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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

NASA NTRS2022

Building Lunar Maps for Terrain Relative Navigation and Hazard Detection Applications

Terrain Relative Navigation (TRN) systems that localize a spacecraft with respect to a map of the surface by comparing descent imagery to that reference map can only be as accurate as the reference map itself. Accurate map products that are based on orbital reconnaissance data must be validated for navigation applications to ensure that all relevant error sources are minimized. Currently available map products have been generated for scientific applications, so the need for accurate TRN maps remains a gap to be filled for upcoming lunar lander missions, in particular missions to the South Pole region. Additionally, representative high-resolution maps that contain lander-scale features are needed for successful development and testing of Hazard Detection (HD) systems. This paper describes one of NASA’s current efforts to develop benchmark data sets that can be used for developing and testing TRN and HD algorithms as well as suggested processes and metrics for generating and validating lunar maps that can be used for navigation and hazard detection.

Beyer, Ross A.↗

NASA NTRSDate not supplied

Mapping Process Model Results to Fracture Model Initial Conditions for Adhesive Bonding

A systematic approach is proposed for mapping adhesive bonding process outcomes to initial conditions for progressive damage analysis (PDA). Herein, two mapping procedures are developed: 1) direct mapping for residual stresses, strains, and deformations obtained from a process model; and 2) functional mapping for quantities such as bondline thickness, degree of cure, and porosity that are assumed to have a functional relationship with fracture toughness but are not discretely modeled in the PDA. The spatial distribution of functionally mapped quantities may be determined by process modeling or inspection. The functional maps between the quantity of interest and fracture toughness may be obtained from independent lower-scale numerical simulations or empirical test campaigns. The proposed mapping procedure links the adhesive bonding process to the structural performance. Therefore, by accounting for the effects of bondline nonuniformities in structural analysis, analytical predictive accuracy can be improved, and off-nominal conditions can be evaluated. The mapping procedures are demonstrated and verified with example problems.

Andrew C Bergan↗

NASA NTRSDate not supplied

Mapping Process Model Results to Fracture Model Initial Conditions for Adhesive Bonding

Andrew Bergan↗

NASA NTRS2023

Capitol Reef Ecological Conservation: Mapping Vegetation Functional Groups to Inform Invasive Vegetation Management, Ecological Conservation and Restoration in Capitol Reef National Park

Invasive exotic plant (IEP) species have been found within the park boundaries of Capitol Reef National Park (CARE) in Utah. Currently, remotely sensed datasets such as the Rangeland Analysis Platform (RAP) from the United States Department of Agriculture (USDA) have been used to investigate IEP species within the park, but validation of the national RAP program is necessary for informing decisions at a local scale. CARE seeks a remote monitoring solution that can precisely target managerial efforts within the park’s challenging terrain and hard-to-reach locations. To fulfill this objective, we harnessed Landsat 8 Operational Land Imager (OLI) imagery and leveraged Random Forest (RF) modeling to generate classification maps characterizing vegetation functional groups for 2013 and 2022 within the park. Subsequently, the Land Change Modeler (LCM) in Idrisi TerrSet facilitated the production of a predicted classification map for 2033. The team also devised an annual grass probability map to accentuate areas impacted by exotic grasses. A comparative assessment between the RF classification map and the RAP map for 2022 revealed an overall agreement of 47.41%, with disparities primarily arising from differences in bare soil and shrub areas. Significantly, the 2022 RF-generated classification map showcased an impressive overall accuracy of 92.17%. In short, the probability map, the land cover change detection spanning 2013 to 2022, and the forecasting of observed trends into the future aids in the evaluation of invasive plant impacts and facilitation of CARE’s preparedness for potential ecological disturbances. Notably, in comparison to the RAP, the RF classification method generates functional group maps that are more representative of the study area.

