Advances in Digital Forestry: Remote Sensing, Biometrics, Measurement Technologies, and AI
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 January 2027 | Viewed by 10
Special Issue Editor
Special Issue Information
Dear Colleagues,
Advances in digital forestry are transforming how forests are observed, measured, and managed by integrating remote sensing, field data, biometrics, and AI into high-resolution digital forest twins. This Special Issue invites innovative research that advances the development, validation, and application of these tools across scales, from individual trees to entire landscapes.
Digital forest twins are rapidly moving from concept to practice. Emerging remote sensing technologies, multitemporal workflows, and AI-driven 3D reconstruction now enable efficient estimation of tree height, crown structure, biomass, canopy dynamics, and disturbance impacts. At the same time, methods such as irregular-grid point-cloud partitioning, Gaussian splatting, and Geiger-mode scanning are improving segmentation accuracy, visualization, and performance across diverse forest conditions.
The need for these tools is urgent. High-resolution digital twins can strengthen forest health monitoring, improve carbon accounting, support precision silviculture, and enhance resilience to wildfire, pests, drought, and other disturbances. We particularly encourage submissions that integrate novel sensor uses, scalable workflows, machine learning, and rigorous validation to improve the reliability, transparency, and operational value of digital forest twins for forest management.
Dr. Mark J. Kimsey
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
- digital twins
- forest mensuration
- biometrics
- gaussian splatting
- LiDAR
- machine learning
- AI
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
