Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
2.4
Journals
Forests
Special Issues
Artificial Intelligence and Machine Learning Applications in Forestry—Second Edition
share announcement

Artificial Intelligence and Machine Learning Applications in Forestry—Second 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: 10 September 2025 | Viewed by 2602

Special Issue Editor

Prof. Dr. Yunfei Liu

E-Mail Website
Guest Editor
College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
Interests: forestry non-destructive detection; forestry Internet of things technology; microwave and optical technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With the improvements in computer hardware performance and deep learning, a new generation of information technology has been continuously integrated into the core business of forestry; the application of artificial intelligence and machine learning technology in the field of forestry has become increasingly widespread. Countries around the world have paid much attention to the development of intelligent forestry. Smart forestry integrates the key technologies in digital forestry with the new generation of information technology, such as artificial intelligence, the Internet of Things, big data, cloud computing and mobile Internet, and forestry intelligent equipment, forming a multidisciplinary deep integration of forestry production and management, including intelligent breeding, cultivation, monitoring, operation management, protection, etc. This helps to realize three-dimensional perception, precise cultivation, real-time monitoring, intelligent management, intelligent decision making, and other new models of forestry information development. The development of intelligent forestry needs to further promote the research, development, and application of intelligent algorithms, such as multisource heterogeneous data fusion and machine vision, integrated data mining, model simulation, and intelligent analysis technology into forestry, promote forestry scientific and technological innovation, and achieve the high-quality development of forestry on the basis of the continuous improvement of forestry theoretical research.

This Special Issue aims to cover a whole range of applications, including typical applications of forestry and forestry engineering. One or more of these problems can be solved through high-quality research or review papers on the following topics:

  • Applications of AI tools and applications in forestry;
  • Forestry deep learning models;
  • Recent forestry development trends and application status of information technology in forestry;
  • Intelligent sensing technology for forestry;
  • Forestry pattern recognition;
  • Multimedia and cognitive informatics in forestry;
  • Application of remote sensing big data and cloud computing in forestry;
  • Forestry virtual reality;
  • Intelligent forestry equipment.

Please check the first edition of the Special Issue “Artificial Intelligence and Machine Learning Applications in Forestry” at  https://www.mdpi.com/journal/forests/special_issues/UJ60769Q36 

Prof. Dr. Yunfei Liu
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 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

artificial intelligence
machine learning
deep learning
forest management
forest protection

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 3507 KiB  
Article
WSSGCN: Hyperspectral Forest Image Classification via Watershed Superpixel Segmentation and Sparse Graph Convolutional Networks
by Pingfei Chen, Xuyang Li, Yong Peng, Xiangsuo Fan and Qi Li
Forests 2025, 16(5), 827; https://doi.org/10.3390/f16050827 - 15 May 2025
Viewed by 145
Abstract
Hyperspectral image classification is crucial in remote sensing but faces challenges in forest ecosystem studies due to high-dimensional data, spectral variability, and spatial heterogeneity. Watershed Superpixel Segmentation and Sparse Graph Convolutional Networks (WSSGCN), a novel framework designed for efficient forest image classification, is [...] Read more.
Hyperspectral image classification is crucial in remote sensing but faces challenges in forest ecosystem studies due to high-dimensional data, spectral variability, and spatial heterogeneity. Watershed Superpixel Segmentation and Sparse Graph Convolutional Networks (WSSGCN), a novel framework designed for efficient forest image classification, is introduced in this paper. Watershed superpixel segmentation is first used by the method to divide hyperspectral images into semantically consistent regions, reducing computational complexity while preserving terrain boundary information. On this basis, a dual-branch model is designed: a local branch with multi-scale convolutional neural networks (CNN) extracts spatial–spectral features, while a global branch constructs superpixel graphs and uses GCNs to model the global context. To enhance efficiency, a sparse tensor-based storage method is proposed for the adjacency matrix, reducing complexity from quadratic to linear. Additionally, an attention-based adaptive fusion strategy dynamically balances local and global features. Experiments on multiple datasets show that WSSGCN outperforms mainstream methods in overall accuracy (OA), average accuracy (AA), and Kappa coefficient. Notably, it achieves a 3.5% OA improvement and a 0.04 Kappa coefficient increase compared to SPEFORMER on the WHU-Hi-HongHu dataset. Practicality in resource-limited scenarios is ensured by sparse graph modeling. This work offers an efficient solution for forest monitoring, supporting applications like biodiversity assessment and deforestation tracking, and advances remote sensing-based forest ecosystem analysis. The proposed approach shows strong potential for real-world ecological conservation and forest management. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry—Second Edition)
►▼ Show Figures

Figure 1

13 pages, 7018 KiB  
Article
Image Classification of Tree Species in Relatives Based on Dual-Branch Vision Transformer
by Qi Wang, Yanqi Dong, Nuo Xu, Fu Xu, Chao Mou and Feixiang Chen
Forests 2024, 15(12), 2243; https://doi.org/10.3390/f15122243 - 20 Dec 2024
Cited by 1 | Viewed by 927
Abstract
Tree species in relatives refer to species belonging to the same genus with high morphological similarity and small botanical differences, making it difficult to perform classification and usually requiring manual identification by experts. To reduce labor costs and achieve accurate species identification, we [...] Read more.
Tree species in relatives refer to species belonging to the same genus with high morphological similarity and small botanical differences, making it difficult to perform classification and usually requiring manual identification by experts. To reduce labor costs and achieve accurate species identification, we conducted research on the image classification of tree species in relatives based on deep learning and proposed a dual-branch feature fusion Vision Transformer model. This model is designed with a dual-branch architecture and two effective blocks, a Residual Cross-Attention Transformer Block and a Multi-level Feature Fusion method, to enhance the influence of shallow network features on the final classification and enable the model to capture both overall image information and detailed features. Finally, we conducted ablation studies and comparative experiments to validate the effectiveness of the model, achieving an accuracy of 90% on the tree relatives dataset. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry—Second Edition)
►▼ Show Figures

Figure 1

21 pages, 8184 KiB  
Article
Estimation of Vegetation Carbon Sinks and Their Response to Land Use Intensity in the Example of the Beijing–Tianjin–Hebei Region
by Qing Yao, Junping Zhang, Huayang Song, Rongxia Yu, Nina Xiong, Jia Wang and Liu Cui
Forests 2024, 15(12), 2158; https://doi.org/10.3390/f15122158 - 6 Dec 2024
Cited by 1 | Viewed by 835
Abstract
Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains [...] Read more.
Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran’s spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry—Second Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop