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This presentation discusses standard techniques and processes used for radiation mapping (RM) via an AMS, Aerial Measurement System. The advantages and shortcomings of standard AMS-based RM are presented, along with some suggested areas for improvement. Issues touched on include what gets counted, data quality, background correction, data processing, altitude correction, isotope extraction, contouring, and time shift.
The Nevada National Security Site (NNSS) provides a comprehensive bicoastal radiological and nuclear emergency response to United States Department of Energy/National Nuclear Security Administration. A major part of the support is to provide systematic radiological search for lost or stolen sources, Radiological search is a core competency of the NNSS with its origin dating back to nuclear weapons test era. Search operations from multiple platforms is the common thread among the various NNSS assets, which include Aerial Measuring System (AMS), Maritime Support Team (MST), National Capitol Response (NCR), National Search Team (NST) and Radiological Assistance Program (RAP). Information collected and analyzed during search operations add to the actionable intelligence for the law enforcement agencies and provide valuable guidance for the tactical resolution of a nuclear or radiological crisis. Search is an intelligence and situational awareness driven operation and most often called upon during a radiological emergency, however it can be brought into play to thwart a potential threat by providing monitoring and surveillance support. The Office of Nuclear Incident Response (NA-84) serves as the technical leader in responding to and resolving nuclear and radiological threats worldwide and integrates its efforts with other NNSA stakeholders (e.g., NNSA office of Defense Nuclear Non-proliferation NA-22). The response includes expertise in the areas of radiological search, render safe, and consequence management. This article will discuss the methodologies, tools, procedures, and techniques to extract maximum radiological characterization information (isotopic composition, activities for individual isotopes, threat assessment etc.) from field monitoring or Search operation data.
Avery Guild-Bingham will present these slides during the 9th Annual AMS Technical Exchange meeting (virtual) held on Oct 6-7, 2021. Attendees include Aerial Measuring System staff and partners from US laboratories, as well as Aerial Radiation detection scientists from partner countries such as the UK, Germany, Switzerland, etc. Overall support provided by NA-81, Office of Nuclear Incident Policy and Cooperation.
This report details the data analysis results from data collected during the September-October 2019 joint survey series between the Department of Energy (DOE) National Nuclear Security Administration (NNSA) Aerial Measuring System (AMS) asset, and the Government of India’s Department of Atomic Energy (DAE) Bhabha Atomic Research Centre (BARC). This joint survey series took place at the Nevada National Security Site (NNSS), areas of public land in Southern Nevada, and at the DOE Office of Legacy Management (LM) Large Area Calibration Pads (LACP) at the Grand Junction Regional Airport in Grand Junction, CO. The data detailed in this report were gathered at the LACP. More details regarding the setup of these surveys and personnel involvement can be found in the AMS / BARC Joint Survey Summary Report. The data from the LACP collected by AMS are consistent with past data collection campaigns at the LACP. The AMS Bell 412 (B412) helicopter, equipped with both the standard AMS thallium-doped sodium iodide (NaI(Tl)) 2"x4"x16" 12-detector system and a subset of the BARC Aerial Gamma Spectroscopy System (AGSS) consisting of two independent 3"x3"cylindrical NaI(Tl) detectors, measured each of the five calibration pads for a minimum of 10 minutes. After the helicopter measurements, a pressurized ion chamber (PIC) was used to directly measure radiation exposure rates on each of the pads for a period 5 minutes. The geophysical calibration procedure published by the International Atomic Energy Agency (IAEA) (Erdi-Krausz et al. 2003) was used to determine the spectral stripping sensitivity matrix for naturally occurring radiological material (NORM). The sensitivity matrix is consistent with the AMS long term average. Additionally, the PIC data was used to calibrate the AMS system for exposure rate. The results of the exposure rate analysis are also consistent with AMS long-term averages.
Aerial Detection and Measurements analysis concept to be presented at the 11th Annual International Aerial Measuring System (iAMS) Technical Exchange workshop in Berne, Switzerland in September, 2023.
