Application of Machine-Learning Techniques 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: 31 December 2025 | Viewed by 69
Special Issue Editors
Interests: remote sensing; computer vision; machine learning; deep learning; change detection; deforestation detection
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition for remote sensing; image analysis; remote sensing applications; change detection
Special Issues, Collections and Topics in MDPI journals
Interests: biophysical properties; data integration and data fusion; change detection methods
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; image processing and interpretation; artificial intelligence; deep learning; remote sensing; digital photogrammetry
Special Issue Information
Dear Colleagues,
Forestry can be broadly defined as the science and craft of cultivating, managing, using, conserving and repairing forests and woodlands. Machine learning (ML) denotes a class of algorithms that learn to solve tasks based on the data they consume. ML-based methods are currently present in day-to-day life, having evolved in such a way that they now represent the state of the art in various application fields, including forestry. Indeed, ML is revolutionizing the field of forestry by enabling smarter, data-driven decision making based on the analysis of complex datasets gathered from forests around the Earth, such as satellite images and sensor data. Among multiple applications, ML technology helps to monitor forest health and detect diseases and illegal activities. For instance, automated detection of deforestation, illegal logging as well as the recognition of tree species, is made more effective through ML-based image analysis. Additionally, ML supports the modeling of forest growth and its sustainable management while wildfire predictions remarkably improved by processing environmental variables and historical fire data. As forestry embraces digital transformation, ML stands as a key tool for enhancing environmental stewardship and operational efficiency.
This Special Issue aims to cover the whole range of machine-learning based methods applied to forestry and forestry engineering. Potential topics include, but are not limited to, the following:
- Forestry machine learning models;
- Intelligent sensing for forestry;
- Cognitive informatics in forestry;
- Forestry virtual reality;
- Intelligent forestry equipment;
- Deforestation detection and prediction;
- Forest degradation and regeneration;
- Forest fire detection and prediction;
- Forest health monitoring;
- Forest cover mapping;
- Species identification;
- Tree phenotype prediction;
- Growth and production prediction;
- Phenology;
- Yield forecasting;
- Sustainable forest management;
- Optimized harvesting;
- Biomass estimation;
- Risk assessment;
- Canopy projection and height estimation;
- Evapotranspiration assessment;
- Forest water resources conservation;
- Forest precipitation analysis.
Prof. Dr. Gilson Alexandre Ostwald Pedro da Costa
Prof. Dr. Raul Queiroz Feitosa
Prof. Dr. Veraldo Liesenberg
Guest Editors
Prof. Dr. Guilherme Lucio Abelha Mota
Dr. Pedro Juan Soto Vega
Guest Editor Assistants
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
- biogeophysical cycles
- carbon stock
- climate change
- deep learning
- deforestation
- ecological services
- forest conservation
- forest degradation
- forest dynamics
- forest fire
- forest landscapes
- forest management
- forest productivity
- forest protection
- forest regeneration
- land use dynamics
- machine learning
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