Satellite Remote Sensing for Land Use/Land Cover(LULC) and Vegetation Monitoring
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".
Deadline for manuscript submissions: 15 December 2026 | Viewed by 191
Special Issue Editors
Interests: geoinformation; geographical analysis; spatial analysis; mapping digital mapping; satellite image analysis; geospatial science; spatial statistics satellite image processing; advanced machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; photogrammetry; lidar; unmanned aerial vehicles; geodesy; geographic information system; geoinformation; satellite image analysis; mapping; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Land use and land cover (LULC) mapping and vegetation monitoring play a vital role in understanding Earth’s dynamic processes and supporting sustainable environmental management. With the continuous advancement of satellite remote sensing technologies, a wide range of multispectral, hyperspectral, radar, and LiDAR sensors now provide vast amounts of spatial, spectral, and temporal data at unprecedented resolution and frequency. These datasets enable the detailed observation and quantification of vegetation conditions, land cover dynamics, and anthropogenic impacts across diverse ecosystems.
This Special Issue aims to bring together innovative research focused on the use of satellite-based remote sensing for mapping, monitoring, and modeling LULC and vegetation changes at local, regional, and global scales. We invite contributions that explore emerging methodologies, data fusion strategies, time-series analyses, and artificial intelligence approaches to advance LULC and vegetation monitoring using multi-source remotely sensed data. Review papers are also welcome.
Authors are encouraged to submit articles on topics including, but not limited to, the following:
- Machine learning and deep learning techniques for LULC and vegetation mapping;
- Multitemporal and multi-sensor data fusion for improved classification accuracy;
- Time-series analysis of vegetation dynamics and land cover change;
- Integration of optical, SAR, and LiDAR data for vegetation structure and biomass estimation;
- Use of analysis-ready datasets and cloud-based platforms for large-scale monitoring;
- Detection and assessment of land degradation, deforestation, and urban expansion;
- Development of new algorithms and indicators for vegetation health assessment.
Dr. Dino Dobrinić
Dr. Mateo Gašparović
Guest Editors
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Keywords
- land use and land cover (LULC) classification
- vegetation indices and dynamics
- multi-sensor data fusion (optical, SAR, LiDAR)
- deep learning for remote sensing
- machine learning for vegetation monitoring
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