Large-Scale Forest Mapping and Monitoring by Synthetic Aperture Radar (SAR) and Multi-Source Remote Sensing Data

Dear Colleagues,

Global forest inventory data (including forest height, biomass, classification, and volume) is of critical importance for global carbon flux calculations and climate change research. Given the intensification of climate change and human activities in the past few years, it is imperative to develop technologies for rapid and high-precision forest mapping and monitoring at a large scale. Synthetic aperture radar (SAR) provides great opportunities for us to investigate the forest system due to its penetration ability and its ability to acquire information about the forest vertical structure and biophysical properties. Particularly, ESA’s BIOMASS (P-band) and NASA-ISRO’s NISAR (L-band) mission will be launched in the upcoming years, which opens a new era of long-wavelength SAR remote sensing, characterized by stronger penetration into the forest canopy. On the other hand, spaceborne light detection and ranging (LiDAR) provides sparse data acquisition but higher precision measurement for each single point compared with SAR. Other spaceborne optical sensors can also provide redundant observations that effectively avoid the limitations brought by the intuitive nature of side-looking SAR, and can, therefore, further improve the accuracy of forest mapping and monitoring. Leveraging the synergies of SAR and other multi-source data is beneficial to improving not only the accuracy of forest parameter retrieval but also the robustness of large-scale forest mapping. This Special Issue aims to delve deep into innovative applications of these techniques for forest inventory, forest system investigation, and monitoring forest dynamics. We also invite research that uses machine learning and deep learning methodologies for forest parameters retrieval across different scales.

Welcome research topics include, but are not limited to, the following:

• PolSAR scattering mechanisms and PolSAR decomposition model;
• Polarimetric SAR interferometry (PolInSAR) data processing theory and methods for forest applications;
• Forest parameter (e.g., height, biomass, horizontal/vertical structure parameter) estimation by InSAR/PolInSAR/TomoSAR;
• Sub-canopy topography mapping by InSAR/PolInSAR/TomoSAR;
• Forest dynamic change monitoring by (Pol)SAR data;
• LiDAR data processing and algorithm development;
• Improved forest mapping by fusion of SAR and LiDAR;
• Large-scale forest mapping with SAR and multi-source remote sensing data using machine learning models.

Dr. Haiqiang Fu
Dr. Qinghua Xie
Guest Editors

Manuscript Submission Information

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• SAR
• polarimetric SAR
• interferometry
• tomographic SAR
• bio-physical parameter
• forest vertical structure
• forest dynamics