Addressing coastal monitoring data gaps in the Great Lakes of North America using UAVs, ROVs, and community scientists

Coastlines along the Great Lakes of North America share many similarities with ocean and estuarine coasts but also have important differences in processes that must be accounted for in coastal monitoring and management. The most obvious of these differences is the frequency and magnitude of water level variability. Lake levels fluctuate on the order of meters on decadal timescales and the changes can be rapid, such as the 1.7 m rise recorded from 2013 to 2020 in Lake Michigan. These fluctuations in lake level modulate the impacts of other physical processes, such as shore ice and storm waves. Despite the dynamic nature of Great Lakes coastal processes long-term monitoring of coastal change is minimal. Annual aerial photography and approximately decadal topobathy LIDAR are the primary region-wide coastal change data sources. Higher spatiotemporal resolution datasets do not exist for most locations along the Great Lakes, creating a data gap for research efforts to understand and predict regional morphodynamics. Advances in technology and data collection methods including UAVs, ROVs and community science create new opportunities for large-scale and rapid monitoring of coastal changes. These technologies facilitate high spatiotemporal resolution monitoring of impacts and processes associated with Great Lakes water level fluctuations, storms, and shore ice, which provide important information to managers and researchers. The implementation of these technologies by our research team has allowed us to document coastal processes at spatiotemporal scales never before achieved in the region. This talk will highlight these findings, which reveal novel morphodynamic insights associated with shore ice, coastal engineering, and fluctuating lake levels. While such knowledge is important, perhaps most valuable for coastal decision-makers is the fact that these technologies can be easily implemented by community scientists. This empowers communities to gather their own data to aid in planning and management efforts. This talk will also explore the utilization of these technologies for crowd-sourced data collection by describing findings from an ongoing NSF project aimed at developing a drone-based community science network in the Great Lakes region focused on coastal change monitoring.