Multi-Source Data Application for Forestry Conservation

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 May 2026 | Viewed by 1104

Special Issue Editors

1. Ecology and Nature Conservation Institute, Chinese Academy of Forestry, Beijing 100091, China
2. Key Laboratory of Biodiversity Conservation of National Forestry and Grassland Administration, Beijing 100091, China
Interests: forest monitoring; landscape dynamics; remote sensing; ecosystem service

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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
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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: land use/cover change; ecological assessment; land degradation; remote sensing

Special Issue Information

Dear Colleagues,

Forest and vegetation resource surveys are essential for sustainable ecosystem management, biodiversity conservation, and climate change mitigation. They provide fundamental data to support effective policy-making, ecological restoration, and environmental protection. Traditional ground-based surveys are often labor-intensive and costly, especially in remote or topographically complex regions. The rapid development of remote sensing technologies, such as satellite imagery, unmanned aerial vehicles (UAVs), and airborne LiDAR, has created new opportunities for ecological monitoring and resource assessment. These technologies enable the acquisition of multi-resolution, timely, and cost-effective spatial information, supporting precise vegetation mapping, forestry conservation, and ecological assessment.

This Special Issue, “Multi-Source Data Application for Forestry Conservation”, seeks to advance research on remotely sensed data in forestry application and ecological conservation strategies. Potential topics include, but are not limited to, the following: (i) filtering and processing methods for UAV-LiDAR and UAV imagery in various landscapes; (ii) forest monitoring, vegetation classification, and tree species identification; (iii) biomass modeling, carbon storage estimation, NDVI- and EVI-based vegetation indices, wildfire detection, illegal logging detection, and dynamic change detection; (iv) resource surveying, biodiversity assessment, and ecological restoration; and (v) conservation and management strategies in urban green spaces, forest parks, protected areas, or national parks. By sharing innovative approaches and case studies, this Special Issue aims to highlight the latest advances in remote sensing technology, fostering a deeper understanding of its potential to support sustainable forestry practices and ecological conservation initiatives worldwide.

Dr. Sisi Yu
Prof. Dr. Zhenyang Hui
Dr. Xiao Wang
Guest Editors

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Keywords

  • vegetation index
  • remote sensing
  • greenness
  • spatiotemporal dynamic
  • ecological conservation
  • environmental assessment
  • forestry management
  • forestry application
  • multi-source data

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Published Papers (1 paper)

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Research

13 pages, 4157 KB  
Article
Automatic Registration of Terrestrial and UAV LiDAR Forest Point Clouds Through Canopy Shape Analysis
by Sisi Yu, Zhanzhong Tang, Beibei Zhang, Jie Dai and Shangshu Cai
Forests 2025, 16(8), 1347; https://doi.org/10.3390/f16081347 - 19 Aug 2025
Viewed by 722
Abstract
Accurate registration of multi-platform light detection and ranging (LiDAR) point clouds is essential for detailed forest structure analysis and ecological monitoring. In this study, we developed a novel two-stage method for aligning terrestrial and unmanned aerial vehicle LiDAR point clouds in forest environments. [...] Read more.
Accurate registration of multi-platform light detection and ranging (LiDAR) point clouds is essential for detailed forest structure analysis and ecological monitoring. In this study, we developed a novel two-stage method for aligning terrestrial and unmanned aerial vehicle LiDAR point clouds in forest environments. The method first performs coarse alignment using canopy-level digital surface models and Fast Point Feature Histograms, followed by fine registration with Iterative Closest Point. Experiments conducted in six forest plots achieved an average registration accuracy of 0.24 m within 5.14 s, comparable to manual registration but with substantially reduced processing time and human intervention. In contrast to existing tree-based methods, the proposed approach eliminates the need for individual tree segmentation and ground filtering, streamlining preprocessing and improving scalability for large-scale forest monitoring. The proposed method facilitates a range of forest applications, including structure modeling, ecological parameter retrieval, and long-term change detection across diverse forest types and platforms. Full article
(This article belongs to the Special Issue Multi-Source Data Application for Forestry Conservation)
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