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Application of Machine-Learning Methods in Forestry

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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: 30 December 2025 | Viewed by 1121

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Special Issue Editors

Prof. Dr. Leiguang Wang

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Guest Editor

School of Horticulture and Landscape Architecture, Southwest Forestry University, No. 300 Bailongsi, Panlong District, Kunming, China
Interests: remote sensing information processing; forestry applications of machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chinsu Lin

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Guest Editor

Department of Forestry and Natural Resources, National Chiayi University, Chiayi 600355, Taiwan
Interests: lidar remote sensing; spectral remote sensing; growth modeling; carbon budgeting; forest management; sensing of climate change signals
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forests, as crucial components of global ecosystems, are experiencing unprecedented transformations driven by various factors, including climate change. In recent years, machine-learning methods have emerged as powerful tools to address complex forestry challenges, offering new perspectives for understanding forest dynamics, enhancing management practices, and mitigating the impacts of environmental stressors; for example, the use of remote sensing and automated tree detection for forest monitoring and inventory. Data-driven models predict forest growth and yield, supporting adaptive management. The early detection of stressors aids in forest health and disease management. Fire risk assessment and post-fire recovery planning are improved with machine learning. For instance, carbon sequestration estimates help track climate mitigation progress and ecosystem services valuation informs policy and decision-making. Therefore, we invite contributions from researchers across various disciplines to share their insights and innovations on the application of machine-learning methods in forestry. Together, we can advance our understanding of forest ecosystems, promote sustainable management practices, and mitigate the impacts of climate change.

Prof. Dr. Leiguang Wang
Prof. Dr. Chinsu Lin
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

machine learning
forestry
remote sensing
forest management
forest inventory
growth prediction
health management
forest structure parameter inversion
fire prediction
tree species identification
pest and disease monitoring
biomass estimation
vegetation mapping
spatial distribution

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Published Papers (2 papers)

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Research

14 pages, 2298 KiB

Open AccessArticle

L²: Accurate Forestry Time-Series Completion and Growth Factor Inference

by Linlu Jiang, Meng Yang, Benye Xi, Weiliang Meng and Jie Duan

Forests 2025, 16(6), 895; https://doi.org/10.3390/f16060895 - 26 May 2025

Viewed by 204

Abstract

In forestry data management and analysis, data integrity and analytical accuracy are of critical importance. However, existing techniques face a dual challenge: first, sensor failures, data transmission interruptions, and human errors lead to the prevalence of missing data in forestry datasets; second, the multidimensional heterogeneity and environmental complexity of forestry systems not only increase the difficulty of missing value estimation, but also significantly affect the accuracy of resolving the potential correlations among data. In order to solve the above problems, we proposed the

L^{2}

model using the aspen woodland as the experimental object. The

L^{2}

model consists of a complementary model and a predictive model. The

L^{2}

complementary model integrates low tensor tensor kernel norm minimisation (LRTC-TNN) to capture global consistency and local trends, and combines long and short-term memory and convolutional neural network (LSTM-CNN) to extract temporal and spatial features, which is effective in accurately reconstructing the missing values in forestry time-series data. We also optimised the LRTC-TNN model to handle multi-class data and incorporated a self-attention mechanism into the LSTM-CNN framework to improve performance in the case of complex missing data. The

L^{2}

prediction model adopts a dual attention mechanism (temporal attention mechanism and feature attention mechanism) based on LSTM to construct a stem diameter prediction model, which achieves high-precision prediction of stem diameter variation. Then we further analyzed the effects of various factors on stem diameter using SHAP (Shapley Additive Explanations). Experimental results demonstrate that our

L^{2}

significantly improves data completion accuracy while preserving the original structure and key characteristics of the data. Moreover, it enables a more precise analysis of the factors affecting stem diameter, providing a robust foundation for advanced forestry data analysis and informed decision making. Full article

(This article belongs to the Special Issue Application of Machine-Learning Methods in Forestry)

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21 pages, 11164 KiB

Open AccessArticle

Estimation of Gross Primary Productivity Using Performance-Optimized Machine Learning Methods for the Forest Ecosystems in China

by Qin Na, Quan Lai, Gang Bao, Jingyuan Xue, Xinyi Liu and Rihe Gao

Forests 2025, 16(3), 518; https://doi.org/10.3390/f16030518 - 15 Mar 2025

Cited by 1 | Viewed by 731

Abstract

Gross primary productivity (GPP) quantifies the rate at which plants convert atmospheric carbon dioxide into organic matter through photosynthesis, playing a vital role in the terrestrial carbon cycle. Machine learning (ML) techniques excel in handling spatiotemporally complex data, facilitating accurate spatial-scale inversion of forest GPP by integrating limited ground flux measurements with Remote Sensing (RS) observations. Enhancing ML algorithm performance for precise GPP estimation is a key research focus. This study introduces the Random Grid Search Algorithm (RGSA) for hyperparameters tuning to improve Random Forest (RF) and eXtreme Gradient Boosting (XGB) models across four major forest regions in China. Model optimization progressed through three stages: the Unoptimized (UO) XGB model achieved

R^{2}

= 0.77 and

R M S E

= 1.42 g Cm⁻² d⁻¹; the Hyperparameter Optimized (HO) XGB model using RGSA improved performance by 5.19% in

R^{2}

(0.81) and reduced

R M S E

by 9.15% (1.29 g Cm⁻² d⁻¹); the Hyperparameter and Variable Combination Optimized (HVCO) XGB model with selected variables (LAI, Temp, NR, VPD, and NDVI) further enhanced

R^{2}

to 0.83 and decreased

R M S E

to 1.23 g Cm⁻² d⁻¹. The optimized GPP estimates exhibited high spatial consistency with existing high-quality products like GOSIF GPP, GLASS GPP, and FLUXCOM GPP, validating the model’s reliability and effectiveness. This research provides crucial insights for improving GPP estimation accuracy and optimizing ML methodologies for forest ecosystems in China. Full article