Advancing LiDAR and machine-learning applications for managing wildfires and cultural resources on public lands: Case studies in collaborative research with federal and academic partners

Wildfires of uncharacteristic size, severity, and frequency, a consequence of climate change and fuel accumulation, pose a significant risk to cultural resources on public lands. Cultural and heritage resources are irreplaceable and non-renewable, have cultural or religious significance for living peoples, and are protected by an extensive body of legislation. However, significant gaps exist between the fire ecology/archaeological research communities and the actionable science deployed by resource specialists and managers on public lands. New science, tools, and mitigation strategies are needed to enhance management readiness and planning before, during, and after wildfires occur, however practitioners rarely have the time or resources to develop them. In this presentation, I present two case studies that highlight how new approaches to data collection, synthesis, and modeling can amplify our ability to effectively manage and research archaeological resources on public lands. These include 1) utilizing LiDAR datasets to map cultural fire and fuel conditions in the past with implications for modern fuels management; and 2) leveraging machine-learning approaches to archaeological object detection for enhancing site inventorying and wildfire protection measures. These projects primarily focus on the intersection between archaeological research and management in the context of wildfire across the United States, however they provide a blueprint for cooperative and applied research that has implications for agencies and academics alike.