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Future Trends and Applications for Airborne Laser Scanning

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 40088

Special Issue Editor

Department of Civil Engineering, Faculty of Engineering and Architecture Science, Ryerson University, Toronto, ON M5B 2K3, Canada
Interests: LiDAR data processing; satellite sensor modelling; image segmentation and classification; 3-D modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of LiDAR technology has been widely spread, serving many application domains in the last decade. With the advancement of recent LiDAR sensor development, higher density of point cloud can be generated paving the way for more possibilities of applications. LiDAR intensity and waveform data can be also collected together with the geometric aspects for each point of the point cloud, opening the gate for further possibilities for information extraction.

This Special Issue focuses on the new trends of the LiDAR technology and their impacts on the LiDAR applications. With the developments of new multispectral LiDAR sensors, Single Photon LiDAR (SPL) and Geiger Mode LiDAR (GML), flash LiDAR, and portable LiDAR sensors mounted on drones, new applications can be found in a variety of topographic, environmental and transportation applications. On the other hand, these new developments may also induce certain drawback toward the collected data so that new algorithmic development is desired for reducing or filtering the data noise, deriving the feature sets and improving the computational efficiency. The Special Issue is looking forward to covering different case studies on how the latest development of LiDAR sensors can aid in improving data collection capability and the derived product resolution/accuracy.

Dr. Ahmed Shaker
Guest Editor

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.

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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

  • Multispectral LiDAR technology
  • Topo-Bathymetric LiDAR applications
  • Multispectral LiDAR in Forest inventory and mapping
  • Radiometric correction of LiDAR Intensity data
  • New technologies and procedures for laser scanning
  • Flash LiDAR, and portable LiDAR sensors
  • LiDAR of coastal applications
  • Methods of LiDAR performance evaluation
  • LiDAR applications in:
    • Urban and land-cover/land-use classification
    • Feature and object extraction
    • 3D modelling and reconstruction
    • Tree species classification
    • Flood estimation
    • Wetland classification

Published Papers (8 papers)

