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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

SHERLOC: Results of the first 350 sols of operations

On February 18th 2021, the Perseverance rover landed in Jezero crater, Mars. This site was chosen because orbiter data analysis provides evidence that the crater hosted a stream-fed lake during the Martian Noachian period. The Octavia Butler landing site is located ~1.9 km east of the remnants of a river delta. Deltaic and lacustrine sediments can preserve biosignatures, making Jezero crater a prime target for Mars sample return science. One of the seven instruments on Perseverance’s science payload is SHERLOC –Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals. SHERLOC combines fluorescence and Raman spectroscopy with microscopic imaging to analyze surface material to better understand the history of the aqueous environments recorded in the rocks of Jezero crater and to search for potential biosignatures. SHERLOC imaging obtains high spatial resolution images of geological targets to identify grain-scale structure and texture. SHERLOC spectroscopy enables high-sensitivity detection, characterization, and spatially resolved correlation of trace organic materials. Native fluorescence emissions from aromatic organic species allow for detection and classification of aromatic organic molecules, whereas Raman scattered photons from molecules allow identification of functional groups of organics, chemicals, and minerals. In the first 300 sols, SHERLOC has analyzed 3 natural surfaces, and 5 abraded rock patches created during the Crater Floor Campaign within Jezero crater. SHERLOC has been able to identify phosphates, amorphous/microcrystalline silicate (AMS), olivine, sulfates, and carbonates in abraded patches in the green zone campaign within Jezero Crater Máaz and Séítah formations. Within these detections we have begun to tell the story of what this crater was like when it was full of liquid water over 3 billion years ago. In each of these samples we have identified fluorescence features that are likely aromatic organics native to the rock interiors. We have identified multiple unique fluorescence signatures, within each of the abraded patches. The organic signatures have either been widely distributed over an extended area, which is probably due to planetary wide dust, or have spatially resolved locations that are collocated with different mineral signatures. Note: Additional information available on attachment.

L. W. Beegle↗
NASA NTRS2020

An Evolutionary System of Mineralogy. Part I: Stellar Mineralogy (>13 to 4.6 Ga)

Minerals preserve records of the physical, chemical, and biological histories of their origins and subsequent alteration, and thus provide a vivid narrative of the evolution of Earth and other worlds through billions of years of cosmic history. Mineral properties, including trace and minor elements, ratios of isotopes, solid and fluid inclusions, external morphologies, and other idiosyncratic attributes, represent information that points to specific modes of formation and subsequent environmental histories—information essential to understanding the co-evolving geosphere and biosphere. This perspective suggests an opportunity to amplify the existing system of mineral classification, by which minerals are defined solely on idealized end-member chemical compositions and crystal structures. Here we present the first in a series of contributions to explore a complementary evolutionary system of mineralogy—a classification scheme that links mineral species to their paragenetic modes. The earliest stage of mineral evolution commenced with the appearance of the first crystals in the universe at >13 Ga and continues today in the expanding, cooling atmospheres of countless evolved stars, which host the high-temperature (T > 1000 K), low-pressure (P < 10-2 atm) condensation of refractory minerals and amorphous phases. Most stardust is thought to originate in three distinct processes in carbon- and/or oxygen-rich mineral-forming stars: (1) condensation in the cooling, expanding atmospheres of asymptotic giant branch stars; (2) during the catastrophic explosions of supernovae, most commonly core collapse (Type II) supernovae; and (3) classical novae explosions, the consequence of runaway fusion reactions at the surface of a binary white dwarf star. Each stellar environment imparts distinctive isotopic and trace element signatures to the micro- and nanoscale stardust grains that are recovered from meteorites and micrometeorites collected on Earth’s surface, by atmospheric sampling, and from asteroids and comets. Although our understanding of the diverse mineral-forming environments of stars is as yet incomplete, we present a preliminary catalog of 41 distinct natural kinds of stellar minerals, representing 22 official International Mineralogical Association (IMA) mineral species, as well as 2 as yet unapproved crystalline phases and 3 kinds of non-crystalline condensed phases not codified by the IMA.

