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Applied Sciences
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Methods and Applications of Lidar Remote Sensing
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Methods and Applications of Lidar Remote Sensing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 2097

Special Issue Editors

Dr. Wei Wang

E-Mail Website
Guest Editor
School of Geoscience and Info-Physics, Central South University, Changsha 410083, China
Interests: atmospheric remote sensing; point cloud processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jian Yang

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China
Interests: fluorescence/multispectral lidar; UAV lidar; classification based on multispectral/hyperspectral point clouds; power-line inspection; vegetation parameter retrieval
Special Issues, Collections and Topics in MDPI journals
Dr. Ge Han

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 4730079, China
Interests: CO2; CH4; remote sensing; Lidar
Special Issues, Collections and Topics in MDPI journals
Dr. Boming Liu

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 4730079, China
Interests: atmospheric aerosol–PBL–cloud interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is a great pleasure to present this Special Issue “Methods and Applications of Lidar Remote Sensing” to be published in the journal Applied Sciences.

As an advanced technology, lidar has attracted increasing interest as a topic of research in recent years. With the development of lidar hardware, the science and technology of lidar remote sensing is rapidly developing, with substantial output of meaningful scientific achievements. Given the wide application of lidar remote sensing in the fields of atmosphere, environment, vegetation, surveying and mapping, and so on, the development of lidar remote sensing has ushered in great opportunities and challenges. Therefore, the main goal of this Special Issue is to summarize achievements in the development of lidar remote sensing technology, discuss and look forward to the future directions of development, and promote the rapid application of lidar remote sensing in different fields.

This Special Issue is particularly encouraging submissions on the methods and applications of lidar remote sensing for retrieving atmosphere parameters, monitoring environmental quality, estimating vegetation status from leaf and canopy, etc. Related hardware design and retrieval algorithms for ground-based, airborne, and space-based lidar sensors are also of interest, as are data fusion approaches for acquiring new data with higher accuracy and temporal and spatial resolution. In summary, this Special Issue invite submissions exploring cutting-edge research and recent advances in the fields of lidar remote sensing. Both theoretical and experimental studies are welcome, as are comprehensive review and survey papers.

Dr. Wei Wang
Dr. Jian Yang
Dr. Ge Han
Dr. Boming Liu
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. Applied Sciences 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 2400 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
  • multispectral lidar
  • remote sensing
  • atmosphere
  • boundary layer
  • environmental
  • vegetation
  • aerosol
  • CO2
  • leaf biochemical content

Published Papers (1 paper)

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Research

15 pages, 5398 KiB  
Article
A Novel Ground Filtering Method for Point Clouds in a Forestry Area Based on Local Minimum Value and Machine Learning
by Yueqiao Wu, Mengting Sang and Wei Wang
Appl. Sci. 2022, 12(18), 9113; https://doi.org/10.3390/app12189113 - 10 Sep 2022
Cited by 5 | Viewed by 1705
Abstract
Lidar point cloud filtering is the process of separating ground points from non-ground points and is a particularly important part of point cloud data processing. Forest filtering has always been a difficult topic in point cloud filtering research. Given that vegetation cannot be [...] Read more.
Lidar point cloud filtering is the process of separating ground points from non-ground points and is a particularly important part of point cloud data processing. Forest filtering has always been a difficult topic in point cloud filtering research. Given that vegetation cannot be completely summarized according to the structure of ground objects, and given the diversity and complexity of the terrain in woodland areas, filtering in the forest area is a particularly difficult task. However, only few studies have tested the application of the point cloud filtering method for forest areas, the parameter setting of filtering methods is highly complex, and their terrain adaptability is weak. This paper proposes a new filtering method for forest areas that effectively combines iterative minima with machine learning, thereby greatly reducing the degree of manual participation. Through filtering tests on three types of woodlands, the filtering results were evaluated based on the filtering error definition proposed by ISPRS and were compared with the filtering results of other classical methods. Experimental results highlight the advantages of the proposed method, including its high accuracy, strong terrain universality, and limited number of parameters. Full article
(This article belongs to the Special Issue Methods and Applications of Lidar Remote Sensing)
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