Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Accuracy Assessment of UAS Lidar
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Accuracy Assessment of UAS Lidar

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 20327

Special Issue Editors

Dr. Benjamin E. Wilkinson

E-Mail Website
Guest Editor
Geomatics Program, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611-0565, USA
Interests: photogrammetry; lidar; UAS
Dr. Amr Abd-Elrahman

E-Mail Website
Guest Editor
Gulf Coast Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Wimauma, FL 33598, USA
Interests: object-based image analysis; machine learning; deep learning; hyperspectral and multispectral image analysis; lidar data analysis
Special Issues, Collections and Topics in MDPI journals
Dr. H. Andrew Lassiter

E-Mail Website
Guest Editor
Geospatial Mapping and Applications Laboratory, University of Florida, Gainesville, FL, USA
Interests: lidar; photogrammetry; forestry; UAS

Special Issue Information

Dear Colleagues,

Small drones, or Unoccupied Aerial Systems (UAS) are now widely used across many disciplines for a vast number of applications. This is due in large part to their relatively low required initial investment and ability to collect high resolution geospatial data rapidly and repeatedly. Most drone systems used for mapping employ small cameras and rely on structure from motion (SfM) photogrammetric workflows to develop 3D products. Over the past few years, UAS equipped with laser scanners have been made available and provide significant advantages over UAS SfM, particularly the ability to penetrate gaps in vegetation towards development of digital terrain models, and independence from image matching which may lead to artifacts in SfM-generated 3D models of challenging scenes.

With UAS lidar hardware becoming more economical, and new commercial systems emerging with varying sensor architecture, there are significant research opportunities to explore the characteristics of these systems, particularly the accuracy of the generated products in the context of applications. This includes comparisons between available UAS lidar systems and against other methods, such as SfM, for 3D reconstructions of scenes. This work will be beneficial to current and potential practitioners and will inform proper selection and use of UAS laser scanners for geospatial mapping.

This Special Issue of Remote Sensing covers investigations of UAS Lidar Accuracy Assessment to include, but not limited to:

  • Statistically-rigorous accuracy studies
  • Innovative accuracy assessment methods
  • Uncertainty estimation and error propagation
  • Lidar/image fusion
  • Sensor calibration procedures, results, and stability
  • Mission/flight planning effects on product fidelity

Dr. Benjamin E. Wilkinson
Dr. Amr Abd-Elrahman
Dr. H. Andrew Lassiter
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAS
  • lidar
  • laser scanning
  • 3D
  • accuracy

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

33 pages, 15087 KiB  
Article
Enhancing LiDAR-UAS Derived Digital Terrain Models with Hierarchic Robust and Volume-Based Filtering Approaches for Precision Topographic Mapping
by Valeria-Ersilia Oniga, Ana-Maria Loghin, Mihaela Macovei, Anca-Alina Lazar, Bogdan Boroianu and Paul Sestras
Remote Sens. 2024, 16(1), 78; https://doi.org/10.3390/rs16010078 - 24 Dec 2023
Cited by 2 | Viewed by 1957
Abstract
Airborne Laser Scanning (ALS) point cloud classification in ground and non-ground points can be accurately performed using various algorithms, which rely on a range of information, including signal analysis, intensity, amplitude, echo width, and return number, often focusing on the last return. With [...] Read more.
Airborne Laser Scanning (ALS) point cloud classification in ground and non-ground points can be accurately performed using various algorithms, which rely on a range of information, including signal analysis, intensity, amplitude, echo width, and return number, often focusing on the last return. With its high point density and the vast majority of points (approximately 99%) measured with the first return, filtering LiDAR-UAS data proves to be a more challenging task when compared to ALS point clouds. Various algorithms have been proposed in the scientific literature to differentiate ground points from non-ground points. Each of these algorithms has advantages and disadvantages, depending on the specific terrain characteristics. The aim of this research is to obtain an enhanced Digital Terrain Model (DTM) based on LiDAR-UAS data and to qualitatively and quantitatively compare three filtering approaches, i.e., hierarchical robust, volume-based, and cloth simulation, on a complex terrain study area. For this purpose, two flights over a residential area of about 7.2 ha were taken at 60 m and 100 m, with a DJI Matrice 300 RTK UAS, equipped with a Geosun GS-130X LiDAR sensor. The vertical and horizontal accuracy of the LiDAR-UAS point cloud, obtained via PPK trajectory processing, was tested using Check Points (ChPs) and manually extracted features. A combined approach for ground point classification is proposed, using the results from a hierarchic robust filter and applying an 80% slope condition for the volume-based filtering result. The proposed method has the advantage of representing with accuracy man-made structures and sudden slope changes, improving the overall accuracy of the DTMs by 40% with respect to the hierarchical robust filtering algorithm in the case of a 60 m flight height and by 28% in the case of a 100 m flight height when validated against 985 ChPs. Full article
(This article belongs to the Special Issue Accuracy Assessment of UAS Lidar)
►▼ Show Figures

