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Artificial Intelligence and Machine Learning Applications in Forestry—Second Edition
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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 March 2026 | Viewed by 6057

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 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

artificial intelligence
machine learning
deep learning
forest management
forest protection

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Published Papers (5 papers)

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20 pages, 2921 KB  
Article
Optimal Training Sample Sizes for U-Net-Based Tree Species Classification with Sentinel-2 Imagery
by Heejae Lee, Cheolho Lee, Hanbyol Woo and Sol-E Choi
Forests 2025, 16(11), 1718; https://doi.org/10.3390/f16111718 - 12 Nov 2025
Viewed by 397
Abstract
Detecting forest tree species distribution using satellite imagery with deep-learning models is essential for effective forest management. While sufficient training samples are crucial for developing deep-learning-based tree species classification models, creating these training samples requires significant resources. Therefore, understanding the optimal balance between [...] Read more.
Detecting forest tree species distribution using satellite imagery with deep-learning models is essential for effective forest management. While sufficient training samples are crucial for developing deep-learning-based tree species classification models, creating these training samples requires significant resources. Therefore, understanding the optimal balance between model accuracy and training sample size is essential for efficient resource allocation. Here, we determined the optimal training sample size for forest tree species classification using Sentinel-2 imagery and the U-Net model. The study area comprised the Seoul–Gyeonggi region of South Korea, where the nine dominant tree species were selected for classification. We utilized multi-temporal Sentinel-2 imagery, incorporating spectral, vegetation, and textural features. Optimal points were identified using Locally Estimated Scatterplot Smoothing (LOESS) regression. The maximum overall accuracy reached 61%, with 90% and 95% of the maximum accuracy with training sample sizes of 2.37%–2.67% and 4.42%–5.89%, respectively. The congeneric Pinus and Quercus groups had major confusion, with species-specific F1-scores ranging from 0.40 (Robinia pseudoacacia) to 0.75 (Pinus koraiensis). These results provide practical guidelines for efficient resource allocation in tree species classification. Rather than pursuing excessive data collection beyond the optimal point, integrating multiple sensor types can overcome existing limitations and enhance classification accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry—Second Edition)
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24 pages, 1470 KB  
Article
Integrating Ecological Semantic Encoding and Distribution-Aligned Loss for Multimodal Forest Ecosystem
by Jing Peng, Zhengjie Fu, Huachen Zhou, Yibin Liu, Yang Zhang, Rui Shi, Jiangfeng Li and Min Dong
Forests 2025, 16(11), 1697; https://doi.org/10.3390/f16111697 - 7 Nov 2025
Viewed by 530
Abstract
In this study, a cross-hierarchical intelligent modeling framework integrating an ecological semantic encoder, a distribution-aligned contrastive loss, and a disturbance-aware attention mechanism was developed to address the semantic alignment challenge between aboveground vegetation and belowground seed banks within forest ecosystems. The proposed framework [...] Read more.
In this study, a cross-hierarchical intelligent modeling framework integrating an ecological semantic encoder, a distribution-aligned contrastive loss, and a disturbance-aware attention mechanism was developed to address the semantic alignment challenge between aboveground vegetation and belowground seed banks within forest ecosystems. The proposed framework leverages artificial intelligence and deep learning to characterize the structural and functional coupling between vegetation and soil communities, thereby elucidating the ecological mechanisms that underlie forest regeneration and stability. Experiments using representative forest ecological plot datasets demonstrated that the model achieved a top-1 accuracy of 78.6%, a top-5 accuracy of 89.3%, a mean cosine similarity of 0.784, and a reduced Kullback–Leibler divergence of 0.128, while the Jaccard index increased to 0.512—surpassing traditional statistical and machine-learning baselines such as RDA, CCA, Procrustes, Siamese, and SimCLR. The model also reduced NMDS stress to 0.094 and improved the Sørensen coefficient to 0.713, reflecting high robustness and precision in reconstructing community structure and ecological distributions. Additionally, the integration of distribution alignment and disturbance-aware mechanisms allows the model to capture dynamic vegetation–soil feedbacks across environmental gradients and disturbance regimes. This enables more accurate identification of regeneration potential, resilience thresholds, and restoration trajectories in degraded forests. Overall, the framework provides a novel theoretical foundation and a data-driven pathway for applying artificial intelligence to forest ecosystem monitoring, degradation diagnosis, and adaptive management for sustainable recovery. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Forestry—Second Edition)
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21 pages, 3507 KB  
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
Cited by 1 | Viewed by 778
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)
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13 pages, 7018 KB  
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 4 | Viewed by 1700
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)
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21 pages, 8184 KB  
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 3 | Viewed by 1370
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)
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