Landslide mapping from analysis of UAV-SFM point clouds
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Asghar, Umair
issued: 2017-11-15T22:02:29Z
issued: 2017
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: 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 object…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/63604
note: 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 …
has type: Thesis/Dissertation
date issued: 2018-02
is shown at: 10.14288/1.0357952
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/63604