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NASA NTRS2000

Development and Implementation of Real-Time Information Delivery Systems for Emergency Management

The disaster management community has an on-going need for real-time data and information, especially during catastrophic events. Currently, twin engine or jet aircraft with limited altitude and duration capabilities collect much of the data. Flight safety is also an issue. Clearly, much of the needed data could be delivered via over-the-horizon transfer through a uninhabited aerial vehicles (UAV) platform to mission managers at various locations on the ground. In fact, because of the ability to stay aloft for long periods of time, and to fly above dangerous situations, UAV's are ideally suited for disaster missions. There are numerous situations that can be considered disastrous for the human population. Some, such as fire or flood, can continue over a period of days. Disaster management officials rely on data from the site to respond in an optimum way with warnings, evacuations, rescue, relief, and to the extent possible, damage control. Although different types of disasters call for different types of response, most situations can be improved by having visual images and other remotely sensed data available. "Disaster Management" is actually made up of a number of activities, including: - Disaster Prevention and Mitigation - Emergency Response Planning - Disaster Management (real-time deployment of resources, during an event) - Disaster / Risk Modeling All of these activities could benefit from real-time information, but a major focus for UAV-based technology is in real-time deployment of resources (i.e., emergency response teams), based on changing conditions at the location of the event. With all these potential benefits, it is desirable to demonstrate to user agencies the ability to perform disaster management missions as described. The following demonstration project is the first in a program designed to prove the feasibility of supporting disaster missions with UAV technology and suitable communications packages on-board. A several-year program is envisioned, in which a broad range of disaster-related activities are demonstrated to the appropriate user communities.

Wegener, Steve↗

DOE OSTI2012

Atmospheric Radiation Measurement Climate Research Facility Operations Quarterly Report January 1–March 31, 2012

Individual raw datastreams from instrumentation at the Atmospheric Radiation Measurement (ARM) Climate Research Facility fixed and mobile sites are collected and sent to the Data Management Facility (DMF) at Pacific Northwest National Laboratory (PNNL) for processing in near real-time. Raw and processed data are then sent approximately daily to the ARM Data Archive, where they are made available to the research community. For each instrument, we calculate the ratio of the actual number of processed data records received daily at the Archive to the expected number of data records. The results are tabulated by (1) individual datastream, site, and month for the current year and (2) site and fiscal year (FY) dating back to 1998.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Laboratory Simulations of the Titan Surface to Elucidate the Huygens Probe GCMS Observations

The Cassini/Huygens mission has vastly increased the information we have available to stndy Satnro's moon Titan. The complete mission has included an array of observational methods including remote sensing techniques, upper atmosphere in-situ saropling, and the descent of the Huygens probe directly through the atmosphere to the surface [1,2]. The instruments on the Huygens probe remain the ouly source of in-situ measurements at the surface of Titan, and work evaluating these measurements to create a pict.rre of the surface environment is ongoing. In particular, the Gas Chromatograph Mass Spectrometer (GCMS) experiment on Huygens found that although there were no heavy hydrocarbons detected in the lower atmosphere, a rich spectrum of mass peaks arose once the probe landed on the surface [3,4], However, to date it has not been possible to extract the identity and abundances of the many minor components of the spectra due to a lack of temperatnre- and instrumentappropriate data for the relevant species. We are performing laboratory stndies designed to elucidate the spectrum collected on Titan's surface, utilizing a cryogenic charober maintained at appropriate temperature and pressure conditions. The experiments will simulate the temperatnre rise experienced by the surface, which led to an enhanced signal of volatiles detected by the Huygens GCMS. The objective of this study is to exaroine the characteristics of various surface analogs as measured by the Huygens GCMS flight spare instrument, which is currently housed in our laboratory at NASA Goddard Space Flight Center (GSFC). This identification cannot be adequately accomplished through theoretical work alone since the thermodynamic properties of many species at these temperatnres (94 K, HASI measurement [5]) are not known.