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Research

20 pages, 5293 KiB  
Article
LiDAR DEM Smoothing and the Preservation of Drainage Features
by John B. Lindsay, Anthony Francioni and Jaclyn M. H. Cockburn
Remote Sens. 2019, 11(16), 1926; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161926 - 17 Aug 2019
Cited by 36 | Viewed by 9240
Abstract
Fine-resolution Light Detection and Ranging (LiDAR) data often exhibit excessive surface roughness that can hinder the characterization of topographic shape and the modeling of near-surface flow processes. Digital elevation model (DEM) smoothing methods, commonly low-pass filters, are sometimes applied to LiDAR data to [...] Read more.
Fine-resolution Light Detection and Ranging (LiDAR) data often exhibit excessive surface roughness that can hinder the characterization of topographic shape and the modeling of near-surface flow processes. Digital elevation model (DEM) smoothing methods, commonly low-pass filters, are sometimes applied to LiDAR data to subdue the roughness. These techniques can negatively impact the representation of topographic features, most notably drainage features, such as headwater streams. This paper presents the feature-preserving DEM smoothing (FPDEMS) method, which modifies surface normals to smooth the topographic surface in a similar manner to approaches that were originally designed for de-noising three-dimensional (3D) meshes. The FPDEMS method has been optimized for application with raster DEM data. The method was compared with several low-pass filters while using a 0.5-m resolution LiDAR DEM of an agricultural area in southwestern Ontario, Canada. The findings demonstrated that the technique was better at removing roughness, when compared with mean, median, and Gaussian filters, while also preserving sharp breaks-in-slope and retaining the topographic complexity at broader scales. Optimal smoothing occurred with kernel sizes of 11–21 grid cells, threshold angles of 10°–20°, and 3–15 elevation-update iterations. These parameter settings allowed for the effective reduction in roughness and DEM noise and the retention of terrace scarps, channel banks, gullies, and headwater streams. Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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18 pages, 4525 KiB  
Article
Multi-Spectral Lidar: Radiometric Calibration, Canopy Spectral Reflectance, and Vegetation Vertical SVI Profiles
by Maxim Okhrimenko, Craig Coburn and Chris Hopkinson
Remote Sens. 2019, 11(13), 1556; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11131556 - 30 Jun 2019
Cited by 22 | Viewed by 4829
Abstract
Multi-spectral (ms) airborne lidar data are enriched relative to traditional lidar due to the multiple channels of intensity digital numbers (DNs), which offer the potential for active Spectral Vegetation Indices (SVIs), enhanced classification, and change monitoring. However, in case of SVIs, indices should [...] Read more.
Multi-spectral (ms) airborne lidar data are enriched relative to traditional lidar due to the multiple channels of intensity digital numbers (DNs), which offer the potential for active Spectral Vegetation Indices (SVIs), enhanced classification, and change monitoring. However, in case of SVIs, indices should be calculated from spectral reflectance values derived from intensity DNs after calibration. In this paper, radiometric calibration of multi-spectral airborne lidar data is presented. A novel low-cost diffuse reflectance coating was adopted for creating radiometric targets. Comparability of spectral reflectance values derived from ms lidar data for coniferous stand (2.5% for 532 nm, 17.6% for 1064 nm, and 8.4% for 1550 nm) to available spectral libraries is shown. Active vertical profiles of SVIs were constructed and compared to modeled results available in the literature. The potential for a new landscape-level active 3D SVI voxel approach is demonstrated. Results of a field experiment with complex radiometric targets for estimating losses in detected lidar signals are described. Finally, an approach for estimating spectral reflectance values from lidar split returns is analyzed and the results show similarity of estimated values of spectral reflectance derived from split returns to spectral reflectance values obtained from single returns (p > 0.05 for paired test). Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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26 pages, 6521 KiB  
Article
Investigating the Consistency of Uncalibrated Multispectral Lidar Vegetation Indices at Different Altitudes
by Maxim Okhrimenko and Chris Hopkinson
Remote Sens. 2019, 11(13), 1531; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11131531 - 28 Jun 2019
Cited by 7 | Viewed by 2774
Abstract
Multi-spectral (ms) airborne light detection and ranging (lidar) data are increasingly used for mapping purposes. Geometric data are enriched by intensity digital numbers (DNs) and, by utilizing this additional information either directly, or in the form of active spectral vegetation indices (SVIs), enhancements [...] Read more.
Multi-spectral (ms) airborne light detection and ranging (lidar) data are increasingly used for mapping purposes. Geometric data are enriched by intensity digital numbers (DNs) and, by utilizing this additional information either directly, or in the form of active spectral vegetation indices (SVIs), enhancements in land cover classification and change monitoring are possible. In the case of SVIs, the indices should be calculated from reflectance values derived from intensity DNs after rigorous calibration. In practice, such calibration is often not possible, and SVIs calculated from intensity DNs are used. However, the consistency of such active ms lidar products is poorly understood. In this study, the authors reported on an ms lidar mission at three different altitudes above ground to investigate SVI consistency. The stability of two families of indices—spectral ratios and normalized differences—was compared. The need for atmospheric correction in case of considerable range difference was established. It was demonstrated that by selecting single returns (provided sufficient point density), it was possible to derive stable SVI products. Finally, a criterion was proposed for comparing different lidar acquisitions over vegetated areas. Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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33 pages, 8608 KiB  
