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Forests
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LiDAR Remote Sensing for Forestry
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LiDAR Remote Sensing for Forestry

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 1620

Special Issue Editors

Dr. Zhenyang Hui

E-Mail Website
Guest Editor
School of Surveying and Geoinformation Engineering, East China University of Technology (ECUT), Nanchang 330013, China
Interests: LiDAR remote sensing; 3D point cloud analysis; forest inventory
Prof. Dr. Penggen Cheng

E-Mail Website
Guest Editor
School of Surveying and Geoinformation Engineering, East China University of Technology (ECUT), Nanchang 330013, China
Interests: LiDAR remote sensing; geographical analysis; spatial analysis
Prof. Dr. Bo Liu

E-Mail Website
Guest Editor
School of Surveying and Geoinformation Engineering, East China University of Technology (ECUT), Nanchang 330013, China
Interests: fuzzy topology; spatial database; 3D data processing
Dr. Mark Vanderwel

E-Mail Website
Guest Editor
Department of Biology, University of Regina, 3737 Wascana Pkwy, Regina, SK S4S 0A2, Canada
Interests: tree demography; stand dynamics; forest simulation modelling; global change; forest distributions; carbon sequestration; LiDAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests are essential to maintaining ecological function, biodiversity and the health of the planet. To better investigate forest resources and promote the study of tree growth mechanisms, it is urgent to obtain more accurate and timely forest inventory information. In recent decades, with continuous improvements made to the measurement accuracy and sampling rates of laser scanners, LiDAR has been widely employed for calculating tree metrics (e.g., height, diameter at breast height (DBH), crown width), estimating above-ground biomass (AGB), and identifying tree species remotely. Nonetheless, existing studies continue to encounter the challenges of low accuracy or low robustness across different forest environments. Thus, this Special Issue focuses on the latest studies addressing forest inventory using LiDAR technology. We hope this Special Issue will elevate research interest in LiDAR technology and unleash its geometric and topological potential within the forest and plant sciences. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Multi-platform point cloud fusion;
  • Filtering for forest environment;
  • Individual tree detection;
  • Biomass estimation;
  • Tree species identification;
  • Quantitative structure modeling for trees;
  • Forest parameters estimation;
  • Forest ecology;
  • Carbon cycle analysis;
  • Forest planning and management.

Dr. Zhenyang Hui
Prof. Dr. Penggen Cheng
Prof. Dr. Bo Liu
Dr. Mark Vanderwel
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. Forests is an international peer-reviewed open access monthly 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
  • point cloud
  • forest inventory
  • filtering
  • biomass
  • quantitative structure model
  • forest dynamics
  • forest structure analysis
  • carbon cycle/sequestration
  • forest management

Published Papers (2 papers)

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Research

16 pages, 8621 KiB  
Article
Extracting the DBH of Moso Bamboo Forests Using LiDAR: Parameter Optimization and Accuracy Evaluation
by Longwei Li, Linjia Wei, Nan Li, Shijun Zhang, Zhicheng Wu, Miaofei Dong and Yuyun Chen
Forests 2024, 15(5), 804; https://doi.org/10.3390/f15050804 - 2 May 2024
Viewed by 479
Abstract
The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and [...] Read more.
The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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16 pages, 10176 KiB  
Article
Evaluation of Accuracy in Estimating Diameter at Breast Height Based on the Scanning Conditions of Terrestrial Laser Scanning and Circular Fitting Algorithm
by Yongkyu Lee and Jungsoo Lee
Forests 2024, 15(2), 313; https://doi.org/10.3390/f15020313 - 7 Feb 2024
Viewed by 804
Abstract
A growing societal interest exists in the application of lidar technology to monitor forest resource information and forestry management activities. This study examined the possibility of estimating the diameter at breast height (DBH) of two tree species, Pinus koraiensis (PK) and [...] Read more.
A growing societal interest exists in the application of lidar technology to monitor forest resource information and forestry management activities. This study examined the possibility of estimating the diameter at breast height (DBH) of two tree species, Pinus koraiensis (PK) and Larix kaempferi (LK), by varying the number of terrestrial laser scanning (TLS) scans (1, 3, 5, 7, and 9) and DBH estimation methods (circle fitting [CF], ellipse fitting [EF], circle fitting with RANSAC [RCF], and ellipse fitting with RANSAC [REF]). This study evaluates the combination that yields the highest estimation accuracy. The results showed that for PK, the lowest RMSE of 0.97 was achieved when REF was applied to the data from nine scans after noise removal. For LK, the lowest RMSE of 1.03 was observed when applying CF to the data from seven scans after noise removal. Furthermore, ANOVA revealed no significant difference in the estimated DBH from nine scans when more than three scans were used for CF and RCF and more than five for EF and REF. These results are expected to be useful in establishing efficient and accurate DBH estimation plans using TLS for forest resource monitoring. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing for Forestry)
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