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Forests
Special Issues
Applications of LiDAR in Forestry: Challenges, Opportunities and the Future
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Applications of LiDAR in Forestry: Challenges, Opportunities and the Future

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: 30 September 2025 | Viewed by 1167

Special Issue Editors

Dr. Jozef Výbošťok

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Guest Editor
Faculty of Forestry, Technical University in Zvolen, 96001 Zvolen, Slovakia
Interests: LiDAR remote sensing; 3D point cloud analysis; forest inventory; GIS
Dr. Arunima Singh

E-Mail Website
Guest Editor
Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Kamycká 129, 16500 Prague, Czech Republic
Interests: LiDAR and SAR; forests
Dr. Juliána Chudá

E-Mail Website
Guest Editor
Department of Forest Harvesting, Logistics and Ameliorations, Faculty of Forestry, Technical University in Zvolen, Zvolen, Slovakia
Interests: forest mapping; SLAM; laser scanning

Special Issue Information

Dear Colleagues,

The past decade has witnessed the rapid development of lidar systems and their applications in various fields, including in forestry.  LiDAR sensors (e.g., static or mobile, terrestrial, airborne, Unmanned Aerial Vehicle, satellite, backpack, and handheld) have been widely used in different forest applications. LiDAR data have a wide application and can significantly help in the field of forest management, forest inventory, biomass conservation, and forest protection.

This Special Issue welcomes studies covering the applications of LiDAR technologies in forestry and data processing, as well as the applications of spaceborne, airborne, and ground-based LiDAR systems. Articles may address, but are not limited to, the following topics:

  • Terrestrial laser scanning;
  • Handheld mobile laser scanning;
  • Photogrammetry;
  • Unmanned Aerial Vehicle (UAV) lidar applications;
  • Space-based LiDAR applications in forestry;
  • Postprocessing of data;
  • Machine learning and deep learning approaches in the processing of LiDAR data;
  • Application of LiDAR technologies in urban forest;
  • Forest biomass estimation using LiDAR data. 

Dr. Jozef Výbošťok
Dr. Arunima Singh
Dr. Juliána Chudá
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
  • forest management
  • forest remote sensing
  • forest ecosystems
  • data fusion
  • individual tree detection
  • biomass estimation
  • tree species identification

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Published Papers (2 papers)

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Research

14 pages, 10909 KiB  
Article
Impact of Backpack LiDAR Scan Routes on Diameter at Breast Height Estimation in Forests
by Longwei Li, Linjia Wei, Nan Li, Shijun Zhang, Mengyi Hu and Jing Ma
Forests 2025, 16(3), 527; https://doi.org/10.3390/f16030527 - 16 Mar 2025
Cited by 1 | Viewed by 278
Abstract
Forest resource surveys are of vital importance for grasping the current status of forest resources, formulating management strategies, and evaluating ecosystem functions. Traditional manual measurement methods have numerous limitations in complex forest environments. The emergence of LiDAR technology has provided a new approach. [...] Read more.
Forest resource surveys are of vital importance for grasping the current status of forest resources, formulating management strategies, and evaluating ecosystem functions. Traditional manual measurement methods have numerous limitations in complex forest environments. The emergence of LiDAR technology has provided a new approach. Backpack LiDAR has been increasingly applied due to its portability and flexibility. However, there is a lack of comprehensive research on the influence of different scanning routes on data quality and analysis results. In this study, forest plots of four tree species, namely Carya cathayensis, Cinnamomum camphora, Koelreuteria bipinnata, and Quercus acutissima in Chuzhou City, Anhui Province, were selected as the research objects. Six scanning routes were designed to collect point cloud data using backpack LiDAR. After preprocessing, including denoising and ground point classification, diameter at breast height (DBH) fitting and accuracy evaluation were carried out. The results indicated that the individual tree recognition rates of C. cathayensis, C. camphora, and K. bipinnata reached 100%, while that of Q. acutissima was between 64.71% and 78.07% and was significantly affected by the scanning route. The DBH fitting accuracy of each tree species varied among different routes. For example, C. cathayensis had high accuracy in routes 1 and 6, and C. camphora had high accuracy in routes 1 and 3. Tree species characteristics, scanning routes, and data processing methods jointly affected the DBH fitting accuracy. This study provides a basis for the application of backpack LiDAR in forest resource surveys. Although backpack LiDAR has advantages, it is still necessary to optimize data acquisition schemes targeting tree species characteristics and improve point cloud data processing algorithms to promote its in-depth application in the forestry field. Full article
(This article belongs to the Special Issue Applications of LiDAR in Forestry: Challenges, Opportunities and the Future)
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19 pages, 5510 KiB  
Article
Unveiling Population Structure Dynamics of Populus euphratica Riparian Forests Along the Tarim River Using Terrestrial LiDAR
by Alfidar Arkin, Asadilla Yusup, Ümüt Halik, Abdulla Abliz, Ailiya Ainiwaer, Aolei Tian and Maimaiti Mijiti
Forests 2025, 16(2), 368; https://doi.org/10.3390/f16020368 - 18 Feb 2025
Viewed by 487
Abstract
The Populus euphratica desert riparian forest, predominantly distributed along the Tarim River in northwestern China, has experienced significant degradation due to climate change and anthropogenic activities. Despite its ecological importance, systematic assessments of P. euphratica stand structure across the entire Tarim River remain [...] Read more.
The Populus euphratica desert riparian forest, predominantly distributed along the Tarim River in northwestern China, has experienced significant degradation due to climate change and anthropogenic activities. Despite its ecological importance, systematic assessments of P. euphratica stand structure across the entire Tarim River remain scarce. This study employed terrestrial laser scanning (TLS) to capture high-resolution 3D structural data from 2741 individual trees across 30 plots within six transects, covering the 1300 km mainstream of the Tarim River. ANOVA, PCA, and RDA were applied to examine tree structure variation and environmental influences. Results revealed a progressive decline in key structural parameters from the upper to lower reaches of the river, with the lower reaches showing pronounced degradation. Stand density decreased from 440 to 257 trees per hectare, mean stand height declined from 9.3 m to 5.6 m, mean crown diameter reduced from 4.1 m to 3.8 m, canopy cover dropped from 62% to 42%, and the leaf area index fell from 0.51 to 0.29. Age class distributions varied along the river, highlighting population structures indicative of growth in the upper reaches, stability in the middle reaches, and decline in the lower reaches. Abiotic factors, including groundwater depth, soil salinity, soil moisture, and precipitation, exhibited strong correlations with stand structural parameters (p < 0.05, R2 ≥ 0.69). The findings highlight significant spatial variations in tree structure, with healthier growth in the upper reaches and degradation in the lower reaches, enhance our understanding of forest development processes, and emphasize the urgent need for targeted conservation strategies. This comprehensive quantification of P. euphratica stand structure and its environmental drivers offer valuable insights into the dynamics of desert riparian forest ecosystems. The findings contribute to understanding forest development processes and provide a scientific basis for formulating effective conservation strategies to sustain these vital desert ecosystems, as well as for the monitoring of regional environmental changes. Full article
(This article belongs to the Special Issue Applications of LiDAR in Forestry: Challenges, Opportunities and the Future)
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