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NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
DOE OSTI2024

CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

A Deep, High-angular-resolution 3D Dust Map of the Southern Galactic Plane

We present a deep, high-angular-resolution 3D dust map of the southern Galactic plane over 239° < l < 6° and ∣ b∣ < 10° built on photometry from the DECaPS2 survey, in combination with photometry from VISTA Variables in the Via Lactea, the Two Micron All Sky Survey, and “Unofficial” Wide-field Infrared Survey Explorer and parallaxes from Gaia Data Release 3 where available. To construct the map, we first infer the distance, extinction, and stellar types of over 700 million stars using the brutus stellar inference framework with a set of theoretical MESA Isochrone and Stellar Tracks (MIST) stellar models. Our resultant 3D dust map has an angular resolution of 1′ , roughly an order of magnitude finer than existing 3D dust maps and comparable to the angular resolution of the Herschel 2D dust emission maps. We detect complexes at the range of distances associated with the Sagittarius-Carina and Scutum-Centaurus arms in the fourth quadrant, as well as more distant structures out to a maximum reliable distance of d ≈ 10 kpc from the Sun. The map is sensitive up to a maximum extinction of roughly A V ≈ 12 mag. We publicly release both the stellar catalog and the 3D dust map, the latter of which can easily be queried via the Python package dustmaps. When combined with the existing Bayestar19 3D dust map of the northern sky, the DECaPS 3D dust map fills in the missing piece of the Galactic plane, enabling extinction corrections over the entire disk ∣b∣ < 10°. Our map serves as a pathfinder for the future of 3D dust mapping in the era of LSST and Roman, targeting regimes accessible with deep optical and near-infrared photometry but often inaccessible with Gaia.

Milky Way galaxy↗
NASA NTRS1991

A new catalog of planetary maps

A single, concise reference to all existing planetary maps, including lunar ones, is being prepared that will allow map users to identify and locate maps of their areas of interest. This will be the first such comprehensive listing of planetary maps. Although the USGS shows index maps on the collar of each map sheet, periodically publishes index maps of Mars, and provides informal listings of the USGS map database, no tabulation exists that identifies all planetary maps, including those published by DMA and other organizations. The catalog will consist of a booklet containing small-scale image maps with superimposed quadrangle boundaries and map data tabulations.

Batson, R. M.↗
NASA NTRS1994

Formal results regarding metric space techniques for the study of astrophysical maps

We extend a newly developed formal system for the description of astrophyscial maps. In this formalism, we consider the difference between maps to be the distance between elements of a pseudometric space (the space of all such maps). This Ansatz allows us to measure quantitatively the difference between any two maps and to order the space of all maps. For each physical characteristic of interest, this technique assigns an 'output' function to each map; the difference between the maps is then determined from the difference between their corresponding output functions. In this present study, we show that the results of this procedure are invariant under a class of transformations of the maps and the domains of the maps. In addition, we study the propagation of errors (observational uncertainties) through this formalism. We show that the uncertainties in the output functions can be controlled provided that the signal to noise ratios in the original astrophysical maps are sufficiently high. The results of this paper thus increase the effectiveness of this formal system for the description, classification, and analysis of astrophysical maps.

Adams, Fred C.↗
NASA NTRS2013

Geologic Interpretation of Data Sets Collected by Planetary Analog Geology Traverses and by Standard Geologic Field Mapping: A Comparison Study - Part 1

Geologic maps integrate the distributions, contacts, and compositions of rock and sediment bodies as a means to interpret local to regional formative histories. Applying terrestrial mapping techniques to other planets is challenging because data is collected primarily by orbiting instruments, with infrequent, spatiallylimited in situ human and robotic exploration. Although geologic maps developed using remote data sets and limited "Apollo-style" field access likely contain inaccuracies, the magnitude, type, and occurrence of these are only marginally understood. This project evaluates the interpretative and cartographic accuracy of both field- and remote-based mapping approaches by comparing two 1:24,000 scale geologic maps of the San Francisco Volcanic Field (SFVF), north-central Arizona. The first map is based on traditional field mapping techniques, while the second is based on remote data sets, augmented with limited field observations collected during NASA Desert Research & Technology Studies (RATS) 2010 exercises. The RATS mission used Apollo-style methods not only for pre-mission traverse planning but also to conduct geologic sampling as part of science operation tests. Cross-comparison demonstrates that the Apollo-style map identifies many of the same rock units and determines a similar broad history as the field-based map. However, field mapping techniques allow markedly improved discrimination of map units, particularly unconsolidated surficial deposits, and recognize a more complex eruptive history than was possible using Apollo-style data. Further, the distribution of unconsolidated surface units was more obvious in the remote sensing data to the field team after conducting the fieldwork. The study raises questions about the most effective approach to balancing mission costs with the rate of knowledge capture, suggesting that there is an inflection point in the "knowledge capture curve" beyond which additional resource investment yields progressively smaller gains in geologic knowledge.