Vanchy Li↗

DOE OSTI2014

Metrics for comparison of crystallographic maps

Numerical comparison of crystallographic contour maps is used extensively in structure solution and model refinement, analysis and validation. However, traditional metrics such as the map correlation coefficient (map CC, real-space CC or RSCC) sometimes contradict the results of visual assessment of the corresponding maps. This article explains such apparent contradictions and suggests new metrics and tools to compare crystallographic contour maps. The key to the new methods is rank scaling of the Fourier syntheses. The new metrics are complementary to the usual map CC and can be more helpful in map comparison, in particular when only some of their aspects, such as regions of high density, are of interest.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Datasets and U-Net Model for "A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma"

This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

Artificial Intelligence↗

DOE OSTI2022

Multi-Sensor Optimal Motion Planning for Radiological Contamination Surveys by Using Prediction-Difference Maps

Distributed and networked mobile sensor platforms using unmanned aerial and/or ground vehicles to survey areas of interest offer a safer and more efficient method for radiological contamination mapping; however, most applications rely on uniformly sweeping of the area in a raster-type motion without utilizing the information available in a dynamic sense. We have developed a fully autonomous optimal motion planning procedure for networks with two or more mobile sensors. The procedure utilizes well-established concepts of Gaussian processes in combination with control laws based on centroidal Voronoi tessellations to achieve optimal next-iteration sensor movements. A new method of informing optimal motion planning is proposed, whereby the absolute difference between the prior and current full-map prediction, referred to as the prediction-difference map, is used as the spatial density function within each Voronoi cell, providing immediate and iterative feedback for dynamic use of available information. The Gaussian process regression model used to estimate the contamination in unvisited locations also provides prediction uncertainties, and can be used as a quantitative metric to assess the confidence in the calculated contamination map; these estimates and prediction uncertainties are unavailable for standard uniform survey routines as they can only produce maps in the vicinity of observed locations. We present through simulation the achievable performance gains from using this new method by directly comparing to a uniform survey method. Results show that using the prediction-difference maps to inform motion planning procedures offers a faster rate of producing an accurate and convergent map relative to a uniform survey route.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

How to correct relative voxel scale factors for calculations of vector-difference Fourier maps in cryo-EM

The atomic coordinates derived from cryo-electron microscopy (cryo-EM) maps can be inaccurate when the voxel scaling factors are not properly calibrated. Here, we describe a method for correcting relative voxel scaling factors between pairs of cryo-EM maps for the same or similar structures that are expanded or contracted relative to each other. We find that the correction of scaling factors reduces the amplitude differences of Fourier-inverted structure factors from voxel-rescaled maps by up to 20–30%, as shown by two cryo-EM maps of the SARS-CoV-2 spike protein measured at pH 4.0 and pH 8.0. This allows for the calculation of the difference map after properly scaling, revealing differences between the two structures for individual amino acid residues. Unexpectedly, the analysis uncovers two previously overlooked differences of amino acid residues in structures and their local structural changes. Furthermore, we demonstrate the method as applied to two cryo-EM maps of monomeric apo-photosystem II from the cyanobacteria Synechocystis sp. PCC 6803 and Thermosynechococcus elongatus. The resulting difference maps reveal many changes in the peripheral transmembrane PsbX subunit between the two species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

A map of dust reddening to 4.5 kpc from Pan-STARRS1

We present a map of the dust reddening to 4.5 kpc derived from Pan-STARRS1 stellar photometry. The map covers almost the entire sky north of declination –30° at a resolution of 7'-14', and is based on the estimated distances and reddenings to more than 500 million stars. The technique is designed to map dust in the Galactic plane, where many other techniques are stymied by the presence of multiple dust clouds at different distances along each line of sight. This reddening-based dust map agrees closely with the Schlegel et al. (SFD) far-infrared emission-based dust map away from the Galactic plane, and the most prominent differences between the two maps stem from known limitations of SFD in the plane. We also compare the map with Planck, finding likewise good agreement in general at high latitudes. The use of optical data from Pan-STARRS1 yields reddening uncertainty as low as 25 mmag E(B – V).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

SU-G-IeP1-12: Size Selective Arterial Cerebral Blood Volume Mapping Using Multiple Inversion Time Arterial Spin Labeling