Robert M. Hazen↗
NASA NTRS1982

Mapping of deciduous forest cover using simulated Landsat-D TM data

An evaluation is presented of the supervised vegetation classification images of heavily forested terrain in the eastern US produced from simulated Landsat-D Thematic Mapper data as part of the Joint NASA/Geosat Test Case study of the Lost River, West Virginia, gas field. This investigation utilized data supplied by the NS-001 aircraft multispectral scanner (15 m IFOV) of NASA. The instruments recognized a total of 9 vegetation classes and one soil class. A hybrid parallelepiped-Bayesian maximum likelihood classifier was employed to produce the supervised vegetation classifications. The data were obtained at the height of the fall foliage display for this portion of West Virginia. Results show that both classifications are highly accurate, based on a comparison of assignment of vegetation classes on each of the images with actual ground conditions. It is noted that the anomalous distribution of certain woody species found in the resulting supervised classifications may be related to the microseepage of methane from the gas reservoir.

Rock, B. N.↗
NASA NTRS2015

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

hyperspectral fusion↗
NASA NTRS2011

Nature's Way of Making Audacious Space Projects Viable

Building a starship within the next 100 years is an audacious goal. To be successful, we need sustained funding that may be difficult to maintain in the face of economic challenges that are poised to arise during these next 100 years. Our species' civilization has only recently reached the classification as (approximately) Type-I on the Kardashev scale; that is, we have spread out from one small locality to become a global species mastering the energy and resources of an entire planet. In the process we discovered the profound truth that the two-dimensional surface of our world is not flat, but has positive curvature and is closed so that its area and resources are finite. It should come as no surprise to a Type I civilization when its planet's resources dWindle; how could they not? Yet we have gone year by year, government by government, making little investment for the time when civilization becomes violent in the unwelcome contractions that must follow, when we are forced too late into the inevitable choice: to remain and diminish on an unhappy world; or to expand into the only dimension remaining perpendicularly outward from the surface into space. Then some day we may become a Type-II civilization, mastering the resources of an entire solar system. Our species cannot continue as we have on this planet for another 100 years. Doubtless it falls on us today, the very time we intended to start building a starship, to make the late choice. We wished this century to be filled with enlightenment and adventure; it could be an age of desperation and war. What a time to begin an audacious project in space! How will we maintain consistent funding for the next 100 years? Fortunately, saving a civilization, mastering a solar system, and doing other great things like building starships amount to mostly the same set of tasks. Recognizing what we must be about during the next 100 years will make it possible to do them all.

Metzger, Philip T.↗
NASA NTRS1987

Evaluation of several classification schemes for mapping forest cover types in Michigan

Landsat MSS data were evaluated for mapping forest cover types in the northern Lower Peninsula of Michigan. The study examined seasonal variations, interpretation procedures and vegetation composition/distribution and their effect on overall classification accuracy and ability to identify individual pine species. Photographic images were used for visual interpretations while digital analysis was performed using a common (ERDAS) microcomputer image processing system. The classification schemes were evaluated using contingency tables and were ranked using the KAPPA statistic. The various classification schemes were ranked differentially according to study site location. Visual interpretation procedures ranked best, or least accurate, depending on the spatial distribution and complexity of the forest cover. Supervised classification techniques were more accurate than unsupervised clustering over all sites and seasons. Maximum likelihood classification of June data was superior to any digital classification technique of February data. The study indicates that classification accuracy is more dependent on the composition and distribution of forests in the northern lower Peninsula of Michigan than on the selection of a particular classification scheme.

Hudson, W. D.↗
NASA NTRS1993

Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

A maximum a posteriori Bayesian classifier for multifrequency polarimetric SAR data is used to perform a supervised classification of forest types in the floodplains of Alaska. The image classes include white spruce, balsam poplar, black spruce, alder, non-forests, and open water. The authors investigate the effect on classification accuracy of changing environmental conditions, and of frequency and polarization of the signal. The highest classification accuracy (86 percent correctly classified forest pixels, and 91 percent overall) is obtained combining L- and C-band frequencies fully polarimetric on a date where the forest is just recovering from flooding. The forest map compares favorably with a vegetation map assembled from digitized aerial photos which took five years for completion, and address the state of the forest in 1978, ignoring subsequent fires, changes in the course of the river, clear-cutting of trees, and tree growth. HV-polarization is the most useful polarization at L- and C-band for classification. C-band VV (ERS-1 mode) and L-band HH (J-ERS-1 mode) alone or combined yield unsatisfactory classification accuracies. Additional data acquired in the winter season during thawed and frozen days yield classification accuracies respectively 20 percent and 30 percent lower due to a greater confusion between conifers and deciduous trees. Data acquired at the peak of flooding in May 1991 also yield classification accuracies 10 percent lower because of dominant trunk-ground interactions which mask out finer differences in radar backscatter between tree species. Combination of several of these dates does not improve classification accuracy. For comparison, panchromatic optical data acquired by SPOT in the summer season of 1991 are used to classify the same area. The classification accuracy (78 percent for the forest types and 90 percent if open water is included) is lower than that obtained with AIRSAR although conifers and deciduous trees are better separated due to the presence of leaves on the deciduous trees. Optical data do not separate black spruce and white spruce as well as SAR data, cannot separate alder from balsam poplar, and are of course limited by the frequent cloud cover in the polar regions. Yet, combining SPOT and AIRSAR offers better chances to identify vegetation types independent of ground truth information using a combination of NDVI indexes from SPOT, biomass numbers from AIRSAR, and a segmentation map from either one.