Eppler, Dean B.↗
NASA NTRS2014

Comparing and Reconciling Traditional Field and Photogeologic Mapping Techniques: Lessons from the San Francisco Volcanic Field, Arizona

Cartographic products and - specifically - geologic maps provide critical assistance for establishing physical and temporal frameworks of planetary surfaces. The technical methods that result in the creation of geologic maps vary depending on how observations are made as well as the overall intent of the final products [1-3]. These methods tend to follow a common linear work flow, including the identification and delineation of spatially and temporally discrete materials (units), the documentation of their primary (emplacement) and secondary (erosional) characteristics, analysis of the relative and absolute age relationships between these materials, and the collation of observations and interpretations into an objective map product. The "objectivity" of a map is critical cross comparison with overlapping maps and topical studies as well as its relevance to scientific posterity. However, the "accuracy" and "correctness" of a geologic map is very subject to debate. This can be evidenced by comparison of existing geologic maps at various scales, particularly those compiled through field- and remote-based mapped efforts. Our study focuses on comparing the fidelity of (1) "Apollo-style" geologic investigations, where typically non-geologist crew members follow static traverse routes established through pre-mission planning, and (2) "traditional" field-based investigations, where geologists are given free rein to observe without preplanned routes. This abstract summarizes the regional geology wherein our study was conducted, presents the geologic map created from traditional field mapping techniques, and offers basic insights into how geologic maps created from different tactics can be reconciled in support of exploratory missions. Additional abstracts [4-6] from this study discuss various exploration and science results of these efforts.

Skinner, J. A., Jr.↗
NASA NTRS2019

Heat Analysis Manager (HAM), a Thermal Desktop API Based Heat Map Generation Software

Thermal engineers often create custom heat maps to analyze their thermal model. However, generating a heat map is difficult because thermal simulation only readily provide attributes of simulated nodes such as temperature, capacitance, heat generation, and a network of conductances. Heat flow values are a derived quantity from the nodal attributes, and the data processing and management of heat flow between nodes quickly become difficult for large models. Deriving a network of heat flow values requires vast amount of calculations and data handling, heat map generation process generally suffers from processing speed, loss of accuracy, and/or presentation of data in a useful format. Heat Analysis Manager (HAM) is a Thermal Desktop (TD) based free multi-purpose tool developed to aid thermal engineers in analyzing their thermal model, including a heat map generation functionality. HAM’s heat map generator retains accuracy and fast processing speed by utilizing TD’s application programming interface (API) and built-in TD’s “Qflow from Results.” Furthermore, HAM’s heat map output is presented in an easily customizable format in Excel, allowing users to create various custom visual heat maps. A full description of how HAM utilizes TD’s API to create a customizable heat map is provided. A simple model demonstration is included along with step-by-step procedures on creating custom heat maps. HAM’s heat map result has been verified against TD’s and other heat map generation software, and verification methods are also included.