Purpose: To demonstrate the feasibility of a novel method for size specific arterial cerebral blood volume (aCBV) mapping using pseudo-continuous arterial spin labeling (PCASL), with multiple TI. Methods: Multiple PCASL images were obtained from a subject with TI of [300, 400, 500, 600, 700, 800, 900, 1000, 1500, 2000, 2500, 3000, 3500, 4000] ms. Each TI pair was averaged six times. Two scans were performed: one without a flow crusher gradient and the other with a crusher gradient (10cm/s in three directions) to remove signals from large arteries. Scan times were 5min. without a crusher gradient and 5.5 min with a crusher gradient. Non-linear fitting algorithm finds the minimum mean squared solution of per-voxel based aCBV, cerebral blood flow, and arterial transit time, and fits the data into a hemodynamic model that represents superposition of blood volume and flow components within a single voxel. Results: aCBV maps with a crusher gradient represent signals from medium and small sized arteries, while those without a crusher gradient represent signals from all sized arteries, indicating that flow crusher gradients can be effectively employed to achieve size-specific aCBV mapping. Regardless of flow crusher, the CBF and ATT maps are very similar in appearance. Conclusion: Quantitative size selective blood volume mapping controlled by a flow crusher is feasible without additional information because the ASL quantification process doesn’t require an arterial input function measured from a large artery. The size specific blood volume mapping is not interfered by sSignals from large arteries do not interfere with size specific aCBV mapping in the applications of interest in for applications in which only medium or small arteries are of interest.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-AB-202-12: A Novel Method to Map Endoscopic Video to CT for Treatment Planning and Toxicity Analysis in Radiation Therapy

Purpose: Endoscopic examinations are routine procedures for head-and-neck cancer patients. Our goal is to develop a method to map the recorded video to CT, providing valuable information for radiotherapy treatment planning and toxicity analysis. Methods: We map video frames to CT via virtual endoscopic images rendered at the real endoscope’s CT-space coordinates. We developed two complementary methods to find these coordinates by maximizing real-to-virtual image similarity:(1)Endoscope Tracking: moves the virtual endoscope frame-by-frame until the desired frame is reached. Utilizes prior knowledge of endoscope coordinates, but sensitive to local optima. (2)Location Search: moves the virtual endoscope along possible paths through the volume to find the desired frame. More robust, but more computationally expensive. We tested these methods on clay phantoms with embedded markers for point mapping and protruding bolus material for contour mapping, and we assessed them qualitatively on three patient exams. For mapped points we calculated 3D-distance errors, and for mapped contours we calculated mean absolute distances (MAD) from CT contours. Results: In phantoms, Endoscope Tracking had average point error=0.66±0.50cm and average bolus MAD=0.74±0.37cm for the first 80% of each video. After that the virtual endoscope got lost, increasing these values to 4.73±1.69cm and 4.06±0.30cm. Location Search had point error=0.49±0.44cm and MAD=0.53±0.28cm. Point errors were larger where the endoscope viewed the surface at shallow angles<10 degrees (1.38±0.62cm and 1.22±0.69cm for Endoscope Tracking and Location Search, respectively). In patients, Endoscope Tracking did not make it past the nasal cavity. However, Location Search found coordinates near the correct location for 70% of test frames. Its performance was best near the epiglottis and in the nasal cavity. Conclusion: Location Search is a robust and accurate technique to map endoscopic video to CT. Endoscope Tracking is sensitive to erratic camera motion and local optima, but could be used in conjunction with anchor points found using Location Search.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Construction of high resolution genetic linkage maps to improve the soybean genome sequence assembly Glyma1.01

Background. A landmark in soybean research, Glyma1.01, the first whole genome sequence of variety Williams 82 (Glycine max L. Merr.) was completed in 2010 and is widely used. However, because the assembly was primarily built based on the linkage maps constructed with a limited number of markers and recombinant inbred lines (RILs), the assembled sequence, especially in some genomic regions with sparse numbers of anchoring markers, needs to be improved. Molecular markers are being used by researchers in the soybean community, however, with the updating of the Glyma1.01 build based on the high-resolution linkage maps resulting from this research, the genome positions of these markers need to be mapped. Results. Two high density genetic linkage maps were constructed based on 21,478 single nucleotide polymorphism loci mapped in the Williams 82 x G. soja (Sieb. & Zucc.) PI479752 population with 1083 RILs and 11,922 loci mapped in the Essex x Williams 82 population with 922 RILs. There were 37 regions or single markers where marker order in the two populations was in agreement but was not consistent with the physical position in the Glyma1.01 build. In addition, 28 previously unanchored scaffolds were positioned. Map data were used to identify false joins in the Glyma1.01 assembly and the corresponding scaffolds were broken and reassembled to the new assembly, Wm82.a2.v1. Based upon the plots of the genetic on physical distance of the loci, the euchromatic and heterochromatic regions along each chromosome in the new assembly were delimited. Genomic positions of the commonly used markers contained in BARCSOYSSR_1.0 database and the SoySNP50K BeadChip were updated based upon the Wm82.a2.v1 assembly. Conclusions. The information will facilitate the study of recombination hot spots in the soybean genome, identification of genes or quantitative trait loci controlling yield, seed quality and resistance to biotic or abiotic stresses as well as other genetic or genomic research.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Cost-Effective Mapping of Benthic Habitats in Inland Reservoirs through Split-Beam Sonar, Indicator Kriging, and Historical Geologic Data