Trainer, M. G.↗

DOE OSTI2022

Automated identification of local contamination in remote atmospheric composition time series

Abstract. Atmospheric observations in remote locations offer a possibility of exploring trace gas and particle concentrations in pristine environments. However, data from remote areas are often contaminated by pollution from local sources. Detecting this contamination is thus a central and frequently encountered issue. Consequently, many different methods exist today to identify local contamination in atmospheric composition measurement time series, but no single method has been widely accepted. In this study, we present a new method to identify primary pollution in remote atmospheric datasets, e.g., from ship campaigns or stations with a low background signal compared to the contaminated signal. The pollution detection algorithm (PDA) identifies and flags periods of polluted data in five steps. The first and most important step identifies polluted periods based on the derivative (time derivative) of a concentration over time. If this derivative exceeds a given threshold, data are flagged as polluted. Further pollution identification steps are a simple concentration threshold filter, a neighboring points filter (optional), a median, and a sparse data filter (optional). The PDA only relies on the target dataset itself and is independent of ancillary datasets such as meteorological variables. All parameters of each step are adjustable so that the PDA can be “tuned” to be more or less stringent (e.g., flag more or fewer data points as contaminated). The PDA was developed and tested with a particle number concentration dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic. Using strict settings, we identified 62 % of the data as influenced by local contamination. Using a second independent particle number concentration dataset also collected during MOSAiC, we evaluated the performance of the PDA against the same dataset cleaned by visual inspection. The two methods agreed in 94 % of the cases. Additionally, the PDA was successfully applied to a trace gas dataset (CO2), also collected during MOSAiC, and to another particle number concentration dataset, collected at the high-altitude background station Jungfraujoch, Switzerland. Thus, the PDA proves to be a useful and flexible tool to identify periods affected by local contamination in atmospheric composition datasets without the need for ancillary measurements. It is best applied to data representing primary pollution. The user-friendly and open-access code enables reproducible application to a wide suite of different datasets. It is available at https://doi.org/10.5281/zenodo.5761101 (Beck et al., 2021).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2002

Midlatitude Cirrus Clouds Derived from Hurricane Nora: A Case Study with Implications for Ice Crystal Nucleation and Shape

Hurricane Nora traveled up the Bala Peninsula coast in the unusually warm El Nino waters of September 1997, until rapidly decaying as it approached Southern California on 24 September. The anvil cirrus blowoff from the final surge of tropical convection became embedded in subtropical flow that advected the cirrus across the western US, where it was studied from the Facility for Atmospheric Remote Sensing (FARS) in Salt Lake City, Utah. A day later, the cirrus shield remnants were redirected southward by midlatitude circulations into the Southern Great Plains, providing a case study opportunity for the research aircraft and ground-based remote sensors assembled at the Clouds and Radiation Testbed (CART) site in northern Oklahoma. Using these comprehensive resources and new remote sensing cloud retrieval algorithms, the microphysical and radiative cloud properties of this unusual cirrus event are uniquely characterized. Importantly, at both the FARS and CART sites the cirrus generated spectacular optical displays, which acted as a tracer for the hurricane cirrus, despite the limited lifetimes of individual ice crystals. Lidar polarization data indicate widespread regions of uniform ice plate orientations, and in situ particle masticator data show a preponderance of pristine, solid hexagonal plates and columns. It is suggested that these unusual aspects are the result of the mode of cirrus particle nucleation, presumably involving the lofting of sea-salt nuclei in thunderstorm updrafts into the upper troposphere. This created a reservoir of haze particles that continued to produce halide-saltcontaminated ice crystals during the extended period of cirrus cloud maintenance. The reference that marine microliters are embedded in the replicas of ice crystals collected over the CART site points to the longevity of marine effects. Various nucleation scenarios proposed for cirrus clouds based on this and other studies, and the implications for understanding cirrus radiative properties or a global scale, are discussed.

Sassen, Kenneth↗

NASA NTRSDate not supplied

Dynamic Assurance of Autonomous Systems through Ground Control Software

Assurance cases are being increasingly acknowledged as a way to build trust in complex systems with autonomous capabilities [1]. An assurance case is a comprehensive, defensible, and valid justification that a system will function as intended for the specific mission and operating environment. Such justifications for systems with autonomous capabilities are often based on various probabilistic quantifications [2]. Due to the dynamic nature of the environmental conditions in which these systems operate, as well as the changing nature of the autonomous systems themselves, these probabilistic quantifications cannot be simply estimated once during design time. Rather, they need to be continually evaluated during systems operations to ensure that the assurance case justifications are valid. We refer to the assurance case that combines both the static and dynamic elements as a Dynamic Assurance Case (DAC). Such complex systems with autonomous capabilities are often deployed with a Ground Control Software (GCS) component to enable remote operation. Whether the system is composed of a single unit or a fleet of units, deployed distributed or in remote environments, GCS acts as a window into the behavior of the deployed system. It receives telemetry from the system, issues commands to the system and provides various functionalities to visualize the system performance. We propose a dynamic assurance framework where the GCS acts as a relay between the autonomous system and its DAC. GCS can be used to measure both unit-specific as well as system-wide probabilistic quantifications using the incoming telemetry. We embed these quantifications throughout the DAC as variables that can be updated by external sources. We use the GCS to periodically update these variables, which allows us to continually evaluate the formally defined assurance case justifications. We demonstrate our dynamic assurance framework in the NASA Ames project Troupe1 that aims at developing a fleet of rovers capable of au- tonomously mapping their environment. The rovers work cooperatively, each collecting data for different parts of the environment. Each rover runs an identical core Flight System (cFS) [4] application. Troupe1 uses OpenC3 Cosmos [5] as the ground system, and AdvoCATE [3] to capture the system DAC. We show how we can measure both rover-specific and system-wide quantifications in Cosmos using its Ruby scripting editor and pass them into the DAC modelled in AdvoCATE. Then, we show how these incoming variables can be embedded in different parts of the DAC and how effects of their updates can be observed

