An Intelligent Systems Approach to Measuring Surface Flow Velocities in River Channels

The goal of this project is to develop a New Observing Strategy (NOS) for measuring streamflow from a UAS using an intelligent system. This framework will satisfy the AIST program objectives of enabling new measurements through intelligent, timely, and dynamic distributed sensing; facilitating agile science investigations that utilize diverse observations using advanced analytic tools and computing environments; and supporting applications that inform decisions and guide actions for societal benefit. More specifically, by focusing on hydrologic data collection, this project is consistent with NASA's vision for NOS: optimize measurement campaigns by using diverse observing and modeling capabilities to provide complete representations of a critical Earth Science phenomenon - floods. The USGS operates an extensive monitoring network but maintaining streamgages is expensive and places personnel at risk. This project will build upon a UAS-based payload for measuring surface flow velocities in rivers, developed jointly by the USGS and NASA, to improve the efficiency and safety of data collection. The sensor package consists of thermal and visible cameras, a laser range finder, and an embedded computer, all integrated within a common software middleware. At present, these instruments provide situational awareness for the operator on the ground by transmitting a reduced frame rate image stream. Our current concept of operations involves landing the UAS, downloading the images, and performing Particle Image Velocimetry (PIV). This analysis includes image pre-processing, stabilization, geo-referencing, and an ensemble correlation algorithm that tracks the displacement of water surface features. For this project, the workflow will be adapted for real-time implementation onboard the platform. Developing this NOS is timely because the impacts of climate change on rivers create a compelling need for reliable hydrologic information not only through regular monitoring but also in response to hazardous events. Our intelligent system will be designed to address both of these scenarios. First, we will facilitate quality control during routine streamgaging operations by quantifying uncertainty. For example, the UAS could be directed to hover at a fixed location above the channel and acquire images until a threshold that accounts for natural variability and measurement error is reached; only after this criterion is satisfied would the platform advance to the next station. We refer to this mode of operations as stationing autonomy. Second, during hazardous flood conditions identified via communication with other sensors, the focus of the intelligent system would shift to autonomous route-finding. To enable dynamic data collection, heavy precipitation or abrupt rises in water level within a basin would trigger deployment of a UAS to measure streamflow during a flood. Onboard PIV will provide the intelligent system with real-time velocity information to direct the UAS to focus on high velocity zones, areas likely to scour, and threatened infrastructure. This information could be transmitted wirelessly and used to inform disaster response. By developing these capabilities, we will introduce a NOS that significantly enhances both hydrologic monitoring and response to extreme events. Our intelligent systems framework will be implemented in three phases. Initially, simulations will be used to develop methods of characterizing uncertainty and selecting optimal routes. These simulations will be based on field data sets from a range of river environments and created within a real-time robotics simulator. In the second stage, the algorithms will be applied to data recorded during previous UAS flights. The third phase will apply the intelligent system to live data during a flight, with the sensor payload being used for verification. This progression will transition the NOS from an initial TRL of 4 to 6 by the conclusion of the two-year effort.

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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