Because bottom substrate composition is an important control on the temporal and spatial location of the aquatic community, accurate maps of benthic habitats of inland lakes and reservoirs provide valuable information to managers, recreational users, and scientists. Therefore, we collected vertical, split-beam sonar data (roughness [E1], hardness [E2], and bathymetry) and sediment samples to make such maps. Statistical calibration between sonar parameters and sediment classes was problematic because the E1:E2 ratios for soft (muck and clay) sediments overlapped a lower and narrower range for hard (gravel) substrates. Thus, we used indicator kriging (IK) to map the probability that unsampled locations did not contain coarse sediments. To overcome the calibration issue we tested proxies for the natural processes and anthropogenic history of the reservoir as potential predictive variables. Of these, a geologic map proved to be the most useful. The central alluvial valley and mudflats contained mainly muck and organic-rich clays. The surrounding glacial till and shale bedrock uplands contained mainly poorly sorted gravels. Anomalies in the sonar data suggested that the organic-rich sediments also contained trapped gases, presenting additional interpretive issues for the mapping. We extended the capability of inexpensive split-beam sonar units through the incorporation of historic geologic maps and other records as well as validation with dredge samples. Through the integration of information from multiple data sets, were able to objectively identify bottom substrate and provide reservoir users with an accurate map of available benthic habitat.

bottom sediment, fisheries, geostatistics, GIS, ha↗

DOE OSTI2015

Scanning the landscape of genome architecture of non-O1 and non-O139 Vibrio cholerae by whole genome mapping reveals extensive population genetic diversity

Historically, cholera outbreaks have been linked to V. cholerae O1 serogroup strains or its derivatives of the O37 and O139 serogroups. A genomic study on the 2010 Haiti cholera outbreak strains highlighted the putative role of non O1/non-O139 V. cholerae in causing cholera and the lack of genomic sequences of such strains from around the world. Here we address these gaps by scanning a global collection of V. cholerae strains as a first step towards understanding the population genetic diversity and epidemic potential of non O1/non-O139 strains. Whole Genome Mapping (Optical Mapping) based bar coding produces a high resolution, ordered restriction map, depicting a complete view of the unique chromosomal architecture of an organism. To assess the genomic diversity of non-O1/non-O139 V. cholerae , we applied a Whole Genome Mapping strategy on a well-defined and geographically and temporally diverse strain collection, the Sakazaki serogroup type strains. Whole Genome Map data on 91 of the 206 serogroup type strains support the hypothesis that V. cholerae has an unprecedented genetic and genomic structural diversity. Interestingly, we discovered chromosomal fusions in two unusual strains that possess a single chromosome instead of the two chromosomes usually found in V. cholerae . We also found pervasive chromosomal rearrangements such as duplications and indels in many strains. The majority of Vibrio genome sequences currently in public databases are unfinished draft sequences. The Whole Genome Mapping approach presented here enables rapid screening of large strain collections to capture genomic complexities that would not have been otherwise revealed by unfinished draft genome sequencing and thus aids in assembling and finishing draft sequences of complex genomes. Furthermore, Whole Genome Mapping allows for prediction of novel V. cholerae non-O1/non-O139 strains that may have the potential to cause future cholera outbreaks.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2005

Mapping Weathering and Alteration Minerals in the Comstock and Geiger Grade Areas using Visible to Thermal Infrared Airborne Remote Sensing Data