Irfan Sljivo↗

DOE OSTI2015

Remote synchronization of amplitudes across an experimental ring of non-linear oscillators

In this paper, the emergence of remote synchronization in a ring of 32 unidirectionally coupled non-linear oscillators is reported. Each oscillator consists of 3 negative voltage gain stages connected in a loop to which two integrators are superimposed and receives input from its preceding neighbour via a “mixing” stage whose gains form the main system control parameters. Collective behaviour of the network is investigated numerically and experimentally, based on a custom-designed circuit board featuring 32 field-programmable analog arrays. A diverse set of synchronization patterns is observed depending on the control parameters. While phase synchronization ensues globally, albeit imperfectly, for certain control parameter values, amplitudes delineate subsets of non-adjacent but preferentially synchronized nodes; this cannot be trivially explained by synchronization paths along sequences of structurally connected nodes and is therefore interpreted as representing a form of remote synchronization. Complex topology of functional synchronization thus emerges from underlying elementary structural connectivity. In addition to the Kuramoto order parameter and cross-correlation coefficient, other synchronization measures are considered, and preliminary findings suggest that generalized synchronization may identify functional relationships across nodes otherwise not visible. Further work elucidating the mechanism underlying this observation of remote synchronization is necessary, to support which experimental data and board design materials have been made freely downloadable.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1984

Air-sampling inlet contamination by aircraft emissions on the NASA CV-990 aircraft

Results of an experimental investigation of the contamination of air sampling inlets by aircraft emissions from the NASA CV-990 research aircraft are presented. This four-engine jet aircraft is a NASA facility used for many different atmospheric and meteorological experiments, as well as for developing spacecraft instrumentation for remote measurements. Our investigations were performed to provide information on which to base the selection of sampling locations for a series of multi-instrument missions for measuring tropospheric trace gases. The major source of contamination is the exhaust from the jet engines, which generate many of the same gases that are of interest in atmospheric chemistry, as well as other gases that may interfere with sampling measurements. The engine exhaust contains these gases in mixing ratios many orders of magnitude greater than those that occur in the clean atmosphere which the missions seek to quantify. Pressurized samples of air were collected simultaneously from a scoop located forward of the engines to represent clean air and from other multiport scoops at various aft positions on the aircraft. The air samples were analyzed in the laboratory by gas chromatography for carbon monoxide, an abundant combustion by-product. Data are presented for various scoop locations under various flight conditions.

Condon, E. P.↗

NASA NTRS2007

Evaluation of Integrating the Invasive Species Forecasting System to Support National Park Service Decisions on Fire Management Activities and Invasive Plant Species Control

The USGS and NASA, in conjunction with Colorado State University, George Mason University and other partners, have developed the Invasive Species Forecasting System (ISFS), a flexible tool that capitalizes on NASA's remote sensing resource to produce dynamic habitat maps of invasive terrestrial plant species across the United States. In 2006 ISFS was adopted to generate predictive invasive habitat maps to benefit noxious plant and fire management teams in three major National Park systems: The Greater Yellowstone Area (Yellowstone / Grand Tetons National Parks), Sequoia and Kings Canyon National Park, and interior Alaskan (between Denali, Gates of The Arctic and Yukon-Charley). One of the objectives of this study is to explore how the ISFS enhances decision support apparatus in use by National Park management teams. The first step with each park system was to work closely with park managers to select top-priority invasive species. Specific species were chosen for each study area based on management priorities, availability of observational data, and their potential for invasion after fire disturbances. Once focal species were selected, sources of presence/absence data were collected from previous surveys for each species in and around the Parks. Using logistic regression to couple presence/absence points with environmental data layers, the first round of ISFS habitat suitability maps were generated for each National Park system and presented during park visits over the summer of 2006. This first engagement provided a demonstration of what the park service can expect from ISFS and initiated the ongoing dialog on how the parks can best utilized the system to enhance their decisions related to invasive species control. During the park visits it was discovered that separate "expert opinion" maps would provide a valuable baseline to compare against the ISFS model output. Opinion maps are a means of spatially representing qualitative knowledge into a quantitative two-dimensional map. Furthermore, our approach combines the qualitative expert opinion habitat maps -- with the quantitative ISFS habitat maps in a difference map that shows where the two maps agree and disagree. The objective of the difference map is to help focus future field sampling and improve model results. This paper presents a demonstration of the habitat, expert opinion, and difference map for Yellowstone National Park.

