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DOE OSTI2025

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗

DOE OSTI2017

Imaging Nutrient Distribution in the Rhizosphere Using FTIR Imaging

Symbiotic associations in the rhizosphere between plants and microorganisms lead to efficient changes in the distribution of nutrients that promote growth and development for each organism involved. Understanding these nutrient fluxes provides insight into the molecular dynamics involved in nutrient transport from one organism to the other. Here, to study such a nutrient flow, a new application of Fourier transform infrared imaging (FTIRI) was developed that entailed growing Populus tremulodes seedlings on a thin, nutrient-enriched Phytagel matrix that allows pixel to pixel measurement of the distribution of nutrients, in particular, nitrate, in the rhizosphere. The FTIR spectra collected from ammonium nitrate in the matrix indicated the greatest changes in the spectra at 1340 cm -1 due to the asymmetric stretching vibrations of nitrate. For quantification of the nitrate concentration in the rhizosphere of experimental plants, a calibration curve was generated that gave the nitrate concentration at each pixel in the chemical image. These images of the poplar rhizosphere showed evidence for symbiotic sharing of nutrients between the plant and the fungi, Laccaria bicolor, where the nitrate concentration was five times higher near mycorrhizal roots than further out into the rhizosphere. This suggested that nitrates are acquired and transported from the media toward the plant root by the fungi. Similarly, the sucrose used in the growth media as a carbon source was depleted around the fungi, suggesting its uptake and consumption by the system. In conclusion, this study is the first of its kind to visualize and quantify the nutrient availability associated with mycorrhizal interactions, indicating that FTIRI has the ability to monitor nutrient changes with other microorganisms in the rhizosphere as a key step for understanding nutrient flow processes in more diverse biological systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

WE-E-BRE-02: BEST IN PHYSICS (THERAPY) - Stereotactic Radiotherapy for Renal Sympathetic Ablation for the Treatment of Refractory Hypertension

Purpose: To determine the safety and efficacy of stereotactic radiotherapy as a novel treatment for patients with refractory hypertension in a swine model. Uncontrolled hypertension is a significant contributor to morbidity and mortality, substantially increasing the risk of ischemic stroke, ischemic heart disease, and kidney failure. Methods: High-resolution computed tomography (CT) images of anesthetized pigs were acquired and treatment plans for each renal artery and nerve were developed using our clinically implemented treatment planning system. Stereotactic radiotherapy, 40Gy in single fraction was delivered bilaterally to the renal nerves using a state-of-the-art medical linear accelerator under image guidance utilizing dynamic conformal arcs. Dose to nearby critical organs was evaluated by dosevolume histogram analysis and correlated to toxicity data obtained through follow up pathology analysis. The animals were observed for six months with serial measurements of blood pressure, urine analysis, serum laboratories, and overall clinical and behavioral status. Results: All animals survived to the follow-up point without evidence of renal dysfunction (stable serum creatinine), skin changes, or behavioral changes that might suggest animal discomfort. Plasma norepinephrine levels (ng/ml) were followed monthly for 6 months. The average reduction observed was 63%, with the median reduction at 73.5%. Microscopic evaluation 4–6 weeks after treatment showed evidence of damage to the nerves around treated renal arteries. Considerable attenuation in pan neurofilament expression by immunohistochemistry was observed with some vacuolar changes indicative of injury. There was no histological or immunohistochemical evidence of damage to nearby spinal cord or spinal nerve root structures. Conclusion: Our preclinical studies have shown stereotactic radiotherapy to the renal sympathetic plexus to be safe and effective in reducing blood pressure, thus this approach holds great promise as a novel treatment modality for patients with refractory hypertension. This study was funded by the Stanford University Cardiovascular Institute. B. Loo and P. Maxim have received funding from RaySearch Laboratories.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Rewiring the unfolded protein response for plant growth recovery after stress

The unfolded protein response (UPR) is a highly coordinated signaling network that alleviates endoplasmic reticulum (ER) stress, a condition induced by diverse environmental challenges in plants. Over the past two decades, substantial progress has been made in elucidating the genetic and molecular mechanisms of ER stress sensing and signal transduction in plants, largely through studies in the model plant Arabidopsis thaliana . These advances have established the UPR as a central regulator of proteostasis and underscored its broader relevance to plant growth and development and crop productivity under stress conditions. Despite this progress, critical knowledge gaps remain, particularly concerning the downstream biological processes required for growth recovery once ER stress has subsided and how these processes are coordinated by UPR regulators. Recent systems-level and integrative studies have begun to reveal critical roles of UPR signaling in pathways governing growth re-establishment and homeostasis of nutrient allocation and energy metabolism. In this review, we highlight recent findings on the functional roles of the plant UPR in recovery from ER stress, with a focus on mechanisms mediated by UPR regulators and downstream biological pathways that enable the transition from stress mitigation to growth restoration. Although this research area is still emerging, accumulating evidence supports a model in which the UPR functions as a dynamic regulatory network that actively coordinates post-stress physiological recovery to support plant fitness.