To support research into both precious metal exploration and environmental site characterization a combination of high spatial/spectral resolution airborne visible, near infrared, short wave infrared (VNIR/SWIR) and thermal infrared (TIR) image data were acquired to remotely map hydrothermal alteration minerals around the Geiger Grade and Comstock alteration regions, and map the mineral by-products of weathered mine dumps in Virginia City. Remote sensing data from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS), SpecTIR Corporation's airborne hyperspectral imager (HyperSpecTIR), the MODIS-ASTER airborne simulator (MASTER), and the Spatially Enhanced Broadband Array Spectrograph System (SEBASS) were acquired and processed into mineral maps based on the unique spectral signatures of image pixels. VNIR/SWIR and TIR field spectrometer data were collected for both calibration and validation of the remote data sets, and field sampling, laboratory spectral analyses and XRD analyses were made to corroborate the surface mineralogy identified by spectroscopy. The resulting mineral maps show the spatial distribution of several important alteration minerals around each study area including alunite, quartz, pyrophyllite, kaolinite, montmorillonite/muscovite, and chlorite. In the Comstock region the mineral maps show acid-sulfate alteration, widespread propylitic alteration and extensive faulting that offsets the acid-sulfate areas, in contrast to the larger, dominantly acid-sulfate alteration exposed along Geiger Grade. Also, different mineral zones within the intense acid-sulfate areas were mapped. In the Virginia City historic mining district the important weathering minerals mapped include hematite, goethite, jarosite and hydrous sulfate minerals (hexahydrite, alunogen and gypsum) located on mine dumps. Sulfate minerals indicate acidic water forming in the mine dump environment. While there is not an immediate threat to the community, there are clearly sources of acidic drainage that were identified remotely.

imaging spectroscopy↗

NASA NTRSDate not supplied

Lunar Science Investigations and Exploration in Celestial Mapping System

Introduction: As NASA expands the mission portfolio on the lunar surface, there is a need for applications with a broad range of analytical and functional capabilities that can be simultaneously deployed onto multiple mobile and desktop platforms to perform in-situ operations and hence enabling extensive Lunar exploration. Celestial Mapping System (CMS) [1,2] is developed to address the need for tools for science investigations, mission planning, operations and support for planetary sciences. Built on top of NASA WorldWind libraries, CMS can be simultaneously deployed onto multiple platforms, has the flexibility to update to the latest imagery and terrain datasets as they are being acquired (in real time) before and/or during the exploration mission and has the potential to enable traverse path planning suited for rovers, EVA and surface mobility units. It can provide critical functionalities such as equipment planning and optimized placement on Lunar surface, line of sight analysis to inform the coverage area for various equipment, powerful measurement tools based on 3D terrain, 3D COLLADA models to represent rovers, humans and equipment, visualization of derived mapping products (e.g. resource maps), and a data engine for hosting new observations that are not available in other contemporary lunar data tools [1]. Visualization of PSRs: The current presentation focuses on the work performed by the authors, to consume a unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [3]. This tool was developed in direct support of NASA's VIPER and Artemis programs to enhance the extremely low-light images of the interior of PSRs and provide the first-time ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the region near Nobili crater near Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Line of Sight Analysis and Traverse Planning in PSRs: We have developed an in-built line of sight analysis (LOS) tool in CMS that analyzes the terrain profile and obstructions and provides the visibility of a given terrain for a remote observer. This tool was utilized to perform viewshed analysis to investigate the area inside the PSR, a remote observer such as a rover could see without actually crossing the region. Figure 1 shows the viewshed analysis on the PSR in Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. Figure 1: (left) PSR image on top of a high-resolution mosaic of sunlit images of a crater, in Nobile region (right) Viewshed Analysis of the same PSR with observer location shown by yellow pin. This analysis was extended further to set different heights for various observers and then perform the viewshed analysis. Combining the different visibility profiles can help designing improved traverses within the crater. Future plans: Eventually, HORUS datasets will be integrated into CMS as a layer in selected lunar polar regions. Hazard Maps will then be created based on terrain analysis in those regions. This integration will enable multiple scientific and exploration applications, such as designing traverses within PSRs, analyzing potential landing and science mission targets, investigating the meter-scale geomorphology of PSRs, including craters, boulder, surface roughness, mass wasting features and other indications of the presence of water-ice and other volatiles. Acknowledgments: CMS developers team including Kaitlyn J. Dickinson, Tyler A. Lucarz, Tyler W. Choi from USRA, NASA WorldWind Advisory team including Mark Peterson and Guillermo Miguel Del Castillo, HORUS team member V.T. Bickel, Robinson, M., LRO MOON LROC 2 EDR V1.0, LRO-L-LROC-2-EDR-V1.0, NASA Planetary Data System (PDS), 2009. https://doi.org/10.17189/1520643 References: [1] https://celestial.arc.nasa.gov [2] Agrawal et. al. “Celestial Mapping System for Lunar Surface Mapping and Analytics”, Lunar Surface Innovation Consortium, 2021 [3] Bickel V. et al. (2021) Nat Commun 12, 5607

