In September 2018, Hurricane Florence struck the southeastern United States, depositing an estimated ten trillion gallons of water on the states of North Carolina and South Carolina. The resulting floodwaters caused the loss of human lives and inflicted tremendous economic costs upon the states. The flooding was particularly damaging to both livestock and crops, and due to the importance of agriculture to the economy of North Carolina, this damage is of special concern. Overhead sensing modalities, including synthetic aperture radar (SAR) and optical imagery, provide tools to study the affected regions using time-varying geospatial analytics. We discuss our work with the analysis of floodwaters related to livestock waste waters and to crop health. We discuss several collection platforms as well as the sensor geometries in order to discuss the performance trade-offs. We process overhead data sets to analyze the floodwater surface areas and normalized differential vegetation index (NDVI) before and after the time of the hurricane to understand the effects of the storm as it relates to agriculture in North Carolina.
Mapping alternating current (AC) magnetic field strengths over time by mapping of the field strengths presents detailed information of human activities and the presence of facilities. Unlike few fixed location viewing, with a moving detector measurement accuracy becomes more challenging. The use of real-time geospatial analytics requires several changes in detection methodologies and data dissemination. For small unmanned airborne systems (sUAS), ensuring the rotational stability of the sensor while reducing the self-sensing noise presents design challenges in the airframe. For ground vehicles, the rapidly changing values of the AC magnetic fields require rapid and accurate updates of the position. This information must be precisely indexed to magnetic field strength frequency and phase information. We discuss our work with the collection of multi-axis magnetic field data from both multi-rotor sUAS and ground vehicles. We model the power lines using detailed measurements of the conductors and field modeling software. We study the sUAS magnetic field data as a function of height above the ground plane of the experimental sites for both the fundamental and harmonic frequencies and phases relative to a fixed location reference time signal. The collection regions include rural, interstate, and suburban areas with both overhead and buried power lines contributing to the signals.
The electrical power consumption of an area is indicative of industrial activities of high power-consumption facilities. Research on detection and characterization of loads on AC lines has shown that magnetic field sensors at a fixed point can be used to determine the electrical current flowing through a line as a means of determining power consumption versus time. The relative frequency harmonic power and phase content of the current flow can distinguish between types of electrical loads (i.e., resistive or inductive) and changes in those loads. Coupled with an understanding of the line geometry (conductors and cross-sectional location) in the modeling, we can model the geographic distribution of the fields from a given current source. Experimentally, we collect multiple axis magnetic field data from moving vehicles with GPS time and location data for the data recordings. The collection regions include rural, interstate, and suburban areas with both overhead and buried power lines contributing to the signals. We analyze the data using ArcGIS to visualize the geospatial content and compare qualitatively and quantitatively the power levels to data layers such as the area land use. We examine the 60 Hz fundamental frequency, harmonic and non-harmonic signals, and compare the results to 2-D and 3-D modeling tools using known power line conductors. We discuss the effects of the time-varying presence of vehicles in modifying the detected signals as well as the changes in the spectral information over time.
Monitoring the level and type of power consumption in an area over a period of time by mapping of field strengths presents detailed information of human activities and the presence of facilities. Power grid usage traditionally has been collected for subsequent viewing at a few fixed locations. Transitioning from collect-and-view to real-time geospatial analytics over a continuous spatial coverage requires making more extensive use of moving sensors. Unmanned airborne systems (UAS) provide mobility in three dimensions, but also present noise issues and severe weight constraints. We discuss our work with collection of multi-axis magnetic and electric field data from a quadcopter UAS. We model collection physical sensor geometries as well as sensor electronics in order to discuss the performance trade-offs. We collect electromagnetic data at several heights above the ground plane of the target sites and calculate the fundamental and harmonic frequency power of the data as well as the self-sense noise. We analyze the data using ArcGIS for visualization of power “hot spots” for different combinations of power spectral data and compare the results to 3-D modeling tools to estimate the magnetic and electric field strengths. We discuss the results of our experiments in using the UAS to perform advanced processing on board in real time in order to initiate cross-cueing of sensors.