ER stress↗

DOE OSTI2022

Sugar loading is not required for phloem sap flow in maize plants

We report phloem transport of photoassimilates from leaves to non-photosynthetic organs, such as the root and shoot apices and reproductive organs, is crucial to plant growth and yield. For nearly 90 years, evidence has been generally consistent with the theory of a pressure-flow mechanism of phloem transport. Central to this hypothesis is the loading of osmolytes, principally sugars, into the phloem to generate the osmotic pressure that propels bulk flow. Here we used genetic and light manipulations to test whether sugar import into the phloem is required as the driving force for phloem sap flow. Using carbon-11 radiotracer, we show that a maize sucrose transporter1 (sut1) loss-of-function mutant has severely reduced export of carbon from photosynthetic leaves (only ~4% of the wild type level). Yet, the mutant remarkably maintains phloem pressure at ~100% and sap flow speeds at ~50–75% of those of wild type. Potassium (K+) abundance in the phloem was elevated in sut1 mutant leaves. Fluid dynamic modelling supports the conclusion that increased K+ loading compensated for decreased sucrose loading to maintain phloem pressure, and thereby maintained phloem transport via the pressure-flow mechanism. Furthermore, these results suggest that sap flow and transport of other phloem-mobile nutrients and signalling molecules could be regulated independently of sugar loading into the phloem, potentially influencing carbon–nutrient homoeostasis and the distribution of signalling molecules in plants encountering different environmental conditions.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Fluid Dynamics Effects on Microstructure Prediction in Single Laser Tracks for Additive Manufacturing

The Laser Powder Bed Fusion Additive Manufacturing (LPBFAM) is one of the most important processes for the production of lightweight, cost-effective, complex, and high-performance enduse parts. At present, the cost and time associated with LPBFAM process development is very high due to a lack of fundamental process understanding. In this project, a multi-physics model was developed on a highly parallel open-source code, Truchas, with the ultimate goal of providing experimentally validated process maps for tailoring microstructure to achieve desired performance for LPBFAM. As a critical step towards fully LPBFAM modeling, modeling of single-track laser fusion (STLF) were conducted. Multi-physics simulations were conducted using Truchas for STLF by considering heat transfer, phase-change, fluid dynamics, surface tension phenomena, and evaporation. In order to assess the effect of the fluid dynamics on the solidification and ensuing microstructure, a heat transfer-and-solidification-only (HTS) model and a fully coupled heat-transfer-solidification and fluid-dynamics (HTS-FD) model were considered. In the HTS_FD model, the fluid flow was considered to be laminar while the molten alloy surface is assumed to be flat and non-deformable. This study is one the first attempt to understand the effect of the fluid flow on microstructure in STLF and LPBFAM. The results show that the fluid flow affects the solidification and ensuing microstructure in STLF. In order to validate Truchas for STLF and LPBFAM modeling, experimental data, which was obtained at GE Global Research (GEGR) for liquid pool shape and microstructure, were compared with those from numerical simulation results. For the keyhole regime case, numerical simulation results indicate that the melt-pool shape was typical to that of the conduction case with very large deviations from the measured melt-pool depths. Two sensitivity studies were conducted by varying the evaporation flux and the surface tension coefficient at fixed laser absorptivities of 0.5 and 0.3, respectively. The minimum value for the overall combined error, between the calculated values with the HTS_FD model and measured values for both the width and height of the melt-pool, was attained for a surface tension coefficient of -0.75e-4 N/m. An analytical solidification model for the columnar-to-equiaxed transition (CET) in rapid solidification was used to assess the microstructure variation within the melt-pool. From numerical simulation results, thermal gradient, G, and solidification velocity, V, were obtained in order to predict the microstructure type (e.g., dendritic, cellular). For the fluid-flow HTS_FD model, G was found to exhibit a maximum at the extremity of the solidified pool (i.e., at the free surface). By contrast, for HTS simulations, G was found to exhibit a maximum around the entire edge of the solidified pool. For the HTS_FD simulations, the minimum values of the cooling rate, GV, were found to be approximately half than their corresponding values for HTS simulations. By contrast with the min GV values, the maximum values for GV were found to occur for the HTS_FD simulations. Thus, HTS_FD simulations were found to exhibit a wider range of cooling rates than the HTS simulations. Concerning the solidification maps, the variation of the solidification velocity, V, as function of the thermal gradient, G, was obtained. It was found that the fluid flow model results (HTS_FD) exhibited an increased spread in the V(G) variation within the melt pool with respect to the HTS model results (without the fluid flow). A preliminary correlation model for the primary dendrite arm spacing (PDAS) based on a power law dependence on thermal gradient and solidification velocity was used to estimate PDAS from HTS and HTS_FD simulations. For low laser powers and low laser speeds, the PDAS obtained 6 with the fluid dynamics model (HTS_FD) was larger by more than 30% with respect to the PDAS calculated with the simple HTS model. In the second part of the project, fifty-seven simulations were conducted in order to obtain data on microstructure variables that can be used for process map development. In order to cover the entire processing space, thirty-three cases were selected STLF process simulations at six power levels and six laser scanning speeds. The 57 STLF process simulations were conducted as follows: 33 simulations with the heat-transfer-only (HTS) model and 24 simulations with the fluid flow model (HTS_FD). The solidification map data shows that for all simulations, a columnar dendritic microstructure would be expected. It was found that the minimum, average, and maximum thermal gradient exhibit exponential variations with respect to a process variable defined as the ratio between the power and square root of scan speed.

