Large UAS Applications for Disaster Response: Near-real-time Inundation Mapping and Beyond

Early initiatives between the National Oceanic and Atmospheric Administration (NOAA) UAS Program Office and the Northern Gulf Institute (NGI) around small unmanned aerial systems (UAS) quickly evolved to include MSU’s Raspet Flight Research Laboratory (Raspet) for exploration of the potential to utilize large UAS. While small UAS have become more prevalent in disaster operations there are limitations in payload, endurance, and range, prompting a demand to explore larger UAS. Starting with a small Group 3 UAS (approximately 160 lbs. & 14’ wingspan) the collaboration has now progressed to Group 4 UAS (approximately 1,800 lbs. & 40’ wingspan). The success of these missions attracted more federal stakeholders, including FEMA Region 4, leading to broader collaborations and expanded research and use-case exploration.

Initial enhancements focused on live tiled imagery and post-processed orthomosaics. The team added a gimbaled EO/IR payload for live video and satellite communication from the ground control station for data management. Iterative testing refined data needs, mission assignments, and coordination, highlighting the strengths and weaknesses of large UAS. Challenges included beyond visual line of sight (BVLOS) operations, FAA airspace access, rapid deployment, weather monitoring, and RF line of sight (RF LOS) limitations. Ongoing activities aim to develop added capabilities, scalability, and refined research products.

Through program iterations and added stakeholders, research goals have continued to grow. One such use case and research need is the situational awareness required for monitoring flood conditions in real time. Identifying inundation extent helps assess human safety risks and emergency services' mobility. Satellite imagery can be unavailable due to clouds and limited overpasses, while small UAS provide inconsistent data or may be unable to reach a desired location. As an application specific to flooding, current research focuses on development of land-water masks derived from visible and near-infrared images transmitted in near-real-time from on-board sensors. The current approach utilizes a modified normalized difference water index (NDWI) calculation, which minimizes error from highly reflective surfaces such as roads and roofs while maximizing computational efficiency for rapid product generation. These images are quickly generated and viewable through a web-based portal on geographic software like Google Earth. While post-processed mosaicked imagery offers smoother and more accurate flood representations, real-time imagery is crucial for emergency operations and decision-making.

Large UAS technology will enhance disaster response by expanding operational capabilities and expediting damage assessment data. Future efforts will improve RF LOS range, deployment structures, and advance autonomy, while applications of AI and machine learning methods to enhance real-time data processing will provide critical insights for more effective disaster response.