LiDAR Remote Sensing of Forest Resources and Wildland Fires
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 July 2021) | Viewed by 34291
Special Issue Editors
Interests: Wildfire; satellite remote sensing; extreme weather events; fire management; fire ecology; global change; burn severity
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2. School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
Interests: LiDAR and hyperspectral remote sensing; tropical forest structure and ecology; industrial forest plantations; algorithms and tools development; data integration and change detection
Special Issues, Collections and Topics in MDPI journals
Interests: forests and nontimber forest products; tropical forest ecology; remote sensing; LiDAR; forest inventory; wildfire; data integration; change detection; fire ecology and fire behavior modeling
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing applications using AI; retrieval of biophysical properties using AI; environmental modeling; spatial data analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
LiDAR (light detection and ranging) remote sensing has emerged as a technology that is well-suited for providing accurate estimates of forest attributes in a wide variety of forest ecosystems at a variety of spatial scales. Wildland fires burn millions of hectares every year, and their impacts are of high interest for society, especially in the wildland urban interface.
The purpose of this Special Issue is to bring together state-of-the-art of remote sensing for forest resource management and wildland fire science. Review papers, technical notes, and research contributions are suitable. In particular, novel contributions covering, but not limited to, the following subtopics described below are welcome:
- Forest attribute estimation at individual tree and landscape levels using lidar and photogrammetry 3-D derived point cloud data applied to wildfire management;
- Machine learning and deep learning approaches for estimating forest structure attributes. Fuel mapping and estimation of canopy characteristics across the landscape. Analysis of spatial and temporal changes of vegetation and associated attributes;
- Use of LiDAR remote sensing data to assess fire/burn severity. Fire effects and post-wildfire landscape change and erosional processes. Quantification of biomass consumption and carbon release;
- Integration and data fusion approaches using multiple remote sensing data sources to estimate fire progression and burned area. Additionally, fire simulation and fire behavior analysis based on remote sensing data;
- New methodologies to estimate live and dead fuel moisture content;
- LiDAR measurements of wildfire smoke over urban environments;
- Characterization of the wildland fire exposure and risk. Wildfire prevention and planning based on remote sensing technologies;
- Synergies among platforms (airborne, terrestrial, and spaceborne) for forest inventory and monitoring.
Dr. Adrian Cardil
Dr. Carlos Alberto Silva
Prof. Dr. Carine Klauberg
Dr. Veraldo Liesenberg
Guest Editors
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