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Forests
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Forest Resources Inventory, Monitoring, and Assessment
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Forest Resources Inventory, Monitoring, and Assessment

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 March 2026 | Viewed by 1320

Special Issue Editor

Prof. Dr. Weisheng Zeng

E-Mail Website
Guest Editor
Academy of Inventory and Planning, National Forestry and Grassland Administration, Beijing, China
Interests: forest resources inventory and monitoring; forestry modeling; statistics; sampling; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest resources inventory, monitoring, and assessment are important foundations for scientific forest management, planning implementation assessment, and reasonable decision-making. National forest inventory (NFI) mainly serves macro forestry decision-making, forestry development planning, and global forest resource assessment, while forest management inventory (FMI) mainly serves scientific forest management and sustainable development. Integrated monitoring that combines NFI and FMI can meet the needs of information on forest resources at different levels such as global, regional, national and local, and should become part of the future development of forest resource inventory and monitoring. With the implementation of carbon peaking and carbon neutrality strategies, forest biomass and forest carbon storage have increasingly become as extremely important factors as forest stock volume in forest resource inventory and monitoring. In addition, the integrated application of traditional technologies, such as measuring, sampling, and modeling, with modern technologies, such as remote sensing, global positioning, geographic information systems, and AI technology, would facilitate the enhanced efficiency and quality of forest resource inventory, monitoring, and assessment. The comprehensive monitoring of various natural resources such as forests, grasslands, and wetlands is set to become a future development trend.

Prof. Dr. Weisheng Zeng
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. 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

  • national forest inventory
  • forest management inventory
  • integrated monitoring
  • biomass and carbon monitoring
  • remote sensing application
  • ecosystem services assessment

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Published Papers (2 papers)

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Research

20 pages, 5982 KB  
Article
Estimating Growing Stock Volume at Tree and Stand Levels for Chinese Fir (Cunninghamia lanceolata) in Southern China Using UAV Laser Scanning
by Zhigang Yang, Zexin Guo, Jianpei Zhou, Kang Shen, Die Zhong, Xinfu Feng, Sheng Ding and Jinsheng Ye
Forests 2025, 16(12), 1779; https://doi.org/10.3390/f16121779 - 27 Nov 2025
Viewed by 266
Abstract
UAV laser scanning (UAV-LS) combines extensive scanning coverage with high point cloud density, enabling efficient and precise acquisition of key forest attributes. Based on field-measured data and UAV-LS data from 138 Chinese fir (Cunninghamia lanceolata) plantation plots in southern China, this [...] Read more.
UAV laser scanning (UAV-LS) combines extensive scanning coverage with high point cloud density, enabling efficient and precise acquisition of key forest attributes. Based on field-measured data and UAV-LS data from 138 Chinese fir (Cunninghamia lanceolata) plantation plots in southern China, this study systematically developed growing stock volume (GSV) estimation models at both tree and stand levels. The models included base models (allometric models), linear models, dummy variable models incorporating age groups, and nonlinear mixed-effects models incorporating random effects (plot and area levels for the tree level, and only the area level for the stand level). The results showed the following: (1) Stand-level GSV prediction relied primarily on height metrics, achieving optimal performance through a combination of the 10th cumulative height percentile (AIH10) and canopy cover (CC), both of which showed near-linear relationships with GSV; tree-level GSV was predicted by LiDAR-derived tree height (LH) and crown width (LCW), with LH explaining most variation. (2) Tree-level models achieved R2 = 0.639–0.725 and RMSE = 0.050–0.058 m3, exhibiting larger individual prediction errors (mean percentage standard error, MPSE > 30%) with smaller aggregate prediction errors (mean prediction error, MPE < 1%); stand-level models reached R2 = 0.785–0.879 and RMSE = 46.052–61.314 m3 ha−1 while maintaining controlled errors across scales (MPE < 5%, MPSE < 20%). (3) At both the tree and stand levels, the nonlinear mixed-effects model outperformed the others, followed by the dummy variable model and the base model, with the linear model exhibiting the worst performance; area-level random effects primarily influenced the baseline value of tree-level GSV and the allometric relationship between stand-level GSV and AIH10, whereas plot-level random effects affected the allometric relationships of tree-level GSV with LH and LCW. This study confirms the effectiveness of UAV-LS for large-scale forest resource monitoring, while underscoring the necessity of incorporating spatial heterogeneity in GSV estimation. Full article
(This article belongs to the Special Issue Forest Resources Inventory, Monitoring, and Assessment)
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20 pages, 3300 KB  
Article
Development of an Integrated Forestry Survey Device for Tree Height and DBH
by Ao Xu, Xianfang Zheng, Kejie Zhao, Shaobin Zhang, Linhao Sun and Luming Fang
Forests 2025, 16(10), 1529; https://doi.org/10.3390/f16101529 - 30 Sep 2025
Viewed by 423
Abstract
Tree diameter at breast height (DBH) and tree height are important quantitative attributes in forestry surveys. They serve as essential data for calculating forest stock, growth, and carbon sequestration, and are of significant research value for forest health assessments and other research outcomes. [...] Read more.
Tree diameter at breast height (DBH) and tree height are important quantitative attributes in forestry surveys. They serve as essential data for calculating forest stock, growth, and carbon sequestration, and are of significant research value for forest health assessments and other research outcomes. To improve the efficiency of forest resource inventories and to reduce labor costs, a forestry survey device integrating multiple sensors has been developed. Based on the principles of laser ranging and the tunnel magnetoresistance effect, this device integrates both the DBH and tree height measurements. Compared to traditional measurement methods, it boasts a compact size, low cost, and high measurement accuracy. Experimental applications have shown that the average root mean square error (RMSE) of tree height measurements ranges from 31 to 55 cm, while the DBH measurement accuracy reaches 98%, We acknowledge that, although this accuracy meets the requirements for general forestry surveys, it still falls short of the accuracy required for high-precision forest resource surveys (<20 cm), which points to a direction for future improvement. Full article
(This article belongs to the Special Issue Forest Resources Inventory, Monitoring, and Assessment)
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