Remote Sensing of Individual Trees: Delineation, Characterization, and Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 15 November 2026 | Viewed by 307
Editors
Interests: AI for forestry; precision forestry harvesting; remote sensing; photogrammetric 3D modelling
Interests: urban remote sensing; land cover/land-use change; forestry
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral image interpretation; deep learning; environmental sustainability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in remote sensing have greatly improved our ability to observe forests at the scale of individual trees. High-resolution UAV, airborne, and satellite imagery, together with LiDAR, hyperspectral, and multi-sensor data, now make it possible to delineate tree crowns, retrieve structural and functional attributes, and assess tree condition across diverse ecosystems. Individual-tree remote sensing is increasingly important for forestry inventory, biodiversity conservation, urban tree management, biomass estimation, and carbon assessment. As the field moves beyond stand-level mapping toward finer ecological and management units, robust methods for individual-tree delineation and characterization have become a key research frontier.
This Special Issue aims to bring together recent advances in the remote sensing of individual trees. It seeks to provide a forum for studies that develop or apply innovative methods based on optical, LiDAR, hyperspectral, SAR, or fused datasets across spatial scales and ecosystem types. Contributions specifically focused on individual trees are particularly encouraged, including research on tree crown delineation, individual tree detection and extraction, structural and functional trait characterization, multisource data fusion, multiscale/multitemporal analysis, and related applications.
Articles may address, but are not limited to, the following topics:
- Individual tree detection or crown delineation;
- Tree-level structural analysis and clustering;
- Biomass, carbon stock, and productivity estimation;
- Tree health and vitality assessment;
- Time-series monitoring and change detection;
- Multi-source and multi-scale data fusion;
- Benchmark datasets;
- Evaluation frameworks.
Dr. Zhu Mao
Dr. Qingwei Zhuang
Dr. Jiaqi Yang
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 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-anonymized 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
- individual trees
- crown delineation
- tree monitoring and assessment
- LiDAR
- UAV remote sensing
- multi-source data fusion
- remote sensing applications
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