Prescribed fires have long been used by ranchers and farmers in the Great Plains as a land management tool. They help farming and grazing by replenishing the soil, increasing forage production, and protecting prairies from invasive overgrowth. They are also used by rural and Wildland Urban Interface (WUI) communities to remove built-up fuels for reducing risks of wildfires. Despite the many benefits of prescribed fires, there are safety and environmental concerns for prescribed burn events. On the safety aspect, an escaped fire or a fire reignited from smoldering fuels can become uncontrolled and result in severe property damages and injuries to people. On the environmental aspect, smoke from prescribed fires causes air pollution for local communities and communities downwind. To manage and minimize these concerns, optimal planning and execution of prescribed fires are crucial. The objective of this project is to develop a community sensing, planning, and learning infrastructure to support smart and safe prescribed burning for communities that use prescribed fires for rangeland and wildfire risk management. The developed infrastructure will be integrated into a cloud-based platform to support landowners to optimally plan and operate prescribed burns, collect and share data about burning, and train fire operators to learn the most effective ways of burning. The project will also promote technology awareness for building smart communities in rural areas, by increasing partnerships among academia, rural communities, and local governments.

The integrated research of this project includes: 1) technical research on multi-scale sensing and data fusion, data-driven burn condition modeling, grassland fuel mapping & hotspot detection, and fire behavior modeling and simulation; 2) social science research that addresses the knowledge gap on how communities engage with and coordinate burn practices through the use of technology; and 3) community engagement that develops tools, data repositories, and activities to support communities’ smart and safe prescribed burning. The multiscale sensing and data fusion integrate data from heterogeneous sources including satellite remote sensing, unmanned aircraft systems, and crowdsourced reports. We will work with two communities in Kansas to evaluate and demonstrate the developed research: 1) The Gyp Hills community represents a rangeland community where an average prescribed fire covers over hundreds of acres for grasslands primarily used for grazing; 2) the Eastern Kansas community represents a suburban WUI community where prescribed fires are employed at a smaller scale.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.