Rignot, Eric↗
DOE OSTI2025

Dissociation of gas-phase anisole induced by low-energy electron interactions: understanding patterns of aromatic bond cleavage

Abstract In view of elucidating the fragmentation patterns of aromatic systems induced by low-energy electron interactions, dissociative electron attachment (DEA) to gas-phase anisole was performed. Anionic fragments resulting from this DEA process were detected by a quadrupole mass spectrometer, and ion yields of those fragments as a function of incident electron energy were rendered. Our study showed the formation of CH 3 − , HCC − , and OCH 3 − fragments, suggesting that various dissociation channels proceed out of DEA to anisole. We employed density functional theory to compute thermodynamic threshold energies for each potential dissociation channel. Those theoretical calculations supported the prediction that the CH 3 − and OCH 3 − fragments form via mechanisms of single-bond cleavage; the HCC − fragments may form through two-, three-, or four-body dissociation channels that entail hydrogen transfers and the cleavage of multiple aromatic bonds. The experimental resonance energies that form the CH 3 − , HCC − , and OCH 3 − fragments were 6.0 eV, 5.8 and 9.7 eV, and 9.8 eV, respectively. Given the classification of anisole as a monosubstituted aromatic species, our results explain generalizable patterns of electron-mediated dissociation in aromatic systems.

Finley, Jacob (ORCID:0009000606963352)↗
NASA NTRS1994

A proposition for the classification of carbonaceous chondritic micrometeorites

Classification of interplanetary dust particles (IDP's) should be unambiguous and, if possible, provide an opportunity to interrelate these ultrafine IDP's with the matrices of undifferentiated meteorites. I prefer a scheme of chemical groupings and petrologic classes that is based on primary IDP properties that can be determined without prejudice by individual investigators. For IDP's of 2-50 microns these properties are bulk elemental chemistry, morphology, shape, and optical properties. The two major chemical groups are readily determined by energy dispersive spectroscopic analysis using the scanning or analytical electron microscope. Refinement of chondritic IDP classification is possible using the dominant mineral species, e.g. olivine, pyroxene, and layer silicates, and is readily inferred from FTIR, and automated chemical analysis. Petrographic analysis of phyllosilicate-rich IDP's will identify smectite-rich and serpentine-rich particles. Chondritic IDP's are also classified according to morphology, viz., CP and CF IDP's are aggregate particles that differ significantly in porosity, while the dense CS IDP's have a smooth surface. The CP IDP's are characterized by an anhydrous silicate mineralogy, but small amounts of layer silicates may be present. Distinction between the CP and CF IDP's is somewhat ambiguous, but the unique CP IDP's are fluffy, or porous, ultrafine-grained aggregates. The CP IDP's, which may contain silicate whiskers, are the most carbon-rich extraterrestrial material presently known. The CF IDP's are much less porous that CP IDP's. Using particle type definitions, CP IDP's in the NASA JSC Cosmic Dust Catalogs are approx. 15 percent of all IDP's that include nonchondritic spheres. Most aggregate particles are of the CF type.

Rietmeijer, Frans J. M.↗
NASA NTRS1975

Wildlife management by habitat units: A preliminary plan of action

Procedures for yielding vegetation type maps were developed using LANDSAT data and a computer assisted classification analysis (LARSYS) to assist in managing populations of wildlife species by defined area units. Ground cover in Travis County, Texas was classified on two occasions using a modified version of the unsupervised approach to classification. The first classification produced a total of 17 classes. Examination revealed that further grouping was justified. A second analysis produced 10 classes which were displayed on printouts which were later color-coded. The final classification was 82 percent accurate. While the classification map appeared to satisfactorily depict the existing vegetation, two classes were determined to contain significant error. The major sources of error could have been eliminated by stratifying cluster sites more closely among previously mapped soil associations that are identified with particular plant associations and by precisely defining class nomenclature using established criteria early in the analysis.