Ma, Peter↗

DOE OSTI2024

Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows

The need for real-time processing to enable automated decision making and experimental steering has driven a shift from high-performance computing workflows on a centralized system to a distributed approach that integrates remote data sources, edge devices, and diverse compute facilities. Under this paradigm, data can be processed close to the source where it is generated, thus reducing latency and bandwidth usage. System resilience is thus a key challenge, requiring distributed workflows to survive component failures and to meet stringent quality-of-service requirements, which results in the need to mitigate anomalies such as congestion and low availability of resources. To address these challenges, we propose Diaspora, a unified resilience framework that is inspired by event-driven communication patterns used in public clouds. Specifically, we propose an event fabric that extends across sites, facilities, and computations to provide timely, reliable, and accurate information about data, application, and resource status. On top of the event fabric, we build resilience-enabling services that combine QoS-aware data streaming, resilient data views, resilient compute and data resources, and anomaly detection and prediction, all of which collectively enhance workflow resilience for these scientific cases.

Rao, Nageswara↗

DOE OSTI2010

3-D MAPPING TECHNOLOGIES FOR HIGH LEVEL WASTE TANKS

This research investigated four techniques that could be applicable for mapping of solids remaining in radioactive waste tanks at the Savannah River Site: stereo vision, LIDAR, flash LIDAR, and Structure from Motion (SfM). Stereo vision is the least appropriate technique for the solids mapping application. Although the equipment cost is low and repackaging would be fairly simple, the algorithms to create a 3D image from stereo vision would require significant further development and may not even be applicable since stereo vision works by finding disparity in feature point locations from the images taken by the cameras. When minimal variation in visual texture exists for an area of interest, it becomes difficult for the software to detect correspondences for that object. SfM appears to be appropriate for solids mapping in waste tanks. However, equipment development would be required for positioning and movement of the camera in the tank space to enable capturing a sequence of images of the scene. Since SfM requires the identification of distinctive features and associates those features to their corresponding instantiations in the other image frames, mockup testing would be required to determine the applicability of SfM technology for mapping of waste in tanks. There may be too few features to track between image frame sequences to employ the SfM technology since uniform appearance may exist when viewing the remaining solids in the interior of the waste tanks. Although scanning LIDAR appears to be an adequate solution, the expense of the equipment ($80,000-$120,000) and the need for further development to allow tank deployment may prohibit utilizing this technology. The development would include repackaging of equipment to permit deployment through the 4-inch access ports and to keep the equipment relatively uncontaminated to allow use in additional tanks. 3D flash LIDAR has a number of advantages over stereo vision, scanning LIDAR, and SfM, including full frame time-of-flight data (3D image) collected with a single laser pulse, high frame rates, direct calculation of range, blur-free images without motion distortion, no need for precision scanning mechanisms, ability to combine 3D flash LIDAR with 2D cameras for 2D texture over 3D depth, and no moving parts. The major disadvantage of the 3D flash LIDAR camera is the cost of approximately $150,000, not including the software development time and repackaging of the camera for deployment in the waste tanks.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Linking drought legacy effects across scales: From leaves to tree rings to ecosystems