Thermal desktop↗
NASA NTRS2021

LiDAR-Based Map Relative Localization Performance Analysis for Landing on Europa

This paper presents preliminary simulations andanalyses done to assess the feasibility of performing Map RelativeLocalization (MRL) with the Europa Lander LiDAR beingdeveloped for the Europa Lander Pre-Phase A concept. MapRelative Localization is the process of determining the horizontalposition of a lander with respect to an onboard, a-priori map,by comparing the map to sensor observations of the terrain duringdeorbit, descent, and landing (DDL). Although kilometerscaleposition knowledge is commonly available during DDL,landing in hazard-rich environments requires position errors of100 m or less. Prior knowledge in the case of Europa Landerwill be visual and topographic maps collected by the upcomingEuropa Clipper mission. The Mars 2020 Lander Vision System(LVS) uses images from a camera to localize with respect tovisual maps. This technology, as well as a 3D imaging LiDAR indevelopment for hazard detection, is currently baselined for theEuropa Lander Pre-Phase A concept. This paper investigatesthe potential use of the hazard detection LiDAR to performMRL with respect to a 3D digital elevation model (DEM)provided by the Europa Clipper mission, as an alternative orbackup solution to passive optical MRL. Compared to passiveoptical MRL, one advantage of LiDAR-based localization isthat it is insensitive to lighting conditions, potentially relaxingrequirements on synchronizing map acquisition and landingtime of day. To analyze LiDAR based MRL performance,six representative terrains are synthetically up-sampled fromGalileo-derived maps of Europa to a resolution of 0.5 m/pxand covering an area of 4 km by 4 km. These maps are usedas ground-truth to generate simulated noisy a-priori onboardtopographic maps expected from Europa Clipper as well assimulated LiDAR DEMs generated at an altitude of 5 km duringEuropa Lander DDL. The simulated LiDAR DEM is matchedagainst the simulated map via 2D normalized cross-correlation,exploiting the accurately known spacecraft attitude to avoidthe need for more computationally intensive algorithms such asIterative Closest Point (ICP). Two sources of measurement errorare identified for analysis: 1) additive Gaussian noise in therange measurements from the Europa Lander LiDAR and theEuropa Clipper derived maps and 2) errors in the LiDAR DEMinduced by errors in the Europa Lander state estimate which isused to de-warp the LiDAR scan data into a DEM format. Weassess the effect of each of these types of errors independently onmatching performance as well as the overall performance whenall types of error are introduced. Additionally, we present theresult of a sensitivity study to terrain frequency content.

Trawny, Nikolas↗
NASA NTRS2014

Geologic Mapping of Vesta

We report on a preliminary global geologic map of Vesta, based on data from the Dawn spacecraft's High- Altitude Mapping Orbit (HAMO) and informed by Low-Altitude Mapping Orbit (LAMO) data. This map is part of an iterative mapping effort; the geologic map has been refined with each improvement in resolution. Vesta has a heavily-cratered surface, with large craters evident in numerous locations. The south pole is dominated by an impact structure identified before Dawn's arrival. Two large impact structures have been resolved: the younger, larger Rheasilvia structure, and the older, more degraded Veneneia structure. The surface is also characterized by a system of deep, globe-girdling equatorial troughs and ridges, as well as an older system of troughs and ridges to the north. Troughs and ridges are also evident cutting across, and spiraling arcuately from, the Rheasilvia central mound. However, no volcanic features have been unequivocally identified. Vesta can be divided very broadly into three terrains: heavily-cratered terrain; ridge-and-trough terrain (equatorial and northern); and terrain associated with the Rheasilvia crater. Localized features include bright and dark material and ejecta (some defined specifically by color); lobate deposits; and mass-wasting materials. No obvious volcanic features are evident. Stratigraphy of Vesta's geologic units suggests a history in which formation of a primary crust was followed by the formation of impact craters, including Veneneia and the associated Saturnalia Fossae unit. Formation of Rheasilvia followed, along with associated structural deformation that shaped the Divalia Fossae ridge-and-trough unit at the equator. Subsequent impacts and mass wasting events subdued impact craters, rims and portions of ridge-and-trough sets, and formed slumps and landslides, especially within crater floors and along crater rims and scarps. Subsequent to the formation of Rheasilvia, discontinuous low-albedo deposits formed or were emplaced; these lie stratigraphically above the equatorial ridges that likely were formed by Rheasilvia. The last features to be formed were craters with bright rays and other surface mantling deposits. Executed progressively throughout data acquisition, the iterative mapping process provided the team with geologic proto-units in a timely manner. However, interpretation of the resulting map was hampered by the necessity to provide the team with a standard nomenclature and symbology early in the process. With regard to mapping and interpreting units, the mapping process was hindered by the lack of calibrated mineralogic information. Topography and shadow played an important role in discriminating features and terrains, especially in the early stages of data acquisition.

Vesta↗
NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗
NASA NTRS1973

Applicability of ERTS-1 to lineament and photogeologic mapping in Montana: Preliminary report

A lineament map prepared from a mosaic of western Montana shows about 85 lines not represented on the state geologic map, including elements of a northeast-trending set through central western Montana which merit ground truth checking and consideration in regional structural analysis. Experimental fold annotation resulted in a significant local correction to the state geologic map. Photogeologic mapping studies produced only limited success in identification of rock types, but they did result in the precise delineation of a late Cretaceous or early Tertiary volcanic field (Adel Mountain field) and the mapping of a connection between two granitic bodies shown on the state map. Imagery was used successfully to map clay pans associated with bentonite beds in gently dipping Bearpaw Shale. It is already apparent that ERTS imagery should be used to facilitate preparation of a much needed statewide tectonic map and that satellite imagery mapping, aided by ground calibration, provides and economical means to discover and correct errors in the state geologic map.

