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Forests
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Application of Machine-Learning Methods in Forestry
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Application of Machine-Learning Methods in Forestry

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 805

Special Issue Editors

Prof. Dr. Leiguang Wang

E-Mail Website
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

E-Mail Website
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 (1 paper)

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Research

21 pages, 11164 KiB  
Article
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
Viewed by 640
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 [...] Read more.
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 R2 = 0.77 and RMSE = 1.42 g Cm−2 d−1; the Hyperparameter Optimized (HO) XGB model using RGSA improved performance by 5.19% in R2 (0.81) and reduced RMSE 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 R2 to 0.83 and decreased RMSE 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
(This article belongs to the Special Issue Application of Machine-Learning Methods in Forestry)
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