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Remote Sensing
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Close-Range LiDAR for Forest Structure and Dynamics Monitoring
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Close-Range LiDAR for Forest Structure and Dynamics Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 4122

Special Issue Editor

Dr. Panagiotidis Dimitrios

E-Mail Website
Guest Editor
Research Unit-Geographical Information System, Agricultural University of Athens (AUA), Iερά οδός 75, 118 55 Athens, Greece
Interests: forest management; remote sensing of vegetation; GIS; photogrammetry; LiDAR; computer programming; 3D modeling; UAS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

LiDAR technology is transforming forestry by providing precise, high-resolution, three-dimensional data about forest structures and dynamics. This capability has made small-scale forestry applications essential for gathering the detailed information necessary for effective forest management planning (FMP). Close-range LiDAR sensing has become an invaluable tool for in-depth forest research and management, demonstrating its versatility across various environments and biomes. Its applications in forest inventory, biomass estimation, health status monitoring, and habitat modeling are crucial for sustainable forest management, conservation, and climate change studies. Therefore, the integration of multi-source data and multi-scale studies focused on forest ecosystem services, such as monitoring and mapping, is highly encouraged.

This Special Issue aims to explore diverse applications of LiDAR remote sensing in studying forest structure and dynamics, utilizing various platforms and sensors within forest sciences. Topics can range from estimating forest parameters at the tree or plot level to broader, more comprehensive objectives and scales. For instance, continuous forest-cover studies over large areas using LiDAR can offer invaluable insights into forest growth and dynamics, including soil fertility and other soil characteristics. This approach, involving regular monitoring and detailed analysis over time, enables a deeper understanding of forest development and responses to various factors.

  • Contributions to this Special Issue may address, but are not limited to, the following topics:
  • Forest change;
  • Forest ecology;
  • The estimation of tree, plot, and stand variables for forest inventory purposes;
  • Forest monitoring and mapping;
  • Canopy height measurements for carbon storage and biomass;
  • Species recognition;
  • Health status assessment;
  • Forest planning and management;
  • Forest soil.

Dr. Panagiotidis Dimitrios
Guest Editor

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. Remote Sensing 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 2700 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

• Forest Growth & Dynamics • Airborne Laser Scanning • Terrestrial Laser Scanning • 3D Point Clouds • Unmanned Aerial Systems • Forest Inventory • Forest Monitoring & Mapping • UAV

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Published Papers (3 papers)

