Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 30 August 2026 | Viewed by 15
Special Issue Editors
Interests: smart forestry; remote sensing; geographic information system; 3D point cloud; model; climate change and carbon sequestration assessment
Special Issues, Collections and Topics in MDPI journals
Interests: forest inventory; remote sensing; 3S; LiDAR; smart forestry; forestry carbon sink
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; UAV; multiview imagery; LiDAR analytics; forest health and carbon modeling
Interests: forestry equipment and informatization; intelligent processing and application of remote sensing big data; regional ecological remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: forest ecology and ecological monitoring; environmental sensors
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The urgent need for sustainable forest management and effective climate change mitigation has propelled precision forestry and accurate carbon sink assessment to the forefront of research. Rapid advancements in remote sensing technologies—spanning high-resolution satellite imagery, unmanned aerial vehicles (UAVs), LiDAR, hyperspectral sensing, and synthetic aperture radar (SAR)—combined with artificial intelligence (AI) and big data analytics, are revolutionizing forest monitoring. These technologies enable a paradigm shift from traditional, labor-intensive field surveys to real-time, high-precision, and large-scale observations. Accurate quantification of forest structure, biomass, and carbon stocks is now feasible, supporting global efforts toward carbon neutrality and ecosystem conservation.
This Special Issue focuses on cutting-edge remote sensing technologies for precision forestry and carbon sink assessment. It highlights innovations in 3D structure modeling (LiDAR, SfM, and 3D point clouds), multi-source data fusion (optical/SAR/UAV), and dynamic monitoring (forest structure/function changes). Aligned with Remote Sensing’s mission to advance technological innovation and interdisciplinary applications, it provides a platform to share methods and case studies that bridge remote sensing with sustainable forest management and carbon neutrality.
We welcome original research, reviews, and technical communications on the following topics:
- Multi-source remote sensing data fusion (e.g., optical, SAR, LiDAR, and hyperspectral) for forest structure and biomass estimation;
- UAV and satellite-based high-spatiotemporal-resolution monitoring of forest dynamics;
- AI and deep learning for forest classification, change detection, and carbon stock mapping;
- 3D forest structure modeling using LiDAR and photogrammetric point clouds;
- Remote sensing of forest carbon cycles and carbon sink assessment;
- Hyperspectral and thermal remote sensing for forest health and stress monitoring;
- Integration of remote sensing with IoT and ground-based observations;
- Cloud computing and open-source platforms for large-scale forest monitoring;
- Applications in forest fire, pest, and disease detection;
- Digital twin development and intelligent decision support systems in forestry.
Prof. Dr. Jincheng Liu
Prof. Dr. Jia Wang
Prof. Dr. Tao Liu
Prof. Dr. Weiheng Xu
Dr. Zhichao Wang
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-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
- forest structure and function
- forest dynamics 3D point cloud
- advanced algorithms
- multi-source data fusion
- UAV remote sensing
- carbon sink assessment
- precision forestry management
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