This PhD research aims to exploit SAR tomography (TomoSAR) to define a new framework for the estimation of forest fuel attributes and fuel maps in forest fire risk areas. It will use Sentinel-1 SAR to investigate deviations of spatio-temporal patterns in canopy moisture. This information is needed by planners, land managers and emergency services. The test site (Gabon) is an area with an increased risk of forest fires. For the fuel maps, the project will evaluate and assess the framework at a local scale using multi-baseline airborne SAR images, which will be processed in order to synthesize 3D data in a TomoSAR configuration. Possibilities and limitations of forest fuel mapping using SAR tomography are identified. The TomoSAR configuration is used for monitoring purposes, using high resolution remote sensing data. The study will include aspects of representativeness, to ensure that the method is robust and can be transferred to other forests. Validation and assessment of tomographic experiments for forest fuel mapping will be performed with existing field data and lidar data. A field visit to collect additional forest data may be included. Future applicability of the developed framework will be in using a new generation of spaceborne SAR sensors and particularly the future NASA-ISRO (NISAR) system.