Lunar Mapping↗

DOE OSTI2017

A transcriptome-SNP-derived linkage map of Apios americana (potato bean) provides insights about genome re-organization and synteny conservation in the phaseoloid legumes

Apios ( Apios americana ; “apios”), a tuberous perennial legume in the Phaseoleae tribe, was widely used as a food by Native Americans. Work in the last 40 years has led to several improved breeding lines. Aspects of the pollination biology (complex floral structure and tripping mechanism) have made controlled crosses difficult, and the previous reports indicated that the plant is likely primarily an outcrosser. We used a pseudo-testcross strategy to construct a genetic map specific to the maternal parent. The map was built using single-nucleotide polymorphism markers identified by comparing the expressed sequences of individuals in the mapping population against a de novo maternal reference transcriptome assembly. The apios map consists of 11 linkage groups and 1121 recombinationally distinct loci, covering ~ 938.6 cM. By sequencing the transcriptomes of all potential pollen parents, we were able to identify the probable pollen donors and to discover new aspects of the pollination biology in apios. No selfing was observed, but multiple pollen parents were seen within individual pods. Comparisons with genome sequences in other species in the Phaseoleae showed extended synteny for most apios linkage groups. This synteny supports the robustness of the map, and also sheds light on the history of the Phaseoleae, as apios is relatively early diverging in this tribe. We detected a translocation event that separates apios and two Vigna species from Phaseolus vulgaris and Glycine max. This apios mapping work provides a general protocol for sequencing-based construction of high-density linkage maps in outcrossing species with heterogeneous pollen parents.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Integrating very-high-resolution UAS data and airborne imaging spectroscopy to map the fractional composition of Arctic plant functional types in Western Alaska

Widespread changes in vegetation cover and composition are driving strong impacts on Arctic ecosystem functioning and global climate feedbacks. An accurate characterization of tundra vegetation composition is required to understand how the Arctic will respond to future climate change. However, quantifying tundra vegetation composition over large areas is challenging as commonly-used satellite observations are too coarse, spatially and spectrally, to differentiate low-lying tundra vegetation types. Recent airborne and spaceborne imaging spectroscopy platforms provide better data to characterize vegetation composition. Yet, our ability to characterize vegetation composition with imaging spectroscopy remains largely unexplored in the Arctic, particularly due to a lack of ground observations needed to train and test classification models. To address this problem, we collected very-high-resolution (VHR, ~5 cm) unoccupied aerial system (UAS) imagery at three low-Arctic tundra sites located on the Seward Peninsula, western Alaska. In this paper, we examine the feasibility of integrating imagery from the UAS and the hyperspectral Airborne Visible/Infrared Imaging Spectrometer, Next Generation (AVIRIS-NG) airborne instrument to map the fractional composition of 12 key Arctic plant functional types (PFTs). To this end, we first mapped the 12 PFTs from our VHR UAS imagery using random forest classification. We then used these UAS-derived PFT maps as ground truth to develop partial least squares regression (PLSR) models to predict the fractional cover (FCover) of each PFT from AVIRIS-NG imagery. Further, we evaluated the performance of our PLSR models using reserved UAS samples, as well as by mapping PFT FCover and dominant PFT for large tundra landscapes. Our results show that 1) Arctic PFTs can be effectively mapped using VHR UAS imagery, with overall accuracy between 86% and 92%, 2) when the UAS mapped PFTs were used to inform PLSR scaling models, the FCover of the 12 PFTs could be effectively estimated from AVIRIS-NG imagery with a mean absolute error (MAE) <0.13, and 3) our PLSR models outperformed traditional, fully constrained least-squares (FCLS) linear mixture analysis and produced high-quality, spatially contiguous PFT FCover and PFT maps that captured vegetation spatial patterns with similar accuracy to those developed from UAS imagery. The developed PLSR models have the potential to be broadly applied for quantifying vegetation composition with AVIRIS-NG images to help monitor tundra vegetation dynamics and improve process-based modeling of tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