Article
Coastal Ecosystem Investigations with LiDAR (Light Detection and Ranging) and Bottom Reflectance: Lake Superior Reef Threatened by Migrating Tailings
by W. Charles Kerfoot, Martin M. Hobmeier, Sarah A. Green, Foad Yousef, Colin N. Brooks, Robert Shuchman, Mike Sayers, Lihwa Lin, Phu Luong, Earl Hayter and Molly Reif
Remote Sens. 2019, 11(9), 1076; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091076 - 07 May 2019
Cited by 8 | Viewed by 5133
Abstract
Where light penetration is excellent, the combination of LiDAR (Light Detection And Ranging) and passive bottom reflectance (multispectral, hyperspectral) greatly aids environmental studies. Over a century ago, two stamp mills (Mohawk and Wolverine) released 22.7 million metric tons of copper-rich tailings into Grand [...] Read more.
Where light penetration is excellent, the combination of LiDAR (Light Detection And Ranging) and passive bottom reflectance (multispectral, hyperspectral) greatly aids environmental studies. Over a century ago, two stamp mills (Mohawk and Wolverine) released 22.7 million metric tons of copper-rich tailings into Grand Traverse Bay (Lake Superior). The tailings are crushed basalt, with low albedo and spectral signatures different from natural bedrock (Jacobsville Sandstone) and bedrock-derived quartz sands. Multiple Lidar (CHARTS and CZMIL) over-flights between 2008–2016—complemented by ground-truth (Ponar sediment sampling, ROV photography) and passive bottom reflectance studies (3-band NAIP; 13-band Sentinal-2 orbital satellite; 48 and 288-band CASI)—clarified shoreline and underwater details of tailings migrations. Underwater, the tailings are moving onto Buffalo Reef, a major breeding site important for commercial and recreational lake trout and lake whitefish production (32% of the commercial catch in Keweenaw Bay, 22% in southern Lake Superior). If nothing is done, LiDAR-assisted hydrodynamic modeling predicts 60% tailings cover of Buffalo Reef within 10 years. Bottom reflectance studies confirmed stamp sand encroachment into cobble beds in shallow (0-5m) water but had difficulties in deeper waters (>8 m). Two substrate end-members (sand particles) showed extensive mixing but were handled by CASI hyperspectral imaging. Bottom reflectance studies suggested 25-35% tailings cover of Buffalo Reef, comparable to estimates from independent counts of mixed sand particles (ca. 35% cover of Buffalo Reef by >20% stamp sand mixtures). Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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20 pages, 19513 KiB  
Article
Scan Line Intensity-Elevation Ratio (SLIER): An Airborne LiDAR Ratio Index for Automatic Water Surface Mapping
by Wai Yeung Yan, Ahmed Shaker and Paul E. LaRocque
Remote Sens. 2019, 11(7), 814; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070814 - 04 Apr 2019
Cited by 8 | Viewed by 5087
Abstract
Owing to the characteristics of how a laser interacts with the water surface and water column, the measured Light Detection and Ranging (LiDAR) intensity values are different with respect to the laser wavelength, the scanning geometry and the reflection mechanism. Depending on the [...] Read more.
Owing to the characteristics of how a laser interacts with the water surface and water column, the measured Light Detection and Ranging (LiDAR) intensity values are different with respect to the laser wavelength, the scanning geometry and the reflection mechanism. Depending on the instantaneous water condition and the laser incidence angle, laser dropouts can appear, causing null returns or empty holes found in the collected LiDAR data. This variable intensity response offers a valuable opportunity for using airborne LiDAR sensors for automatic identification of water regions, and thus, we previously proposed an airborne LiDAR-based ratio index named the scan line intensity-elevation ratio (SLIER). Over the water surface, airborne LiDAR data are always found to have a high fluctuation of the intensity value and low variation of the elevation along each scan line, and thus, the water region has a higher SLIER value compared to the land. We examined the SLIER on a multispectral airborne LiDAR dataset collected by Optech Titan and a monochromatic airborne LiDAR dataset collected by Optech Galaxy on a natural rocky shore and a man-made shore. Our experiments showed that SLIER was able to provide a high separability between land and water regions and was able to outperform the traditional normalized difference water index (NDWI) for estimation of the water surface. With the use of SLIER as a mechanism for training data selection, our case studies demonstrated an overall accuracy of 98% in the use of either monochromatic or multispectral LiDAR data, regardless of the laser channel being used. Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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19 pages, 6657 KiB  
Article
Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification
by Zongxia Xu, Zhenxin Zhang, Ruofei Zhong, Dong Chen, Taochun Sun, Xin Deng, Zhen Li and Cheng-Zhi Qin
