SAR and Deep Learning for Forest Monitoring
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 349
Special Issue Editors
Interests: synthetic aperture radar; SAR interferometry; digital elevation models; SAR system design; forest mapping and monitoring; image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: synthetic aperture radar; SAR interferometry; polarimetry; speckle time-series; forest mapping and monitoring; image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As a vital natural resource, forests are of extreme importance for all living beings on our planet. We would like to dedicate this Special Issue to the documentation of SAR-based methods in combination with artificial intelligence (AI), and in particular, deep learning (DL), for forest mapping, forest degradation monitoring, vegetation parameter retrieval and forest resource assessment. Well-prepared, unpublished submissions that address one or more of the following topics are solicited:
- New methods for the retrieval of forest structure parameters from SAR data using AI;
- DL-based methods and multi-sensor data fusion for forest information retrieval;
- New DL-based methods and concepts for the quantitative assessment of forest biomass;
- Feasibility studies with new sensors, ranging from drones to spaceborne SAR systems and their applications to forestry;
- Comparison and benchmarking studies using various sensors and/or DL-based methods for forest structure retrieval;
- New DL-based approaches for the detection of forest changes and degradation;
- AI methods for the detection of anomalies or areas at risk for fire outbreaks;
- AI methods for the detection of objects under the forest canopy;
- Scalability: the refinement of forest parameter estimates at the global scale.
Dr. Paola Rizzoli
Dr. Elise Colin-Koeniguer
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 100 words) can be sent to the Editorial Office for announcement on this website.
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
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synthetic aperture radar
- forest structure
- biomass
- artificial intelligence
- deep learning
- data fusion
