Advances in Forest Aboveground Biomass Mapping Using Multi-Source Remote Sensing and Machine Learning
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 150
Special Issue Editors
Interests: remote sensing; carbon cycle; machine learning; deep learning; land cover and land change; forest change; canopy cover; carbon stocks
Interests: remote sensing; mountainous region; UAV; SIF; vegetation; agricultural
Interests: climate change; gross primary productivity; photosynthesis; earth observations; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: forests remote sensing; forest aboveground biomass (AGB); synthetic aperture radar (SAR); light detection and ranging (LiDAR); wall-to-wall forest AGB mapping
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Accurate mapping of forest aboveground biomass (AGB) is essential for understanding the global carbon cycle, monitoring ecosystem dynamics, and supporting climate change mitigation. Remote sensing has become a key tool for estimating and monitoring forest biomass at regional to global scales.
Recent advances in Earth observation provide new opportunities for improving biomass estimation. Optical imagery captures canopy spectral properties, synthetic aperture radar (SAR) is sensitive to canopy structure and woody components, and LiDAR enables direct characterization of vertical forest structure. Airborne and unmanned aerial vehicle (UAV) platforms further offer high-resolution observations that help bridge the scale gap between field measurements and satellite data. The integration of these multi-source datasets allows for more accurate and spatially consistent biomass mapping.
At the same time, machine learning and deep learning approaches are increasingly being applied to extract complex relationships from multi-sensor data, improving model performance and scalability.
This Special Issue aims to present recent advances in forest aboveground biomass mapping using multi-source remote sensing data. We welcome contributions that develop new methods, integrate multiple sensors, or explore innovative approaches for monitoring forest biomass and its dynamics across different spatial and temporal scales.
Original research articles and review papers are welcome. Topics include, but are not limited to:
- Forest biomass estimation using optical, SAR, and LiDAR data
- Machine learning and deep learning methods for biomass estimation
- Multi-source remote sensing data fusion for biomass mapping
- UAV and airborne observations for forest structure and biomass
- Monitoring biomass change and forest carbon dynamics
- Validation and uncertainty analysis of biomass estimates
We look forward to receiving your contributions.
Dr. Wenquan Dong
Dr. Zhiqiang Cheng
Dr. Songyan Zhu
Dr. Yongjie Ji
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 aboveground biomass
- optical
- SAR
- LiDAR
- biomass mapping
- machine learning
- deep learning
- forest carbon
- data fusion
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