Weidman, R. M.↗
NASA NTRS1992

Mapping unstructured grid computations to massively parallel computers

Investigated here is this mapping problem: assign the tasks of a parallel program to the processors of a parallel computer such that the execution time is minimized. First, a taxonomy of objective functions and heuristics used to solve the mapping problem is presented. Next, we develop a highly parallel heuristic mapping algorithm, called Cyclic Pairwise Exchange (CPE), and discuss its place in the taxonomy. CPE uses local pairwise exchanges of processor assignments to iteratively improve an initial mapping. A variety of initial mapping schemes are tested and recursive spectral bipartitioning (RSB) followed by CPE is shown to result in the best mappings. For the test cases studied here, problems arising in computational fluid dynamics and structural mechanics on unstructured triangular and tetrahedral meshes, RSB and CPE outperform methods based on simulated annealing. Much less time is required to do the mapping and the results obtained are better. Compared with random and naive mappings, RSB and CPE reduce the communication time two fold for the test problems used. Finally, we use CPE in two applications on a CM-2. The first application is a data parallel mesh-vertex upwind finite volume scheme for solving the Euler equations on 2-D triangular unstructured meshes. CPE is used to map grid points to processors. The performance of this code is compared with a similar code on a Cray-YMP and an Intel iPSC/860. The second application is parallel sparse matrix-vector multiplication used in the iterative solution of large sparse linear systems of equations. We map rows of the matrix to processors and use an inner-product based matrix-vector multiplication. We demonstrate that this method is an order of magnitude faster than methods based on scan operations for our test cases.

Hammond, Steven Warren↗
NASA NTRS2002

The Maneuver Planning Process for the Microwave Anisotropy Probe (MAP) Mission

The Microwave Anisotropy Probe (MAP) was successfully launched from Kennedy Space Center's Eastern Range on June 30, 2001. MAP will measure the cosmic microwave background as a follow up to NASA's Cosmic Background Explorer (COBE) mission from the early 1990's. MAP will take advantage of its mission orbit about the Sun-Earth/Moon L2 Lagrangian point to produce results with higher resolution, sensitivity, and accuracy than COBE. A strategy comprising highly eccentric phasing loops with a lunar gravity assist was utilized to provide a zero-cost insertion into a lissajous orbit about L2. Maneuvers were executed at the phasing loop perigees to correct for launch vehicle errors and to target the lunar gravity assist so that a suitable orbit at L2 was achieved. This paper will discuss the maneuver planning process for designing, verifying, and executing MAP's maneuvers. A discussion of the tools and how they interacted will also be included. The maneuver planning process was iterative and crossed several disciplines, including trajectory design, attitude control, propulsion, power, thermal, communications, and ground planning. Several commercial, off-the-shelf (COTS) packages were used to design the maneuvers. STK/Astrogator was used as the trajectory design tool. All maneuvers were designed in Astrogator to ensure that the Moon was met at the correct time and orientation to provide the energy needed to achieve an orbit about L2. The Mathworks Matlab product was used to develop a tool for generating command quaternions. The command quaternion table (CQT) was used to drive the attitude during the perigee maneuvers. The MatrixX toolset, originally written by Integrated Systems, Inc., now distributed by Mathworks, was used to create HiFi, a high fidelity simulator of the MAP attitude control system. HiFi was used to test the CQT and to make sure that all attitude requirements were met during the maneuver. In addition, all ACS data plotting and output were generated in MatrixX. A final test used FlatSat, a real-time hardware-in-the-loop simulator, which used identical MAP flight code to simulate operations on the spacecraft. Simulations in FlatSat allowed the MAP team to verify maneuver commands, timing, and spacecraft configuration before the commands were sent up to the spacecraft for execution. The MAP maneuver team successfully pieced together all of these COTS tools for designing MAP's maneuvers and MAP is now collecting data at L2.