In January 2014 the U.S. Department of Energy (DOE), National Nuclear Security Administration (NNSA) Aerial Measuring System (AMS) and the Natural Resources Canada (NRCan) Nuclear Emergency Response project conducted a series of joint surveys at a number of locations in Nevada including the Nevada National Security Site (NNSS). The goal of this project was to compare the responses of the two agencies’ aerial radiation detection systems and data analysis techniques. This test included varied radioactive surface contamination levels and isotopic composition experienced at the NNSS and the differing data processing techniques utilized by the respective teams. Because both teams used the commercial aerial radiation detection systems from Radiation Solutions, Inc., the main focus of the campaign was to investigate the data acquisition techniques, data analysis, and ground-truth verification. The NRCan system consisted of four 4" × 4" × 16" NaI(Tl) scintillator crystals of which two were externally mounted in a modified commercial cargo basket certified for the Eurocopter AS350; the NNSA AMS system consisted of twelve 2" × 4" × 16" NaI(Tl) crystals in externally mounted dedicated pods. For NRCan, the joint survey provided an opportunity to characterize their system’s response to extended sources of various fission products at the NNSS. Since both systems play an important role in their respective countries’ national framework of radiological emergency response and are subject to multiple mutual cooperation agreements, it was important for each country to obtain more thorough knowledge of how they would employ these important assets and define the roles that they would each play in an actual response.
Monday, March 24, 2014, marked the beginning of the first interagency WINGS interoperability drill. Local, state, and federal government agencies with aerial radiation detection capabilities were given the opportunity to either attend a WINGS tabletop or a Mobile-Aerial Radiological Surveillance (MARS) refresher covering standard Aerial Measuring Systems (AMS) flight operations.
Radiation occurs naturally in bedrock and soil. Gamma rays are released from the decay of the radioactive isotopes K, U, and Th. Gamma rays observed at the surface come from the first 30 cm of rock and soil. The energy of gamma rays is specific to each isotope, allowing identification. For this research, data was collected from national databases, private companies, scientific literature, and field work. Data points were then evaluated for self-consistency. A model was created by converting concentrations of U, K, and Th for each rock and soil unit into a ground exposure rate using the following equation: D=1.32 K+ 0.548 U+ 0.272 Th. The first objective of this research was to compare the original Aerial Measurement System gamma ray survey to results produced by the model. The second objective was to improve the method and learn the constraints of the model. Future work will include sample data analysis from field work with a goal of improving the geochemical model.
In response to invitation to participate in the Valahia, Romania 2023 Full Field Exercise in October, 2023, host country is requesting exercise objectives and resource needs from the US Aerial Measuring System participants. This document is to illustrate to the IAEA 3rd Consultancy Workshop meeting on Marc 6-7, 2023 (virtually) what will be expected from AMS at the exercise.
Brechtel Manufacturing Incorporated (BMI) is developing a compact, nanoparticle sizer optimized for use on Unmanned Aerial Vehicles (UAVs) and other applications where space, weight, power and cost are significant barriers to deployment. The instrument will allow observations of size-resolved aerosol number concentration (0.005 < D p < 0.3 μm, D p is particle diameter). The new development helps address the need for reduced uncertainties in atmospheric radiative transfer and weather prediction model sensitivities to ambient aerosol properties by promoting the capability to perform aerosol measurements more often, in more locations, and at reduced cost. Applications of the new technology include vertical atmospheric profiling with UAVs, balloons and other mobile platforms to constrain weather forecast models, long-term air quality measurements, aerosol health effects studies, and routine monitoring of ambient aerosol properties in urban air sheds.
Unmanned aerial system (UAS)-based remote sensing can provide high spatial- and temporal-resolution crop monitoring for precision management. Existing crop monitoring UAS primarily use conventional techniques such as multi-spectral broadband vegetation indices (VIs) to track canopy structure/biomass. Recently developed lightweight hyperspectral sensors can track crop physiology and performance via complementary signals such as solar-induced chlorophyll fluorescence (SIF) and photochemical reflectance index (PRI) that can be derived from hyperspectral reflectance. However, few existing UAS can acquire high-quality SIF and hyperspectral reflectance. Here, we designed a novel UAS that simultaneously captures far-red SIF and hyperspectral reflectance, using a single bifurcated fiber and motorized arm to measure downwelling and upwelling irradiance. The UAS was tested over (1) a heterogeneous corn field with spatially varying crop yield potential and (2) a set of soybean and corn plots under differing nutrient treatments. Our UAS can maintain stability under low-to-moderate winds (2–3 m/s) with <0.4 m geolocation accuracy, <0.1 m altitude drift, and <1.5° error of the fiber optic from nadir position during scanning. Weekly seasonal campaigns reveal that SIF outperforms conventional VIs such as the normalized difference vegetation index (NDVI) for distinguishing plots with different crop yield potential, especially after canopy closure when NDVI tends to saturate. The UAS can capture the diurnal dynamics of SIF and PRI for both corn and soybean. At the seasonal scale, SIF acquired from the UAS correlates well with that from a fixed SIF tower system ( R 2 = 0.81), which measured the same target albeit with different footprints, demonstrating the capability of UAS in characterizing the seasonal progression of crop activity. In conclusion, our newly developed UAS provides high-quality SIF and hyperspectral reflectance, facilitating mechanistic understanding of the physiological control on photosynthesis dynamics. Our findings also imply that this UAS could enable extension of fixed tower-based systems to monitor multiple targets from diurnal to seasonal scales and improve monitoring of heterogeneous fields.
