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Close-Range LiDAR for Forest Structure and Dynamics Monitoring

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Dr. Panagiotidis Dimitrios

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Department of Forest Management, Czech University of Life Sciences Prague, Prague, Czech Republic
Interests: forest management; remote sensing of vegetation; G.I.S; photogrammetry; LiDAR; computer programming; 3D modeling; UAS

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

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24 pages, 6603 KB

Open AccessArticle

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

Viewed by 868

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 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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