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Forests
Special Issues
Applications of LiDAR and Photogrammetry for Forests
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Applications of LiDAR and Photogrammetry for Forests

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: 20 February 2026 | Viewed by 670

Special Issue Editors

Dr. Azadeh Abdollahnejad

E-Mail Website
Guest Editor
1. Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece
2. Geosystem Hellas S.A., Athens, Greece
Interests: photogrammetry; remote sensing; forestry; data sciences; UAV
Dr. Dimitrios Panagiotidis

E-Mail Website
Guest Editor
Research Unit Geographical Information System, Agricultural University of Athens, Athens, Greece
Interests: remote sensing of vegetation; photogrammetry; LiDAR; GIS; UAS; forestry, multi-spectral image analysis

Special Issue Information

Dear Colleagues,

Remote sensing technologies have revolutionized forest science. Over the past two decades, LiDAR (light detection and ranging) and photogrammetric methods have enabled detailed three-dimensional mapping of canopy structure, terrain, and vegetation, vastly expanding what can be learned about forests. In forestry, these techniques allow precise measurement of tree height, stem density, canopy cover, and terrain models, supporting applications from forest inventory to ecosystem monitoring. This Special Issue, “Applications of LiDAR and Photogrammetry for Forests”, invites original research and review papers that showcase recent advances and applications of these technologies for forest analysis. Topics include (but are not limited to) forest inventory and structural assessment using LiDAR and photogrammetry, biomass and carbon stock estimation, habitat mapping and biodiversity assessment, change detection in forested landscapes, fusion of LiDAR/photogrammetric data with other remote sensing imagery, and the use of UAV, airborne, and terrestrial LiDAR systems. We also welcome contributions on advances in point cloud processing, 3D reconstruction methods for forest monitoring, and novel methodologies or case studies demonstrating the value of LiDAR and photogrammetry in forestry. Submissions that address new analytical techniques, software tools, or interdisciplinary applications in these areas are particularly encouraged.

Dr. Azadeh Abdollahnejad
Dr. Dimitrios Panagiotidis
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 250 words) can be sent to the Editorial Office for assessment.

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
  • photogrammetry
  • forest inventory forest structure
  • biomass and carbon stock
  • habitat mapping
  • biodiversity assessment
  • change detection
  • point cloud processing
  • UAV and remote sensing

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Published Papers (1 paper)

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Research

24 pages, 15753 KB  
Article
A Novel Canopy Height Mapping Method Based on UNet++ Deep Neural Network and GEDI, Sentinel-1, Sentinel-2 Data
by Xingsheng Deng, Xu Zhu, Zhongan Tang and Yangsheng You
Forests 2025, 16(11), 1663; https://doi.org/10.3390/f16111663 - 30 Oct 2025
Viewed by 433
Abstract
As a vital carbon reservoir in terrestrial ecosystems, forest canopy height plays a pivotal role in determining the precision of biomass estimation and carbon storage calculations. Acquiring an accurate Canopy Height Map (CHM) is crucial for building carbon budget models at regional and [...] Read more.
As a vital carbon reservoir in terrestrial ecosystems, forest canopy height plays a pivotal role in determining the precision of biomass estimation and carbon storage calculations. Acquiring an accurate Canopy Height Map (CHM) is crucial for building carbon budget models at regional and global scales. A novel UNet++ deep-learning model was constructed using Sentinel-1 and Sentinel-2 multispectral remote sensing images to estimate forest canopy height data based on full-waveform LiDAR measurements from the Global Ecosystem Dynamics Investigation (GEDI) satellite. A 10 m resolution CHM was generated for Chaling County, China. The model was evaluated using independent validation samples, achieving an R2 of 0.58 and a Root Mean Square Error (RMSE) of 3.38 m. The relationships between multiple Relative Height (RH) metrics and field validation data are examined. It was found that RH98 showed the strongest correlation, with an R2 of 0.56 and RMSE of 5.83 m. Six different preprocessing algorithms for GEDI data were evaluated, and the results demonstrated that RH98 processed using the ‘a1’ algorithm achieved the best agreement with the validation data, yielding an R2 of 0.55 and RMSE of 5.54 m. The impacts of vegetation coverage, assessed through Normalized Difference Vegetation Index (NDVI), and terrain slope on inversion accuracy are explored. The highest accuracy was observed in areas where NDVI ranged from 0.25 to 0.50 (R2 = 0.77, RMSE = 2.27 m) and in regions with slopes between 0° and 10° (R2 = 0.61, RMSE = 2.99 m). These results highlight that the selection of GEDI data preprocessing methods, RH metrics, vegetation density, and terrain characteristics (slope) all have significant impacts on the accuracy of canopy height estimation. Full article
(This article belongs to the Special Issue Applications of LiDAR and Photogrammetry for Forests)
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