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 402
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 Well-prepared, unpublished submissions that address one or more of the following topics are solicited: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
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Keywords
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synthetic aperture radar
- forest structure
- biomass
- artificial intelligence
- deep learning
- data fusion
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