ForestFireSense
Forest Fire Risk Assessment and Prediction through Vegetation Modeling and Remote Sensing
Research Project
Forest ecosystems provide essential services, such as carbon sequestration, biodiversity conservation, soil and water preservation, and resources for human use. However, climate change, driven by rising temperatures, shifting precipitation patterns, and intensified droughts, significantly increases the risk of wildfires. This research aims to assess and predict fire risk in European forests by combining remote sensing of tree water content and vegetation modelling.
Vegetation Optical Depth (VOD) data from Sentinel-1, along with in situ data, will be used to assess tree water status (Live Fuel Moisture Content - LFMC), considered as a critical indicator of fire risk. A Fire Risk Index will be developed based on the fire module of the CARAIB dynamic vegetation model, with remote sensing-derived vegetation water status as key input.
Future fire risk predictions will be generated using the CARAIB vegetation model, which will be enhanced with a hydraulic module to simulates water dynamics within plants. This will enable the model to use vegetation water content as an indicator of vegetation flammability, replacing the current reliance on soil moisture. By incorporating this key variable, the improved model will provide more accurate fire risk predictions under various climate scenarios.
Ultimately, this research will allow to produce near real-time wildfire risk maps that will support forest management and climate adaptation strategies.
The Team
Benjamin Lecart (PhD Student)
Prof. Louis François (Co-Supervisor)
Prof. François Jonard (Supervisor)