36 MATERIALS SCIENCE↗

NASA NTRS2002

Biogeography

As a field of study, biogeography may be considered a bricolage - it has been constructed from many different facets from an array of research disciplines including biology, botany, zoology, geography, and geology. Biogeography focuses on the study of the constantly changing ranges of plants and animals, over multitude of space and time scales. It also includes the study of the structure and dynamics of biotic communities and ecosystems as they relate to both natural and anthropogenic processes. As it exists today, biogeography is an interdisciplinary research area founded in both the biological and Earth sciences. From a purely biological perspective, biogeography may be perceived as one of two types of studies: 1. biotic distributions and broad scales, and interpretations of the evolutionary and dispersal history of a single taxon or a few taxa; or 2. biotic distributions at local-to-regional scales, and interpretations of these distributions in relation to contemporary environments and rates of immigration or extinction. The first type of study is what is most usually associated with the term "biogeography" as disciplinary research field. It is conventionally termed "classical biogeography" because it reflects the continuity of research foci on which biogeography was founded in the nineteenth-century. The second type of biogeographical study has more modern day roots and is termed "geographical ecology" to reflect the theoretical predilections of ecologists and population biologists. Geographical ecology for all intents has become merged with ecology and exists as a sub discipline within this larger field of study.

Quattrochi, Dale A.↗

NASA NTRS2020

Velocity, Temperature and Density Measurements in Supersonic Jets

Prediction of the flow field properties in supersonic jets using computational fluid dynamics (CFD) codes is challenging when there is a significant temperature difference between the jet core and the ambient air and/or compressibility effects are substantial. A benchmark set of flow field property data were obtained to assess current CFD capabilities and develop better modeling approaches for these turbulent flow fields where accurate calculation of turbulent heat flux is important. Three different convergent-divergent nozzles were investigated with exit Mach numbers of: 1.36, 1.63 and 2.0 at their perfectly expanded conditions. The conditions of the jet were set to obtain a temperature difference of zero between the jet core and the ambient air, and then the temperature difference was progressively increased, at the same jet Mach number. Particle Image Velocimetry (PIV), spontaneous rotational Raman scattering spectroscopy (SRS), Background Oriented Schlieren (BOS) and probe-based measurements were used to acquire high quality, spatially-resolved measurements of the mean and root mean square (rms) velocities as well as the mean and rms gas temperatures and densities in both hot and cold supersonic jet flows. The non-intrusive flow measurements are compared both against probe measurements and against standard Reynolds averaged Navier-Stokes (RANS) predictions of the supersonic jet flow properties.

Rotational Raman Spectroscopy↗

NASA NTRS2021

Velocity, Temperature and Density Measurements in Supersonic Jets

Raman Spectroscopy↗

DOE OSTI2015

Incorporating climate and regional setting into realistic-efficacious end states for contaminated sites - 15386

