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LiDAR Remote Sensing of Terrain and Vegetation
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LiDAR Remote Sensing of Terrain and Vegetation

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (20 March 2020) | Viewed by 14961

Special Issue Editors

Prof. Jukka Heikkonen

E-Mail Website
Guest Editor
Department of Future Technologies, Turun yliopisto, Abo (Turku), Finland
Interests: data analysis; machine learning; statistical modelling; computer vision; remote sensing
Dr. Paavo Nevalainen

E-Mail Website
Guest Editor
Department of Future Technologies, Turun yliopisto, 20500 Turku, Finland
Interests: point clouds; micro-topography classification; terrain models; curvature sampling; object registration
Dr. Jonne Pohjankukka

E-Mail Website
Guest Editor
Natural Resource Institute of Finland (LUKE), Finland
Interests: machine learning; data analysis; computer vision; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing of terrain and vegetation via LiDAR techniques is a field that has gained recently increased attention due to technological advancements in measurement and data processing techniques. A lot of research has been conducted lately, for example, on the autonomous management of forest and agricultural resources by the combination of LiDAR measuring technologies and artificial intelligence techniques. With future advancements in LiDAR remote sensing techniques, point cloud processing and neural networks subjected to point clouds, and research and applications associated with terrain and vegetation prediction and management, can be expected to increase. 

We invite authors to submit manuscripts related to this upcoming Special Issue on LiDAR Remote Sensing of Terrain and Vegetation. All topics, aspects, and suggestions regarding the Special Issue are welcomed. In case of beforehand discussions, the authors are invited to contact us.

We are excited to welcome you to participate in this Special Issue.

Prof. Jukka Heikkonen
Dr. Paavo Nevalainen
Dr. Jonne Pohjankukka
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. Sensors 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 2600 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

  • LiDAR sensors
  • remote sensing
  • sensor fusion
  • point clouds
  • terrain model
  • micro-topography
  • local height analysis
  • biomass distribution
  • remote crop investigation
  • geographic descriptors

Published Papers (4 papers)

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Research

17 pages, 42226 KiB  
Article
Aerial LiDAR Data Augmentation for Direct Point-Cloud Visualisation
by Ciril Bohak, Matej Slemenik, Jaka Kordež and Matija Marolt
Sensors 2020, 20(7), 2089; https://0-doi-org.brum.beds.ac.uk/10.3390/s20072089 - 08 Apr 2020
Cited by 5 | Viewed by 3542
Abstract
Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete [...] Read more.
Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Terrain and Vegetation)
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18 pages, 6674 KiB  
Article
Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy
by Zsuzsanna Szabó, Csaba Albert Tóth, Imre Holb and Szilárd Szabó
Sensors 2020, 20(7), 2063; https://0-doi-org.brum.beds.ac.uk/10.3390/s20072063 - 07 Apr 2020
Cited by 12 | Viewed by 3202
Abstract
Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the [...] Read more.
Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Terrain and Vegetation)
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21 pages, 8077 KiB  
Article
A Rapid Terrestrial Laser Scanning Method for Coastal Erosion Studies: A Case Study at Freeport, Texas, USA
by Lin Xiong, Guoquan Wang, Yan Bao, Xin Zhou, Kuan Wang, Hanlin Liu, Xiaohan Sun and Ruibin Zhao
Sensors 2019, 19(15), 3252; https://0-doi-org.brum.beds.ac.uk/10.3390/s19153252 - 24 Jul 2019
Cited by 13 | Viewed by 3919
Abstract
Terrestrial laser scanning (TLS) has become a powerful data acquisition technique for high-resolution high-accuracy topographic and morphological studies. Conventional static TLS surveys require setting up numerous reflectors (tie points) in the field for point clouds registration and georeferencing. To reduce surveying time and [...] Read more.
Terrestrial laser scanning (TLS) has become a powerful data acquisition technique for high-resolution high-accuracy topographic and morphological studies. Conventional static TLS surveys require setting up numerous reflectors (tie points) in the field for point clouds registration and georeferencing. To reduce surveying time and simplify field operational tasks, we have developed a rapid TLS surveying method that requires only one reflector in the field. The method allows direct georeferencing of point clouds from individual scans to an East–North–Height (ENH) coordinate system tied to a stable geodetic reference frame. TLS datasets collected at a segment of the beach–dune–wetland area in Freeport, Texas, USA are used to evaluate the performance of the rapid surveying method by comparing with kinematic GPS measurements. The rapid surveying method uses two GPS units mounted on the scanner and a reflector for calculating the northing angle of the scanner’s own coordinate system (SOCS). The Online Positioning User Service (OPUS) is recommended for GPS data processing. According to this study, OPUS Rapid-Static (OPUS-RS) solutions retain 1–2 cm root mean square (RMS) accuracy in the horizontal directions and 2–3 cm accuracy in the vertical direction for static observational sessions of approximately 30 min in the coastal region of Texas, USA. The rapid TLS surveys can achieve an elevation accuracy (RMS) of approximately 3–5 cm for georeferenced points and 2–3 cm for digital elevation models (DEMs). The elevation errors superimposed into the TLS surveying points roughly fit a normal distribution. The proposed TLS surveying method is particularly useful for morphological mapping over time in coastal regions, where strong wind and soft sand prohibit reflectors from remaining strictly stable for a long period. The theories and results presented in this paper are beneficial to researchers who frequently utilize TLS datasets in their research, but do not have opportunities to be involved in field data acquisition. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Terrain and Vegetation)
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13 pages, 1376 KiB  
Article
Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales
by Junjie Zhou, Hongqiang Wei, Guiyun Zhou and Lihui Song
Sensors 2019, 19(8), 1852; https://0-doi-org.brum.beds.ac.uk/10.3390/s19081852 - 18 Apr 2019
Cited by 12 | Viewed by 3653
Abstract
The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. [...] Read more.
The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. In this study, we extend the optimal scale method to the multi-optimal-scale method, adaptively selecting multiple optimal scales for each point in the tree point cloud to increase the distinctiveness of extracted geometric features. Compared with the optimal scale method, our method achieves higher separation accuracy. Compared with the multi-scale method, our method achieves more stable separation accuracy with a limited number of optimal scales. The running time of our method is greatly reduced when the optimization strategy is applied. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Terrain and Vegetation)
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