Graphical abstract

24 pages, 4320 KiB  
Article
Estimating Ground Elevation in Coastal Dunes from High-Resolution UAV-LIDAR Point Clouds and Photogrammetry
by Daniele Pinton, Alberto Canestrelli, Robert Moon and Benjamin Wilkinson
Remote Sens. 2023, 15(1), 226; https://doi.org/10.3390/rs15010226 - 31 Dec 2022
Cited by 5 | Viewed by 2479
Abstract
Coastal dune environments play a critical role in protecting coastal areas from damage associated with flooding and excessive erosion. Therefore, monitoring the morphology of dunes is an important coastal management operation. Traditional ground-based survey methods are time-consuming, and data must be interpolated over [...] Read more.
Coastal dune environments play a critical role in protecting coastal areas from damage associated with flooding and excessive erosion. Therefore, monitoring the morphology of dunes is an important coastal management operation. Traditional ground-based survey methods are time-consuming, and data must be interpolated over large areas, thus limiting the ability to assess small-scale details. High-resolution uncrewed aerial vehicle (UAV) photogrammetry allows one to rapidly monitor coastal dune elevations at a fine scale and assess the vulnerability of coastal zones. However, photogrammetric methods are unable to map ground elevations beneath vegetation and only provide elevations for bare sand areas. This drawback is significant as vegetated areas play a key role in the development of dune morphology. To provide a complete digital terrain model for a coastal dune environment at Topsail Hill Preserve in Florida’s panhandle, we employed a UAV, equipped with a laser scanner and a high-resolution camera. Along with the UAV survey, we conducted a RTK–GNSS ground survey of 526 checkpoints within the survey area to serve as training/testing data for various machine-learning regression models to predict the ground elevation. Our results indicate that a UAV–LIDAR point cloud, coupled with a genetic algorithm provided the most accurate estimate for ground elevation (mean absolute error ± root mean square error, MAE ± RMSE = 7.64 ± 9.86 cm). Full article
(This article belongs to the Special Issue Accuracy Assessment of UAS Lidar)
►▼ Show Figures