Mesarch, Michael A.↗
NASA NTRS2003

Orbit Determination Support for the Microwave Anisotropy Probe (MAP)

THe microwave Anisotropy Probe (MAP) ia the second Medium Class Explorer (MIDEX) mission of the National Aeronautics and Space Administration (NASA). The main goal of the MAP observatory is to measure the temperature fluctuations, known as anisotropy, of the cosmic microwave background (CBG) radiation over the entire sky and to produce a map of the CMB anisotropies with an angular resolution of approximately 3 degrees. MAP was launched from the Cape Canaveral Air Force Station Complex 17 aboard a Delta II 7425-10 expendable launch vehicle at exactly 19:46:46.183 UTC on June 30, 20001. The spacecraft receiver a nominal direct insertion by the Delta expendable launch vehicle into a 185-km circular orbit with a 28.7 deg. inclination. MAP was than maneuvered into a sequence of phasing loops designed to set up a lunar gravity-assisted acceleration of the spacecraft onto a transfer trajectory to a lissajous orbit about the Earth-Sun L2 Lagrange point, about 1.5 million km from Earth. The science mission minimum lifetime is two years of observations at L2 with a desired lifetime of 4 years. The MAP transfer orbit consisted of 3.5 phasing loops.The MAP trajectory schematic all the way through L2 is shown. The first loop had a period of 7 days, the second and third loops were 10 days long, and the last half loop was 5 days. The periselene (i.e., lunar encounter or swingby) took place approximately 30 days after launch. After the periselene, the spacecraft cruised for approximately 60 days before it arrived in the vicinity of the L2 libration point. Two mid-course correction (MCC) maneuvers were performed to refine MAP's post-launch trajectory-one after periselene and one prior to arrival at vicinity of L2. Now that MAP is at its operational L2 lissajous orbit, the MAP satellite is commanded to perform occasional station-keeping (SK) maneuvers in order to maintain its orbit around L2. Because of its complex orbital characteristics, the mission provided a unique challenge to orbit determination (OD) support in many orbital regimes.

Truong, Son H.↗
NASA NTRS2010

New Geologic Map of the Argyre Region of Mars: Deciphering the Geologic History Through Mars Global Surveyor, Mars Odyssey, and Mars Express Data

The primary objective of the mapping effort is to produce a geologic map of the Argyre basin and surrounding region at 1:5,000,000 scale in both digital and print formats that will detail the stratigraphic and crosscutting relations among rock materials and landforms (30 deg. S to 65 deg. S, 290 deg. E to 340 deg E). There has not been a detailed geologic map produced of the Argyre region since the Viking-era mapping investigation. The mapping tasks include stratigraphic mapping, crater counting, feature mapping, quantitative landform analysis, and spectroscopic/ stratigraphic investigation feature mapping. The regional geologic mapping investigation includes the Argyre basin floor and rim materials, the transition zone that straddles the Thaumasia plateau, which includes Argyre impactrelated modification, and the southeast margin of the Thaumasia plateau using important new data sets from the Mars Global Surveyor, Mars Odyssey, Mars Express, and Mars Reconnaissance Orbiter. The geologic information unfolded by this new mapping project will be useful to the community for constraining the regional geology, paleohydrology, and paleoclimate, which includes but is not limited to the assessment of: (1) whether the Argyre basin contained lakes, (2) the extent of reported flooding and glaciation, (3) existing interpretations of the origin of the narrow ridges located in the southeast part of the basin floor, and (4) the extent of Argyre-related tectonism and its influence on the surrounding regions.

Dohm, J. M.↗
NASA NTRS2010

A Comparison of Satellite-Derived Snow Maps with a Focus on Ephemeral Snow in North Carolina

In this paper, we focus on the attributes and limitations of four commonly-used daily snowcover products with respect to their ability to map ephemeral snow in central and eastern North Carolina. We show that the Moderate-Resolution Imaging Spectroradiometer (MODIS) fractional snow-cover maps can delineate the snow-covered area very well through the use of a fully-automated algorithm, but suffer from the limitation that cloud cover precludes mapping some ephemeral snow. The semi-automated Interactive Multi-sensor Snow and ice mapping system (IMS) and Rutgers Global Snow Lab (GSL) snow maps are often able to capture ephemeral snow cover because ground-station data are employed to develop the snow maps, The Rutgers GSL maps are based on the IMS maps. Finally, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) provides some good detail of snow-water equivalent especially in deeper snow, but may miss ephemeral snow cover because it is often very thin or wet; the AMSR-E maps also suffer from coarse spatial resolution. We conclude that the southeastern United States represents a good test region for validating the ability of satellite snow-cover maps to capture ephemeral snow cover,

Hall, Dorothy K.↗
NASA NTRS2011

Forest Biomass Mapping From Lidar and Radar Synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R(exp 2) of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI Mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar- SAR synergy 63 was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

Sun, Guoqing↗