This research task develops prescribed fire simulation with dynamic ignitions to support ignition planning and burn size/timing planning for prescribed burn operations. It also supports learning and training for prescribed fires. An outcome of this research is the FireMapSim tool that allows landowners to set up prescribed fire simulations on a map.
This research task integrates data from three heterogeneous data sources: satellite, Unmanned Aircraft Systems (i.e., drones), and crowdsensing to produce useful information for supporting other research tasks. The crowdsensing will be conducted with the help of landowners or community members. A data fusion framework will be developed to integrate hard data from physics-based sensors and soft data from crowdsensing.
This research task quantifies fire-associated risk and environmental impact through a data-driven approach using long-term historic data from multiple sources. It develops an easy-to-use fire risk index and an environmental risk index based on the above data modeling results.
This research task uses multiscale sensing (Satellite, drones, ground measurement) to provide mapping and characterization of vegetation and fuels, which are fundamental to understanding and predicting prescribed fire behavior. It also develops drone technologies to support during-fire and post-fire hot spot detection.
This research task examines how communities engage with and coordinate burn practices through technology. It supports user-focused technology development by providing insight into the role of technology in mediating perceptions of safety in prescribed burning, and identifying both the challenges and opportunities for PBAs to adopt new technologies in diverse contexts.