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Forests
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Applications of Artificial Intelligence in Forestry: 2nd Edition
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Applications of Artificial Intelligence in Forestry: 2nd Edition

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 October 2025 | Viewed by 1201

Special Issue Editors

Prof. Dr. Guojie Wang

E-Mail Website
Guest Editor
School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: climate change; water cycle; vegetation change; remote sensing; vegetation–climate interactions
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zengxin Zhang

E-Mail Website
Guest Editor
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
Interests: forest meteorology; forest hydrology; climate change; forest carbon estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second edition of the Special Issue 'Applications of Artificial Intelligence in Forestry' (https://www.mdpi.com/journal/forests/special_issues/T4SHY8C92E), following the success of the first edition, which published 12 articles. We look forward to receiving more high-quality contributions in this edition.

Recent advances in big data in Earth observations have fostered interdisciplinary studies of forest dynamics and management, as well as their interactions with the environment. Artificial intelligence (AI) provides an interesting and efficient solution for big data applications in forestry. AI-based approaches, e.g., a variety of deep learning models, are currently mainly dedicated to forest monitoring, assessment, mapping, and predictions, e.g., using satellite remote sensing images, for smart decision making in forest management. In such cases, deep learning models have indicated excellent performances. In the era of big data, there are emerging opportunities to utilize deep learning models to improve our understanding of forest dynamics and forest–climate interactions in the warming environment, and explainable artificial intelligence methods can be used to obtain explanations of the model results. Therefore, original research papers using AI approaches to improve our understanding of forestry are welcome in this special collection.

Topics may include but are by no means limited to the following:

  • Forest mapping and change detection;
  • Forest disturbance and damage assessment;
  • Forest threat and health monitoring;
  • Ecosystem service assessment;
  • Forest carbon estimation;
  • Smart decision system of forest management;
  • Wildfire risk assessment and prediction;
  • Forest meteorology;
  • Forest–climate interactions.

Prof. Dr. Guojie Wang
Prof. Dr. Zengxin Zhang
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • forestry
  • artificial intelligence
  • remote sensing
  • meteorology
  • hydroecology
  • forest hydrology
  • climate change

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Related Special Issue

Published Papers (2 papers)

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Research

25 pages, 11027 KiB  
Article
A Novel Approach for the Counting of Wood Logs Using cGANs and Image Processing Techniques
by João V. C. Mazzochin, Giovani Bernardes Vitor, Gustavo Tiecker, Elioenai M. F. Diniz, Gilson A. Oliveira, Marcelo Trentin and Érick O. Rodrigues
Forests 2025, 16(2), 237; https://doi.org/10.3390/f16020237 - 26 Jan 2025
Viewed by 417
Abstract
This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional [...] Read more.
This study tackles the challenge of precise wood log counting, where applications of the proposed methodology can span from automated approaches for materials management, surveillance, and safety science to wood traffic monitoring, wood volume estimation, and others. We introduce an approach leveraging Conditional Generative Adversarial Networks (cGANs) for eucalyptus log segmentation in images, incorporating specialized image processing techniques to handle noise and intersections, coupled with the Connected Components Algorithm for efficient counting. To support this research, we created and made publicly available a comprehensive database of 466 images containing approximately 13,048 eucalyptus logs, which served for both training and validation purposes. Our method demonstrated robust performance, achieving an average Accuracypixel of 96.4% and Accuracylogs of 92.3%, with additional measures such as F1 scores ranging from 0.879 to 0.933 and IoU values between 0.784 and 0.875, further validating its effectiveness. The implementation proves to be efficient with an average processing time of 0.713 s per image on an NVIDIA T4 GPU, making it suitable for real-time applications. The practical implications of this method are significant for operational forestry, enabling more accurate inventory management, reducing human errors in manual counting, and optimizing resource allocation. Furthermore, the segmentation capabilities of the model provide a foundation for advanced applications such as eucalyptus stack volume estimation, contributing to a more comprehensive and refined analysis of forestry operations. The methodology’s success in handling complex scenarios, including intersecting logs and varying environmental conditions, positions it as a valuable tool for practical applications across related industrial sectors. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry: 2nd Edition)
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13 pages, 3746 KiB  
Article
NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest
by Adam Korycki, Cory Yeaton, Gregory S. Gilbert, Colleen Josephson and Steve McGuire
Forests 2025, 16(1), 173; https://doi.org/10.3390/f16010173 - 17 Jan 2025
Viewed by 510
Abstract
Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide (CO2) sequestration. Manual methods of forest mapping are [...] Read more.
Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide (CO2) sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE (2.81%), which consistently outperformed standard cylinder modeling approaches. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Forestry: 2nd Edition)
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