Artificial Intelligence and Remote Sensing Applied to Forest Management: Advances in Machine Learning and Deep Learning Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".
Deadline for manuscript submissions: 15 December 2025 | Viewed by 2
Special Issue Editors
Interests: remote sensing; discrete and full-waveform lidar; forest monitoring; machine and deep learning; time series; geoinformatics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The parallel evolution of artificial intelligence, particularly machine learning and deep learning, and remote sensing technologies has transformed forest monitoring, enabling the extraction of detailed information from both passive and active sensors. Machine learning models, ranging from classical algorithms (i.e., Random Forest) to deep architectures (i.e., convolutional neural networks), have been successfully applied in a variety of tasks, including species classification, biomass estimation, and forest cover change detection. Multi-scale remote sensing, ranging from terrestrial surveys, UAV-based campaigns, and airborne flights to satellite missions, has integrated a wide range of passive and active sensors, significantly improving spatial, temporal, radiometric, and spectral resolutions in recent years. The availability of these multi-resolution and multi-temporal datasets is enhancing forest structural characterization and monitoring, supporting analyses across local, regional, and global spatial domains. In parallel, the proliferation of cloud-based platforms, big data analytics, and open data repositories is increasing the scalability of forest monitoring, allowing for analyses over vast spatial extents and extended timeframes. These technologies are accelerating the transition toward smart forestry practices, characterized by real-time monitoring, intelligent decision-making, and predictive modeling for sustainable forest management.
This Special Issue aims to synthesize studies that develop and apply artificial intelligence techniques in forest remote sensing to address challenges such as species classification, forest segmentation, biomass and live fuel moisture content estimation, early plague detection, or the assessment of forest degradation. Articles may address topics including, but not limited to, the following:
- Forest structure;
- Biomass;
- Carbon stock;
- Live fuel moisture content;
- Forest segmentation;
- Species classification;
- Forest changes;
- Forest inventory;
- Forest fuel;
- Forest degradation;
- Forest diseases.
Dr. Pablo Crespo-Peremarch
Dr. Juan Pedro Carbonell-Rivera
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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
- multispectral
- hyperspectral
- lidar
- radar
- photogrammetry
- UAV
- deep learning
- machine learning
- neural networks
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