Remote Sens. 2019, 11(3), 342; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030342 - 09 Feb 2019
Cited by 5 | Viewed by 3516
Abstract
Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point [...] Read more.
Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point clusters) in consideration of the density distribution of ground objects. The space projection method is first used to convert the three-dimensional point cloud into a two-dimensional (2D) image. The image is then mapped to the 2D manifold space, and restricted centroidal Voronoi tessellation is built for initial segmentation of content-sensitive point clusters. Thus, the segmentation results take the entity content (density distribution) into account, and the initial classification unit is adapted to the density of ground objects. The normalized cut is then used to segment the initial point clusters to construct content-sensitive multilevel point clusters. Following this, the point-based hierarchical features of each point cluster are extracted, and the multilevel point-cluster feature is constructed by sparse coding and latent Dirichlet allocation models. Finally, the hierarchical classification framework is created based on multilevel point-cluster features, and the AdaBoost classifiers in each level are trained. The recognition results of different levels are combined to effectively improve the classification accuracy of the ALS point cloud in the test process. Two scenes are used to experimentally test the method, and it is compared with three other state-of-the-art techniques. Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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23 pages, 11769 KiB  
Article
A Novel Method for High-Voltage Bundle Conductor Reconstruction from Airborne LiDAR Data
by Ruqin Zhou, Wanshou Jiang and San Jiang
Remote Sens. 2018, 10(12), 2051; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10122051 - 17 Dec 2018
Cited by 16 | Viewed by 4821
Abstract
The security of high-voltage power transmission corridors is significantly vital to the national economy and daily life. With its rapid development, LiDAR (Light Detection and Ranging) technology has been widely applied in the inspection of transmission lines. As the basis of potential hazard [...] Read more.
The security of high-voltage power transmission corridors is significantly vital to the national economy and daily life. With its rapid development, LiDAR (Light Detection and Ranging) technology has been widely applied in the inspection of transmission lines. As the basis of potential hazard detection, a robust and precise power line model is a necessary requirement for rapid and correct clearance. Thus, this paper proposes a novel method for high-voltage bundle conductor reconstruction, which can precisely reconstruct each sub-conductor. First, points in high-voltage power transmission corridors are detected and classified into four categories; second, for classified power lines, single power line spans are extracted, and bundle conductors are identified by analyzing the single spans’ fitting residuals; and then, each sub-conductor of bundle conductors is extracted by a projected dichotomy method on the XOY and XOZ planes, respectively; finally, a double-RANSAC (random sample consensus)-based algorithm was introduced to reconstruct each power line. The proposed method makes use of the distribution of bundle conductors in high-voltage transmission lines, and our experiments showed that it could preferably reconstruct the real structure of bundle conductors robustly with a high precision better than 0.2 m. Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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16 pages, 4794 KiB  
Article
The Benefit of the Geospatial-Related Waveforms Analysis to Extract Weak Laser Pulses
by Tee-Ann Teo and Wan-Yi Yeh
Remote Sens. 2018, 10(7), 1141; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10071141 - 19 Jul 2018
Cited by 5 | Viewed by 3545
Abstract
Waveform lidar provides both geometric and waveform properties from the entire returned signals. The waveform analysis is an important process to extract the attributes of the reflecting surface from the waveform. The proposed method analyzes the geospatial relationship between the return signals by [...] Read more.
Waveform lidar provides both geometric and waveform properties from the entire returned signals. The waveform analysis is an important process to extract the attributes of the reflecting surface from the waveform. The proposed method analyzes the geospatial relationship between the return signals by combining the sequential waves. The idea of this method is to analyze the waveform parameters from sequential waves. Since the adjacent return signals are geospatially correlated, they have similar waveform properties that can be used to validate the correctness of the extracted waveform parameters. The proposed method includes three major steps: (1) single-waveform processing for the initial echo detection; (2) multi-waveform processing using waveform alignment and stacking; (3) verification of the enhanced weak return. The experimental waveform lidar data were acquired using Leica ALS60, Optech Pegasus, and Riegl Q680i. The experimental result indicates that the proposed method successfully extracts the weak returns while considering the geospatial relationships. The correctness and increasing rate of the extracted ground points are related to the vegetated coverage such as the complexity and density. The correctness is above 76% in this study. Because the nearest waveform has a higher correlation, the increase in distance of adjacent waveforms will reduce the correctness of the enhanced weak return. Full article
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
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