24107 2828 1 5346 0 65 20 0.42 85190 2751 84 5411 0 67 6 0.47 27989 2134 53 4462 0 68 15 0.42 0 67 14 0.44 MCC 24245 2688 130 5616 1 69 21 0.38 84960 2981 142 5239 0 65 6 0.49 28166 1957 11 4697 0 72 16 0.39 0 68 14 0.42 FUSION 23082 3845 3 3028 0 37 11 0.69 82850 5073 197 2689 0 33 3 0.76 25915 4196 35 1805 0 28 6 0.79 0 33 7 0.75 Houses Manual 272478 174532 97946 94615 56944 37671 CANUPO 191537 80941 13228 30293 8 31 16 0.64 72407 22208 4466 19929 8 53 26 0.42 8 42 21 0.53 CSF 214185 58293 6628 46272 4 47 19 0.54 79486 15129 2516 25056 4 67 29 0.32 4 57 24 0.43 LASground 215052 57426 534 41045 0 42 15 0.64 81937 12678 205 25194 0 67 27 0.37 0 54 21 0.50 MCC 201051 71427 982 27487 1 28 10 0.76 73326 21289 129 16507 0 44 18 0.60 0 36 14 0.68 128 Classification Algorithm Points Incorrectly classified points Error Total Ground Object Ground Object Type-I Type-II Total Kappa N BE OBJ A B A/BE B/OBJ (A+B)/N K FUSION 198022 74431 1217 21516 1 22 8 0.81 74569 20033 298 12708 1 34 14 0.69 1 28 11 0.75 Very low vegetation on slope > 30° Manual 1985816 2E+06 34143 CANUPO 2E+06 58814 43068 18353 2 54 3 0.32 CSF 2E+06 72275 60582 22379 3 66 4 0.20 LASground 2E+06 76350 65054 22748 3 67 4 0.19 MCC 2E+06 39328 30553 25253 2 74 3 0.23 FUSION 2E+06 75600 41502 4815 2 14 2 0.56 Cliffs (Slope >70°) Manual 123958 106488 17470 CANUPO 95749 28209 11807 1070 11 6 10 0.66 CSF 96002 27956 11552 1064 11 6 10 0.66 LASground 93490 30468 14819 1821 14 10 13 0.58 MCC 96655 27303 12144 2309 11 13 12 0.61 FUSION 93044 30874 15337 1931 14 11 14 0.56
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Landslide mapping from analysis of UAV-SFM point clouds
Text