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Research

19 pages, 14391 KB  
Article
Exploratory Analyses of Cross-Species Phenological–Structural Relationships in Urban Park Trees by Using Sentinel-2 Images and Handheld LiDAR Data
by Miao Jiang, Yi Lin and Minghua Cheng
Remote Sens. 2026, 18(8), 1192; https://doi.org/10.3390/rs18081192 - 16 Apr 2026
Viewed by 332
Abstract
Understanding the interplay between tree structure and seasonal dynamics, particularly cross-species, is crucial for managing urban forest ecosystems. However, balancing fine-scale inventory of trees with large-area mapping of forest ecosystems is a challenge. This endeavor integrates multi-temporal Sentinel-2 satellite remote sensing (RS) imagery [...] Read more.
Understanding the interplay between tree structure and seasonal dynamics, particularly cross-species, is crucial for managing urban forest ecosystems. However, balancing fine-scale inventory of trees with large-area mapping of forest ecosystems is a challenge. This endeavor integrates multi-temporal Sentinel-2 satellite remote sensing (RS) imagery with high-density handheld light detection and ranging (LiDAR) point clouds to launch exploratory analyses of cross-species phenological–structural relationships (CSPSRs) in urban park trees. We derived plot-level phenological metrics (e.g., start of growing season, SOS) and quantified fine-scale three-dimensional (3D) tree structural attributes (e.g., tree height and trunk curvature), respectively. Then, we investigated how the 3D structural attributes of urban park trees covary with their phenological traits. The results revealed the underlying CSPSRs, e.g., a weak but significant negative correlation between SOS and tree height in the study area. The derived CSPSRs demonstrate that tree structure is a key predictor of its phenology, even across species. Overall, the integrated RS approach can provide a robust framework for associating the structure and phenology of trees, offering valuable insights for the ecological management of urban forests. Full article
(This article belongs to the Special Issue Close-Range LiDAR for Forest Structure and Dynamics Monitoring)
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19 pages, 6278 KB  
Article
Selecting the Optimal Approach for Individual Tree Segmentation in Euphrates Poplar Desert Riparian Forest Using Terrestrial Laser Scanning
by Asadilla Yusup, Xiaomei Hu, Ümüt Halik, Abdulla Abliz, Maierdang Keyimu and Shengli Tao
Remote Sens. 2025, 17(23), 3852; https://doi.org/10.3390/rs17233852 - 28 Nov 2025
Viewed by 850
Abstract
Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and [...] Read more.
Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and conducting forest inventories due to its complex stand structure and overlapping crowns. To determine the most effective ITS approach for P. euphratica, we benchmarked six commonly used tree segmentation approaches for terrestrial laser scanning (TLS) data: canopy height model segmentation (CHMS), point cloud segmentation (PCS), comparative shortest-path algorithm (CSP), stem location seed point segmentation (SPS), deep-learning trunk-based segmentation (TBS), and leaf–wood separation-based segmentation (LWS). All methods followed a unified preprocessing and tuning protocol. We evaluated these methods based on tree-count accuracy, crown delineation, and structural attributes such as tree height (H), diameter at breast height (DBH), and crown diameter (CD). The results indicated that the TBS and LWS methods performed the best, achieving a mean tree-count accuracy of 98%, while the CHMS method averaged only 46%. These two methods provide the basic branch structure within the tree crown, reducing the likelihood of incorrect segmentation. Validation against field-measured values for H, DBH, and CD showed that both the TBS and LWS methods achieved accuracies exceeding 80% (RMSE = 0.8 m), 86% (RMSE = 0.02 m), and 73% (RMSE = 0.7 m), respectively. For TLS data in P. euphratica desert riparian forests, these two methods provide the most reliable results, facilitating rapid plot-scale inventory and monitoring. These findings establish a practical basis for conducting high-accuracy inventories of Euphrates poplar desert riparian forests. Full article
(This article belongs to the Special Issue Close-Range LiDAR for Forest Structure and Dynamics Monitoring)
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24 pages, 6603 KB  
Article
Advancing Forest Inventory in Tropical Rainforests: A Multi-Source LiDAR Approach for Accurate 3D Tree Modeling and Volume Estimation
by Zongzhu Chen, Ziwei Lin, Tiezhu Shi, Dongping Deng, Yiqing Chen, Xiaoyan Pan, Xiaohua Chen, Tingtian Wu, Jinrui Lei and Yuanling Li
Remote Sens. 2025, 17(17), 3030; https://doi.org/10.3390/rs17173030 - 1 Sep 2025
Cited by 2 | Viewed by 2083
Abstract
This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses [...] Read more.
This study proposes an Automatic Branch Modeling (ABM) framework that combines AdTree and AdQSM algorithms to reconstruct individual tree models and estimate timber volume from fused Hand-held Laser Scanners (HLS) and Unmanned Aerial Vehicle Laser Scanners (UAV-LS) point cloud data. The research focuses on two 50 × 50 m primary tropical rainforest plots in Hainan Island, China, characterized by dense and vertically stratified vegetation. Key steps include multi-source point cloud registration and noise removal, individual tree segmentation using the Comparative Shortest Path (CSP) algorithm, extraction of diameter at breast height (DBH) and tree height, and 3D reconstruction and volume estimation via cylindrical fitting and convex polyhedron decomposition. Results demonstrate high accuracy in parameter extraction, with DBH estimation achieving R2 = 0.89–0.90, RMSE = 2.93–3.95 cm and RMSE% = 13.95–14.75%, while tree height estimation yielded R2 = 0.89–0.94, RMSE = 1.26–1.81 m and RMSE% = 9.41–13.2%. Timber volume estimates showed strong agreement with binary volume models (R2 = 0.90–0.94, RMSE = 0.10–0.18 m3, RMSE% = 32.33–34.65%), validated by concordance correlation coefficients (CCC) of 0.95–0.97. The fusion of HLS (ground-level trunk details) and UAV-LS (canopy structure) data significantly improved structural completeness, overcoming occlusion challenges in dense forests. This study highlights the efficacy of multi-source LiDAR fusion and 3D modeling for precise forest inventory in complex ecosystems. The ABM framework provides a scalable, non-destructive alternative to traditional methods, supporting carbon stock assessment and sustainable forest management in tropical rainforests. Future work should refine individual tree segmentation and wood-leaf separation to further enhance accuracy in heterogeneous environments. Full article
(This article belongs to the Special Issue Close-Range LiDAR for Forest Structure and Dynamics Monitoring)
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