Figure 1

36 pages, 8276 KiB  
Article
Accuracy Assessment of Low-Cost Lidar Scanners: An Analysis of the Velodyne HDL–32E and Livox Mid–40’s Temporal Stability
by Carter Kelly, Benjamin Wilkinson, Amr Abd-Elrahman, Orlando Cordero and H. Andrew Lassiter
Remote Sens. 2022, 14(17), 4220; https://doi.org/10.3390/rs14174220 - 26 Aug 2022
Cited by 8 | Viewed by 3879
Abstract
Identifying and mitigating sources of measurement error is a critical task in geomatics research and the geospatial industry as a whole. In pursuit of such error, accuracy assessments of lidar data have revealed a range bias in low-cost scanners. This phenomenon is a [...] Read more.
Identifying and mitigating sources of measurement error is a critical task in geomatics research and the geospatial industry as a whole. In pursuit of such error, accuracy assessments of lidar data have revealed a range bias in low-cost scanners. This phenomenon is a temporally correlated instability in the lidar scanner where the measured distance between target and sensor changes over time while both are held stationary. This research presents an assessment of two low-cost lidar scanners, the Velodyne® HDL–32E and Livox® Mid–40, in which their temporal stability is analyzed and methods to mitigate systematic error are implemented. By immobilizing each scanner as it observes a stationary target surface over the course of multiple hours, trends in scanner precision are identified. Scanner accuracy is then determined using a terrestrial lidar scanner, the Riegl® VZ-400, to observe both subject scanner and target, and extracting the distances between scanner origin and observed surface. Patterns identified in each scanner’s distance measurements indicate temporal autocorrelation, and, by exploiting the high linear correlation between scanner internal temperature and measured distance in the HDL–32E, it is possible to mitigate the resulting error. Application of the proposed solution lowers the Velodyne® scanner’s measurement RMSE by over 60%, providing levels of measurement accuracy comparable to more expensive lidar systems. Full article
(This article belongs to the Special Issue Accuracy Assessment of UAS Lidar)
►▼ Show Figures

Graphical abstract

17 pages, 5820 KiB  
Article
A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner
by Martin Štroner, Rudolf Urban and Lenka Línková
Remote Sens. 2021, 13(23), 4811; https://doi.org/10.3390/rs13234811 - 27 Nov 2021
Cited by 60 | Viewed by 6853
Abstract
Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be [...] Read more.
Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift. Full article
(This article belongs to the Special Issue Accuracy Assessment of UAS Lidar)
►▼ Show Figures

Figure 1

22 pages, 14450 KiB  
Article
Quality Analysis of Direct Georeferencing in Aspects of Absolute Accuracy and Precision for a UAV-Based Laser Scanning System
by Ansgar Dreier, Jannik Janßen, Heiner Kuhlmann and Lasse Klingbeil
Remote Sens. 2021, 13(18), 3564; https://doi.org/10.3390/rs13183564 - 8 Sep 2021
Cited by 20 | Viewed by 3392
Abstract
The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point [...] Read more.
The use of UAV-based laser scanning systems is increasing due to the rapid development in sensor technology, especially in applications such as topographic surveys or forestry. One advantage of these multi-sensor systems is the possibility of direct georeferencing of the derived 3D point clouds in a global reference frame without additional information from Ground Control Points (GCPs). This paper addresses the quality analysis of direct georeferencing of a UAV-based laser scanning system focusing on the absolute accuracy and precision of the system. The system investigated is based on the RIEGL miniVUX-SYS and the evaluation uses the estimated point clouds compared to a reference point cloud from Terrestrial Laser Scanning (TLS) for two different study areas. The precision is estimated by multiple repetitions of the same measurement and the use of artificial objects, such as targets and tables, resulting in a standard deviation of <1.2 cm for the horizontal and vertical directions. The absolute accuracy is determined using a point-based evaluation, which results in the RMSE being <2 cm for the horizontal direction and <4 cm for the vertical direction, compared to the TLS reference. The results are consistent for the two different study areas with similar evaluation approaches but different flight planning and processing. In addition, the influence of different Global Navigation Satellite System (GNSS) master stations is investigated and no significant difference was found between Virtual Reference Stations (VRS) and a dedicated master station. Furthermore, to control the orientation of the point cloud, a parameter-based analysis using planes in object space was performed, which showed a good agreement with the reference within the noise level of the point cloud. The calculated quality parameters are all smaller than the manufacturer’s specifications and can be transferred to other multi-sensor systems. Full article
(This article belongs to the Special Issue Accuracy Assessment of UAS Lidar)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop