Remote Sensing Data Fusion for Mapping Ecosystem Dynamics

Dear Colleagues,

We are entering an exciting era for active remote sensing of forests. Products generated from spaceborne missions, like GEDI and ICEsat-2, are starting to enable a richer understanding of terrestrial processes and ecology. However, there is a pressing need to develop and test new approaches and frameworks for efficiently combining these current and upcoming datasets. For instance, GEDI and ICESat-2 present an opportunity to obtain global-scale coverage of forest structure, but use different sampling strategies for collecting data and therefore the fusion of these datasets with wall-to-wall data, such as Landsat 8 OLI and Sentinel 2A, as well those that will be provided by the NISAR mission, will be essential for high-resolution mapping of forest attributes across landscapes.

The purpose of this Special Issue is to bring together state-of-the-art remote sensing data fusion approaches for mapping ecosystem dynamics. Review papers and research contributions are suitable. In particular, contributions covering the following subtopics are welcome:

• Calibration of satellite data from lidar (airborne and UAV-borne) and photogrammetry 3-D derived point cloud data.
• Machine learning and deep learning approaches for estimating forest structure attributes. Analysis of spatial and temporal changes of vegetation and associated attributes. 
• Use of remote sensing fusion data to assess forest structure. For instance, fire damage, logging, and dynamics at the landscape scale. 
• Remote sensing data sources to estimate fire progression and burned area. Fire simulation and fire behavior analysis based on remote sensing data.
• New methodologies to estimate forest structure by remote sensing fusion data.
• Estimation of the wild fauna by remote sensing fusion data.
• Synergies among platforms (airborne, terrestrial, and spaceborne) for forest inventory and monitoring.
• Spatial extrapolation methods, sampling design, and error propagation studies using remote sensing fusion data

Dr. Carlos Alberto Silva
Dr. Danilo Roberti Alves de Almeida
Dr. Eben North Broadbent 
Dr. Ruben Valbuena 
Dr. Carine Klauberg 
Dr. Adrian Cardil
Guest Editor

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