Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".
Deadline for manuscript submissions: 31 January 2025 | Viewed by 1421
Special Issue Editor
Interests: remote sensing; machine learning; ecology; plant communities
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Land monitoring, the systematic observation, measurement, and analysis of the Earth's terrestrial surface's biophysical characteristics, is gaining increased interest, driven by the growing need for sustainable management of Earth's resources, advancements in satellite and sensor technologies, and an increased focus on addressing climate change and environmental degradation. The process involves monitoring various environmental factors such as land cover, urban expansion, vegetation, and agricultural practices, to improve climate and disaster response, as well as sustainable environmental management.
The integration of optical, radar, and lidar satellite observations is expected to produce more accurate and consistent land monitoring solutions. However, identifying current research gaps in combining optical, radar, and lidar satellite observations for land monitoring requires examining the limitations and challenges associated with these technologies. One of the primary challenges is developing effective algorithms for integrating the structural information provided by radar and lidar sensors with the spectral information of optical sensors. These datasets are fundamentally different in nature, leading to complexities in data fusion, and research is needed to create advanced algorithms and data fusion techniques that can provide a more comprehensive view of the Earth’s surface. Another challenge is ensuring continuity and consistency in data collection over time. Research is required to develop systems that can integrate data from multiple sources taken at different times while maintaining harmonization temporally. Despite deep learning's role in fusing data from various sensors, creating models that accurately interpret complex data is challenging, and improving radar and lidar resolution without losing quality is necessary.
Contributions in the form of original articles, letters, reviews, and perspectives are invited from researchers and practitioners working on developing algorithms, improving existing techniques, and applying these methods to diverse geographical regions and ecological settings, offering unprecedented insights into our changing world. We thank you in advance for your contributions to this Special Issue.
Dr. Ram C. Sharma
Guest Editor
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Keywords
- land monitoring
- vegetation
- disaster response
- urban
- agriculture
- biomass and carbon stocks
- data fusion
- optical, radar, lidar observations
- algorithm development
- deep learning
- ecological applications
- multi-spectral
- hyper-spectral
- SAR
- landsat
- sentinel
- worldview
- GEDI
