ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand
(2022) In Master Thesis in Geographical Information Science GISM01 20212Dept of Physical Geography and Ecosystem Science
- Abstract
- Gabriela Olekszyk
ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand.
Lidar (Light Detection and Ranging) data is becoming more widely available and accessible. In many cases, it can be obtained free of charge from government agencies or local councils. In order to effectively use it in applications that require high precision, the data must be carefully studied, and sometimes verified with high precision terrestrial survey, to avoid issues introduced by potentially low point cloud accuracy.
Accuracy of Lidar data is influenced by multiple factors, such as instrument... (More) - Gabriela Olekszyk
ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand.
Lidar (Light Detection and Ranging) data is becoming more widely available and accessible. In many cases, it can be obtained free of charge from government agencies or local councils. In order to effectively use it in applications that require high precision, the data must be carefully studied, and sometimes verified with high precision terrestrial survey, to avoid issues introduced by potentially low point cloud accuracy.
Accuracy of Lidar data is influenced by multiple factors, such as instrument position and internal errors, distance to measured surface, errors in point detection, wrong classification or complex, sloping terrain.
This research focuses on analysing if recorded point characteristics, as well as some point cloud shape characteristics, show a relationship with poor data accuracy.
Data used in this study was obtained for and distributed by Auckland Council in New Zealand. The available point cloud covers a large portion of Auckland and its surroundings.
LAStools software has been used to manipulate the point cloud and extract various characteristics for 5m by 5m grid cells. Tested variables included: The number of present classes in a cell, the density of ground points (also after applying thinning algorithms), the height range and standard deviation of ground points, the intensity range, the average value and standard deviation, the average number of returns, the average scan angle, and the slope. Correlation analysis and multiple regression have been performed and no significant relationship was found between the tested variables and data accuracy using this research paper’s methodology. When comparing ground and low vegetation classes, some point cloud characteristics trends have been found, however, these are not suitable to aid with misclassification detection.
Failure to detect meaningful relationships between recorded point cloud characteristics and accuracy or misclassification errors does not definitevely mean that there are none. Different methods could lead to more promising outcomes.
Keywords: Geography, GIS, Lidar, ALS, Point cloud, Accuracy, Quality
Advisor: Hongxiao Jin
Master degree project 30 credits in Geographical Information Sciences, 2022
Department of Physical Geography and Ecosystem Science, Lund University
Thesis nr 140 (Less) - Popular Abstract
- Gabriela Olekszyk
ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand.
3D point clouds captured during aerial surveys are becoming widely available and using them can reduce costs of many projects. However, concerns over the accuracy of Lidar (Light Detection and Ranging) data can lesser its’ usability.
Many factors have influence on the accuracy of Lidar data, for example: instrument position and internal errors, distance to measured surface, errors in point detection, wrong classification or complex, sloping terrain.
Data used for this project was 2016 Lidar data... (More) - Gabriela Olekszyk
ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand.
3D point clouds captured during aerial surveys are becoming widely available and using them can reduce costs of many projects. However, concerns over the accuracy of Lidar (Light Detection and Ranging) data can lesser its’ usability.
Many factors have influence on the accuracy of Lidar data, for example: instrument position and internal errors, distance to measured surface, errors in point detection, wrong classification or complex, sloping terrain.
Data used for this project was 2016 Lidar data collected for, and made available by, Auckland Council in New Zealand. Test areas with low vegetation cover were chosen as they could be prone to incorrect point cloud classification (points representing low vegetation could be mistaken for ground points).
Work presented in this paper focuses on analysing some point cloud characteristics and their relationship with poor data accuracy. The aim of the study was to determine if any of these point cloud characteristics could indicate potential quality issues.
Point cloud characteristics were tested within 5m x 5m cells, and included: the number of present classes, the density of ground points (also after applying thinning algorithms), the height range and standard deviation of ground points, the intensity range, the average value and standard deviation, the average number of returns, the average scan angle, and the slope.
Analysis included correlation and multiple regression between the chosen point cloud characteristics and the data accuracy as described in the paper’s methodology. Comparison of point cloud characteristics between data representing low vegetation and data representing ground has also been performed.
No significant relationships or insights have been determined as a result of this study. This is not a definitive result, and different methods could lead to more promising outcomes.
Keywords: Geography, GIS, Lidar, ALS, Point cloud, Accuracy, Quality
Advisor: Hongxiao Jin
Master degree project 30 credits in Geographical Information Sciences, 2022
Department of Physical Geography and Ecosystem Science, Lund University
Thesis nr 140 (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9073422
- author
- Olekszyk, Gabriela LU
- supervisor
- organization
- course
- GISM01 20212
- year
- 2022
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Geography, GIS, Lidar, ALS, Point cloud, Accuracy, Quality
- publication/series
- Master Thesis in Geographical Information Science
- report number
- 140
- language
- English
- id
- 9073422
- date added to LUP
- 2022-01-25 10:56:11
- date last changed
- 2022-01-25 10:56:11
@misc{9073422,
abstract = {{Gabriela Olekszyk
ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand.
Lidar (Light Detection and Ranging) data is becoming more widely available and accessible. In many cases, it can be obtained free of charge from government agencies or local councils. In order to effectively use it in applications that require high precision, the data must be carefully studied, and sometimes verified with high precision terrestrial survey, to avoid issues introduced by potentially low point cloud accuracy.
Accuracy of Lidar data is influenced by multiple factors, such as instrument position and internal errors, distance to measured surface, errors in point detection, wrong classification or complex, sloping terrain.
This research focuses on analysing if recorded point characteristics, as well as some point cloud shape characteristics, show a relationship with poor data accuracy.
Data used in this study was obtained for and distributed by Auckland Council in New Zealand. The available point cloud covers a large portion of Auckland and its surroundings.
LAStools software has been used to manipulate the point cloud and extract various characteristics for 5m by 5m grid cells. Tested variables included: The number of present classes in a cell, the density of ground points (also after applying thinning algorithms), the height range and standard deviation of ground points, the intensity range, the average value and standard deviation, the average number of returns, the average scan angle, and the slope. Correlation analysis and multiple regression have been performed and no significant relationship was found between the tested variables and data accuracy using this research paper’s methodology. When comparing ground and low vegetation classes, some point cloud characteristics trends have been found, however, these are not suitable to aid with misclassification detection.
Failure to detect meaningful relationships between recorded point cloud characteristics and accuracy or misclassification errors does not definitevely mean that there are none. Different methods could lead to more promising outcomes.
Keywords: Geography, GIS, Lidar, ALS, Point cloud, Accuracy, Quality
Advisor: Hongxiao Jin
Master degree project 30 credits in Geographical Information Sciences, 2022
Department of Physical Geography and Ecosystem Science, Lund University
Thesis nr 140}},
author = {{Olekszyk, Gabriela}},
language = {{eng}},
note = {{Student Paper}},
series = {{Master Thesis in Geographical Information Science}},
title = {{ALS (Airborne Lidar) accuracy: Can potential low data quality of Lidar ground points be modelled/detected based on recorded point cloud characteristics? Case study of 2016 Lidar capture over Auckland, New Zealand}},
year = {{2022}},
}