Hyperspectral Remote Sensing in Soil and Vegetation Degradation Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 104
Special Issue Editors
Interests: remote sensing; identification; imaging classification
Special Issue Information
Dear Colleagues,
Soil and vegetation degradations pose a threat to socioeconomic development and the ecosystem. Efficient monitoring is a method of remediation and protection. Reflectance spectroscopy has been recognized as an efficient alternative for the investigation of soil and vegetation degradations. Laboratory and field reflectance spectroscopy are used to characterize soil and vegetation variables. Hyperspectral remote sensing combines reflectance spectroscopy and remote sensing, further improving the abilities to characterize these variables and monitor degradation. With advances in hyperspectral imaging technology, airborne and satellite hyperspectral sensors are now in commission, increasing the availability of high-quality hyperspectral remote sensing imagery. Deep learning and artificial intelligence exhibit outstanding abilities in data mining and feature extraction.
This Special Issue, therefore, aims to publish original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of monitoring soil and vegetation degradations using hyperspectral remote sensing.
Topics of interest include, but are not limited to, the following:
- Soil contamination and degradation, grassland and forest degradations;
- Proximal, airborne, and satellite hyperspectral sensors’ design and data acquisition;
- Hyperspectral data denoising, variable selection, and model calibration algorithms;
- Land cover classification and change detection algorithms;
- Deep learning and artificial intelligence in hyperspectral data mining and feature extraction;
- Applications of hyperspectral data in characterizing soil and vegetation variables and monitoring soil and vegetation degradations.
Prof. Dr. Xia Zhang
Dr. Weichao Sun
Guest Editors
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Keywords
- soil and vegetation degradations
- remote sensing
- reflectance spectroscopy
- hyperspectral sensors
- land cover classification
- change detection
- deep learning
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