Data from former uranium milling and processing sites in the arid western United States (US) exemplify a conceptual model in which climate and geomorphology, and the associated geochemical and hydrological conditions, control the fate and transport of contaminants. In arid settings, shallow groundwater is transferred to the vadose zone and atmosphere via evaporation, transpiration and seepage. During these transfers, dissolved constituents accumulate in the vadose zone near the capillary fringe and around the roots of phreatophyte plants. In areas where the water table is shallow, minerals will also accumulate at the surface as a result of capillarity and evaporation. Two western-semiarid former milling sites, Tuba City AZ and Riverton WY, represent deep and shallow water table cases, respectively. At Tuba City, hydrological and geochemical factors limit the size of the groundwater plume and reduce the potential for contaminated water to crop at the locally significant Moenkopi Wash. At both sites, minerals that have formed in the groundwater and vadose zone in the near-field and mid-field areas will sustain elevated groundwater concentrations of anthropogenic contaminants, such as sulfate and uranium, for an extended time frame. The presence of secondary source mineral accumulations in the vadose zone in the mid-field area at Riverton is significant because episodic flooding occurs at this site. The contaminant distributions and plume dynamics observed at arid sites contrast with sites in the eastern US with stronger hydrologic driving forces and weaker evaporative processes. In both arid and non-arid settings, long term contaminant behaviors and concomitant exposure, risk and remediation time frames are controlled by regional conditions and climate and by local modifying factors. As a result, incorporating these conditions and factors into environmental management decisions provides new opportunities to develop more robust, sustainable and technically-defensible end states. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Selecting durable building envelope systems with machine learning assisted hygrothermal simulations database

Hygrothermal simulations provide insight into the energy performance and moisture durability of building envelope components under dynamic conditions. The inputs required for hygrothermal simulations are extensive, and carrying out simulations and analyses requires expert knowledge. An expert system, the Building Science Advisor (BSA), has been developed to predict the performance and select the energy-efficient and durable building envelope systems for different climates. The BSA consists of decision rules based on expert opinions and thousands of parametric simulation results for selected wall systems. The number of potential wall systems results in millions, too many to simulate all of them. We present how machine learning can help predict durability data, such as mold growth, while minimizing the number of simulations needed to run. The simulation results are used for training and validation of machine learning tools for predicting wall durability. We tested Artificial Neural Network (ANN) and Gradient Boosted Decision Trees (GBDT) for their applicability and model accuracy. Models developed with both methods showed adequate prediction performance (root mean square error of 0.195 and 0.209, respectively). Finally, we introduce how the information supports guidance for envelope design via an easy-to-use web-based tool that does not require the end-user to run hygrothermal simulations.

Salonvaara, Mikael↗

DOE OSTI2014

The moduli space of vacua of $ \mathcal{N}=2 $ class $ \mathcal{S} $ theories

We develop a systematic method to describe the moduli space of vacua of four dimensional N=2 class S theories including Coulomb branch, Higgs branch and mixed branches. In particular, we determine the Higgs and mixed branch roots, and the dimensions of the Coulomb and Higgs components of mixed branches. They are derived by using generalized Hitchin’s equations obtained from twisted compactification of 5d maximal Super-Yang-Mills, with local degrees of freedom at punctures given by (nilpotent) orbits. The crucial thing is the holomorphic factorization of the Seiberg-Witten curve and reduction of singularity at punctures. We illustrate our method by many examples including N=2 SQCD, T N theory and Argyres-Douglas theories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Differences in cluster and internal wake effects from mesoscale and large-eddy simulations off the US East Coast

Mesoscale simulations are increasingly used to estimate wake effects within and between large wind farms, despite limited validation for large-scale wake effects. This study evaluates the capabilities and limitations of mesoscale simulations in capturing wake-induced impacts on wind turbine power production through a direct comparison with large-domain large-eddy simulations (LESs) for three planned offshore wind farms under realistic atmospheric conditions and a range of atmospheric stabilities. We assess mesoscale performance in replicating wake characteristics behind single and multiple turbine clusters and quantify the resulting variability in mean turbine power. Results show that mesoscale Weather Research and Forecasting simulations with the Fitch wind farm parameterization capture key features of the velocity deficit downstream of both single and multiple wind farms, with mean root-mean-square errors near 5 % and good agreement with stability-driven wake behavior. However, in these simulations, the mesoscale Fitch parameterization underestimates power losses from internal wake effects, particularly when turbines align with the prevailing wind direction or under stable stratification. In these conditions, individual wakes persist and dominate downstream power deficits. The coarse resolution of the mesoscale simulations limits their ability to resolve individual wind turbine wakes that drive power fluctuations within wind farms. Nonetheless, mesoscale simulations can yield accurate estimates of combined wake losses from internal and cluster effects across some wind direction sectors, where errors in wake representation may cancel each other out. These findings underscore the strengths of mesoscale simulations for capturing broader wake patterns while highlighting their limitations for modeling turbine-level power losses. Future work should explore hybrid modeling approaches to capture both long-range cluster wake propagation and localized internal wake dynamics.