Measurement of bridge displacements is important for ensuring the safe operation of railway bridges. Traditionally, contact sensors such as Linear Variable Displacement Transducers (LVDT) and accelerometers have been used to measure the displacement of the railway bridges. However, these sensors need significant effort in installation and maintenance. Therefore, railroad management agencies are interested in new means to measure bridge displacements. This research focuses on mounting Laser Doppler Vibrometer (LDV) on an Unmanned Aerial System (UAS) to enable contact-free transverse dynamic displacement of railroad bridges. Researchers conducted three field tests by flying the Unmanned Aerial Systems Laser Doppler Vibrometer (UAS-LDV) 1.5 m away from the ground and measured the displacement of a moving target at various distances. The accuracy of the UAS-LDV measurements was compared to the Linear Variable Differential Transducer (LVDT) measurements. The results of the three field tests showed that the proposed system could measure non-contact, reference-free dynamic displacement with an average peak and root mean square (RMS) error for the three experiments of 10% and 8% compared to LVDT, respectively. Such errors are acceptable for field measurements in railroads, as the interest prior to bridge monitoring implementation of a new approach is to demonstrate similar success for different flights, as reported in the three results. This study also identified barriers for industrial adoption of this technology and proposed operational development practices for both technical and cost-effective implementation.
Airborne gamma spectrometry is one of the primary techniques used to define the extent of ground contamination after a radiological incident. Its usefulness was demonstrated extensively during the response to the Fukushima nuclear power plant (NPP) accident in March-May 2011. To map ground contamination a set of scintillation detectors is mounted on an airborne platform (airplane or helicopter) and flown over contaminated areas. The acquisition system collects spectral information together with the aircraft position and altitude every second. To provide useful information to decision makers, the count rate data expressed in counts per second (cps) needs to be converted to the terrestrial component of the exposure rate 1 m above ground, or surface activity of isotopes of concern. This is done using conversion coefficients derived from calibration flights. During a large scale radiological event, multiple flights may be necessary and may require use of assets from different agencies. However, as the production of a single, consistent map product depicting the ground contamination is the primary goal, it is critical to establish very early into the event a common calibration line. Such a line should be flown periodically in order to normalize data collected from different aerial acquisition systems and potentially flown at different flight altitudes and speeds. In order to verify and validate individual aerial systems, the calibration line needs to be characterized in terms of ground truth measurements. This is especially important if the contamination is due to short-lived radionuclides. The process of establishing such a line, as well as necessary ground truth measurements, is described in this document.
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The presentation is a briefing on the history and current activities of the Remote Sensing Laboratory.
A new methodology is described for performing near-free space far-field radiation pattern measurements of high frequency (HF) antennas utilizing small Unmanned Aerial Systems (sUAS) and helium-filled aerostat balloons for the radar antennas onboard NASA’s planned Europa Clipper mission to Jupiter’s moon Europa. Adapted from land-based measurements, this test methodology involves hoisting the antenna to be tested above the earth to minimize ground interactions while flying a sUAS with onboard measurement package to map the far field radiation pattern. Initial results producing radiation pattern maps are promising with work remaining to fully adapt fixed VHF measurements to the dynamic HF antenna test setup.
Correction to: Naturehttps://doi.org/10.1038/s41586-024-07117-5 Published online 13 March 2024 In the version of the article initially published, several errors were present and have been corrected in the HTML and PDF versions of the article and Supplementary Information. The main results, conclusions, and our interpretations of the data remain unchanged. See the new Supplementary Information Section S15 for a more detailed description of the errors corrected and the resulting effects on the analysis. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. The following practices may help researchers conducting similar analyses avoid making similar errors: 1, Clear, accessible documentation explaining the interpretation of all columns in data input tables and all internal variables within the model, 2, Simple cross-check calculations computed before and after unit conversions.