Agricultural and Life Sciences (IFAS)
SilvaLab
Led by Carlos Alberto Silva · Forest, Fisheries, and Geomatics Sciences
What we study
SilvaLab studies forests using remote sensing and AI. They develop tools like rLiDAR and rGEDI to monitor forest health, carbon storage, and recovery from disturbances like hurricanes and fires using lidar and satellite data.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Projects
- 2
- Papers
- 4
Current projects
Combining ICESat-2, SAR, and passive optical data for post-hurricane recovery
Monitoring forest recovery after hurricanes using satellite and lidar data.
Fire, Carbon, and the Brazilian Cerrado
Studying how fires affect carbon storage in the Brazilian Cerrado.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Beyond Ice: NASA’s ICESat-2 spaceborne lidar mission for land and vegetation applications
2026 · IEEE Geoscience and Remote Sensing Magazine
Reviews how NASA’s ICESat-2 satellite data is used for studying forests and land beyond its original ice mission.
- A quantitative evaluation of forest aboveground biomass density map products in Oregon, USA
2026 · Environmental Research Letters
Compares different methods for estimating forest biomass in Oregon using satellite and ground data.
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- Fueling the fire: predicting cogongrass (Imperata cylindrica) impacts on fire behavior across environmental and seasonal gradients
2026 · Fire Ecology
Measures how cogongrass affects fire behavior in different seasons and regions, using field data and fire models.
- From Wood–Foliar Semantics to 3D Fuel Characterization: Transferable Deep Learning for Terrestrial LiDAR Across Global Forest Ecosystems
2026 · Remote Sensing
Develops a deep learning method to classify wood and leaves in 3D forest scans, working across different forests and LiDAR systems.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
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