Severe drought can cause lagged effects on tree physiology that negatively impact forest functioning for years. These “drought legacy effects” have been widely documented in tree-ring records and could have important implications for our understanding of broader scale forest carbon cycling. However, legacy effects in tree-ring increments may be decoupled from ecosystem fluxes due to (a) postdrought alterations in carbon allocation patterns; (b) temporal asynchrony between radial growth and carbon uptake; and (c) dendrochronological sampling biases. In order to link legacy effects from tree rings to whole forests, we leveraged a rich dataset from a Midwestern US forest that was severely impacted by a drought in 2012. At this site, we compiled tree-ring records, leaf-level gas exchange, eddy flux measurements, dendrometer band data, and satellite remote sensing estimates of greenness and leaf area before, during, and after the 2012 drought. Additionally, after accounting for the relative abundance of tree species in the stand, we estimate that legacy effects led to ~10% reductions in tree-ring width increments in the year following the severe drought. Despite this stand-scale reduction in radial growth, we found that leaf-level photosynthesis, gross primary productivity (GPP), and vegetation greenness were not suppressed in the year following the 2012 drought. Neither temporal asynchrony between radial growth and carbon uptake nor sampling biases could explain our observations of legacy effects in tree rings but not in GPP. Instead, elevated leaf-level photosynthesis co-occurred with reduced leaf area in early 2013, indicating that resources may have been allocated away from radial growth in conjunction with postdrought upregulation of photosynthesis and repair of canopy damage. Collectively, our results indicate that tree-ring legacy effects were not observed in other canopy processes, and that postdrought canopy allocation could be an important mechanism that decouples tree-ring signals from GPP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Compendium of HFIR and REDC Infrasound and Low-Frequency Acoustic Emissions

This document is a compilation of infrasound and low-frequency acoustic signatures that characterize select nuclear reactor operations and hot-cell operations observed at Oak Ridge National Laboratory (ORNL) in fiscal year 2018. Also included here are the results of digital signal analyses describing these signatures. This effort provides insight about the feasibility of using acoustic signatures for local and remote-sensing and identification of nuclear fuel-cycle operations. The signatures included here, in this first year of on-site observations at ORNL, will become part of the broader basis set of signatures collected by researchers involved in the Multi-Informatics for Nuclear Operations Scenarios (MINOS) venture. The effort by MINOS researchers is to gain a deeper knowledge base of nuclear facility operations which may be used to improve nuclear facility monitoring capabilities. This compendium comprises signatures developed from sound signals that were measured at ORNL's High Flux Isotope Reactor and the Radiochemical Engineering Development Center during the week of June 18, 2018. Most facility operations were performed specifically for this campaign, although some measurements were obtained in the course of normal operations. The knowledge gained from developing the signatures included herein will provide a better understanding that will be useful for upcoming measurement campaigns; as additional data becomes available it will be included in newer versions of this compendium.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Compendium of HFIR and REDC Infrasound and Low-Frequency Acoustic Emissions

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

The ABCflux database: Arctic–boreal CO 2 flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO 2 ) fluxes in terrestrial ecosystems across the rapidly warming Arctic–boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic–boreal CO 2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO 2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June–August; 32 %), and fewer observations were available for autumn (September–October; 25 %), winter (December–February; 18 %), and spring (March–May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO 2 fluxes and to better estimate the terrestrial ABZ CO 2 budget.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1993

Mineralogic variability of the Kelso Dunes, Mojave Desert, California derived from Thermal Infrared Multispectral Scanner (TIMS) data

Mineral identification and mapping of alluvial material using thermal infrared (TIR) remote sensing is extremely useful for tracking sediment transport, assessing the degree of weathering and locating sediment sources. As a result of the linear relation between a mineral's percentage in a given area (image pixel) and the depth of its diagnostic spectral features, TIR spectra can be deconvolved in order to ascertain mineralogic percentages. Typical complications such as vegetation, particle size and thermal shadowing are minimized upon examination of dunes. Actively saltating dunes contain little to no vegetation, are very well sorted and lack the thermal shadows that arise from rocky terrain. The primary focus of this work was to use the Kelso Dunes as a test location for an accuracy analysis of temperature/emissivity separation and linear unmixing algorithms. Accurate determination of ground temperature and component discrimination will become key products of future ASTER data. A decorrelation stretch of the TIMS image showed clear color variations within the active dunes. Samples collected from these color units were analyzed for mineralogy, grain size, and separated into endmembers. This analysis not only revealed that the dunes contained significant mineralogic variation, but were more immature (low quartz percentage) than previously reported. Unmixing of the TIMS data using the primary mineral endmembers produced unique variations within the dunes and may indicate near, rather than far, source locales for the dunes. The Kelso Dunes lie in the eastern Mojave Desert, California, approximately 95 km west of the Colorado River. The primary dune field is contained within a topographic basin bounded by the Providence, Granite Mountains, with the active region marked by three northeast trending linear ridges. Although active, the dunes appear to lie at an opposing regional wind boundary which produces little net movement of the crests. Previous studies have estimated the dunes range from 70% to 90% quartz mainly derived from a source 40 km to the west. The dune field is assumed to have formed in a much more arid climate than present, with the age of the deposit estimated at greater than 100,000 years.

Ramsey, Michael S.↗