17 WIND ENERGY↗

NASA NTRS1999

Iterated Stretching of Viscoelastic Jets

We examine, with asymptotic analysis and numerical simulation, the iterated stretching dynamics of FENE and Oldroyd-B jets of initial radius r(sub 0), shear viscosity nu, Weissenberg number We, retardation number S, and capillary number Ca. The usual Rayleigh instability stretches the local uniaxial extensional flow region near a minimum in jet radius into a primary filament of radius [Ca(1 - S)/ We](sup 1/2)r(sub 0) between two beads. The strain-rate within the filament remains constant while its radius (elastic stress) decreases (increases) exponentially in time with a long elastic relaxation time 3We(r(sup 2, sub 0)/nu). Instabilities convected from the bead relieve the tension at the necks during this slow elastic drainage and trigger a filament recoil. Secondary filaments then form at the necks from the resulting stretching. This iterated stretching is predicted to occur successively to generate high-generation filaments of radius r(sub n), (r(sub n)/r(sub 0)) = square root of 2[r(sub n-1)/r(sub 0)](sup 3/2) until finite-extensibility effects set in.

Chang, Hsueh-Chia↗

DOE OSTI2020

Dynamic x-ray diffraction and nanosecond quantification of kinetics of formation of β -zirconium under shock compression

We report the atomic- and nanosecond-scale quantification of kinetics of a shock-driven phase transition in Zr metal. We uniquely make use of a multiple shock-and-release loading pathway to shock Zr into the β phase and to create a quasisteady pressure and temperature state shortly after. Coupling shock loading with in situ time-resolved synchrotron x-ray diffraction, we probe the structural transformation of Zr in the steady state. Our results provide a quantified expression of kinetics of formation of β-Zr phase under shock loading: transition incubation time, completion time, and crystallization rate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Mechanistic Insights into CO 2 Electroreduction on Ni 2 P: Understanding Its Selectivity toward Multicarbon Products

Recently, nickel phosphides (Ni x P y ) have been reported to enable selective electrochemical formation of multicarbon products (C 3 and C 4 ) via the CO 2 reduction reaction (CO 2 RR); nevertheless, their activities remain low. In order to understand the roots of their high selectivity and low activity and to direct the design of more active Ni x P y -based CO 2 RR catalysts, we investigate the CO 2 RR mechanism on Ni 2 P using density functional theory (DFT) calculations. Iin this work, we reveal that the reaction proceeds through the formate pathway, followed by formaldehyde (H 2 CO*) formation and self-condensation. Moreover, we demonstrate that surface hydride transfer steps, along with surface-mediated C–C coupling, are essential in order to avoid C 1 product formation and boost selectivity toward multicarbon products. In addition, we find that the thermal surface hydride transfer from the surface to the physisorbed CO 2 is one of the key rate-limiting steps, and since it is not electroactive, it cannot be accelerated by applying an overpotential. Finally, our results also show that the hydrogen affinity of the surface and the dynamic surface reconstruction via H adsorption facilitate selective CO 2 reduction and C–C coupling on Ni 2 P. These findings provide an impetus for exploring materials design space to identify the physical principles that govern the thermodynamics of rate-limiting thermal steps in electrocatalytic processes.

36 MATERIALS SCIENCE↗

NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗

NASA NTRS2017

High-Accuracy Tidal Flat Digital Elevation Model Construction Using TanDEM-X Science Phase Data

This study explored the feasibility of using TanDEM-X (TDX) interferometric observations of tidal flats for digital elevation model (DEM) construction. Our goal was to generate high-precision DEMs in tidal flat areas, because accurate intertidal zone data are essential for monitoring coastal environment sand erosion processes. To monitor dynamic coastal changes caused by waves, currents, and tides, very accurate DEMs with high spatial resolution are required. The bi- and monostatic modes of the TDX interferometer employed during the TDX science phase provided a great opportunity for highly accurate intertidal DEM construction using radar interferometry with no time lag (bistatic mode) or an approximately 10-s temporal baseline (monostatic mode) between the master and slave synthetic aperture radar image acquisitions. In this study, DEM construction in tidal flat areas was first optimized based on the TDX system parameters used in various TDX modes. We successfully generated intertidal zone DEMs with 57-m spatial resolutions and interferometric height accuracies better than 0.15 m for three representative tidal flats on the west coast of the Korean Peninsula. Finally, we validated these TDX DEMs against real-time kinematic-GPS measurements acquired in two tidal flat areas; the correlation coefficient was 0.97 with a root mean square error of 0.20 m.

Lee, Seung-Kuk↗