Multi-Source Remote Sensing for Forest Canopy Structure and Biomass Estimation
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 1000
Editors
Interests: forest monitoring; forest scattering mechanisms at mircrowave bands; forest height/forest AGB inversion using multi-remote sensing technology
Special Issues, Collections and Topics in MDPI journals
Interests: processing of stacks of polarimetric synthetic aperture radar (PolSAR) images for environmental applications; change detection; classification
Special Issues, Collections and Topics in MDPI journals
Interests: forest biomass inversion; SAR and optical remote sensing; forest disturbance detection; land carbon cycle
Interests: processing of the TomoSAR data and InSAR data for the forest structure estimation and the vegetation height retrieval
Special Issues, Collections and Topics in MDPI journals
Interests: forest parameter inversion method of polarimetric SAR, interferometric SAR, and polarimetric interferometric SAR
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forests act as the largest carbon sink in the terrestrial ecosystem, playing a critical role in mitigating global climate change and maintaining ecological balance. Accurate estimation of forest aboveground biomass (AGB) and canopy structure is essential for modeling the global carbon cycle, quantifying carbon fluxes, and supporting international REDD+ initiatives. However, traditional single-sensor remote sensing approaches face severe physical limitations, particularly the signal saturation problem in high-biomass regions and topographic distortions in complex terrains. Recently, the field has experienced a profound paradigm shift towards multi-source, multi-dimensional, and multi-modal remote sensing. By integrating active and passive sensors—such as spaceborne LiDAR (e.g., GEDI, ICESat-2), multi-frequency SAR (e.g., NISAR), and high-resolution stereoscopic optical imagery—researchers can capture the complex 3D structure of forests with unprecedented detail. Furthermore, the emergence of advanced artificial intelligence, including self-supervised learning and large vision foundation models, provides revolutionary opportunities to extract precise structural features without the need for massive labeled datasets.
The primary aim of this Special Issue is to gather cutting-edge research that addresses the technical bottlenecks in forest biomass and canopy structure estimation through multi-modal remote sensing. We seek to highlight innovative methodologies that improve estimation accuracy across scales, such as novel data assimilation frameworks, topographic compensation strategies for solving the spectral saturation problem, and next-generation AI architectures. This subject closely aligns with the journal's scope of advancing Earth observation technologies and environmental remote sensing, providing a cross-disciplinary platform for ecologists, data scientists, and remote sensing experts to share robust solutions for global carbon monitoring.
We welcome well-prepared, unpublished submissions that address one or more of the following themes: multi-modal fusion mechanisms of multi-scale remote sensing data (LiDAR, SAR, optical) for 3D forest structure modeling; innovative feature construction and compensation strategies to overcome the signal saturation problem in high-biomass forests; and applications of advanced artificial intelligence, including deep learning, multi-modal reasoning, and self-supervised foundation models in forest parameter extraction, forest AGB estimation, uncertainty quantification, spatial divergence diagnostics, and model generalization in large-scale biomass mapping.
Prof. Dr. Wangfei Zhang
Dr. Armando Marino
Dr. Zhanmang Liao
Dr. Lei Shi
Dr. Lei Zhao
Dr. Sornkitja Boonprong
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
- forest AGB estimation
- forest monitoring
- LiDAR technology
- SAR technology
- optical images
- artificial intelligence (AI)
- multi-modal
- deep learning
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