Frentress, C. D.↗
DOE OSTI2025

Virus species names have been standardized; virus names remain unchanged

Virus taxonomy, comprising classification and nomenclature, is regulated by the International Committee on Taxonomy of Viruses (ICTV). Taxon names are standardized to facilitate recognition and communication, with defined suffixes for each rank (e.g., the names of orders, families, and genera end in -virales, -viridae, and -virus, respectively). However, until recently, a standard format for species names was lacking. In 2021, following extensive discussion and community consultation, the ICTV decided to adopt a standardized binomial (Linnaean) format for virus species names, consisting of the genus name followed by a "freeform" species epithet. Previously assigned virus species names that were non-compliant with the binomial format have been fully updated. In contrast to taxon names regulated by the ICTV, the names of viruses, or "common" names, such as yellow fever virus or human immunodeficiency virus, are not under the remit of the ICTV and have not been changed.

Zerbini, F Murilo↗
NASA NTRS2013

Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals

Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.

Thompson, David R.↗
NASA NTRS2012

Optimization of Support Vector Machine (SVM) for Object Classification

The Support Vector Machine (SVM) is a powerful algorithm, useful in classifying data into species. The SVMs implemented in this research were used as classifiers for the final stage in a Multistage Automatic Target Recognition (ATR) system. A single kernel SVM known as SVMlight, and a modified version known as a SVM with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SVM as a method for classification. From trial to trial, SVM produces consistent results.

support vector machice (SVM)↗
NASA NTRS2011

Testing of the Support Vector Machine for Binary-Class Classification

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

autonomous target recognition systemr↗
NASA NTRS2001

Spectral Classification of Similar Materials using the Tetracorder Algorithm: The Calcite-Epidote-Chlorite Problem

Recent work on automated spectral classification algorithms has sought to distinguish ever-more similar materials. From modest beginnings separating shade, soil, rock and vegetation to ambitious attempts to discriminate mineral types and specific plant species, the trend seems to be toward using increasingly subtle spectral differences to perform the classification. Rule-based expert systems exploiting the underlying physics of spectroscopy such as the US Geological Society Tetracorder system are now taking advantage of the high spectral resolution and dimensionality of current imaging spectrometer designs to discriminate spectrally similar materials. The current paper details recent efforts to discriminate three minerals having absorptions centered at the same wavelength, with encouraging results.

Dalton, J. Brad↗
NASA NTRS2023

Bandelier Ecological Conservation: Mapping Invasive Species Along the Rio Grande Corridor in Bandelier National Monument

The Southwest U.S. has experienced a growth of invasive riparian species, specifically Elaeagnus angustifolia (Russian olive), Tamarix ramosissima (saltcedar), and Ulmus pumila (Siberian elm), which alter local soil chemistry and outcompete native species. Locating these exotic species is critical for ecological conservation; however, field identification can be resource intensive. NASA DEVELOP partnered with the National Park Service (NPS) at Bandelier National Monument (BAND) to assess the feasibility of using Earth observation data to map invasive species along the Rio Grande corridor of the park. The team used Landsat 8 OLI, Sentinel-2 MSI, and ISS DESIS imagery to compute principal components based on spectral bands, vegetation indices, and terrain indices. Using the first five principal components, the team created classification maps using both a k-means classification algorithm and a random forest algorithm to differentiate between native and non-native species. The team derived maps for the three invasive riparian species in the region for the last five years. The team found that invasive species covered 33% of the park's river corridor in 2023, and the invasive species extent has increased by 5.7% from 2019 to 2023. The methods will serve as a guide for aiding historic and present invasive species identification in riparian regions, and the NPS staff at BAND will use the results to inform local mitigation practices and advocate for invasive species removal.

Evan Barrett↗
NASA NTRS1981

Monitoring wetlands change using LANDSAT data

A wetlands monitoring study was initiated as part of Delaware's LANDSAT applications demonstration project. Classifications of digital data are conducted in an effort to determine the location and acreage of wetlands loss or gain, species conversion, and application for the inventory and typing of freshwater wetlands. A multi-seasonal approach is employed to compare data from two different years. Unsupervised classifications were conducted for two of the four dates examined. Initial results indicate the multi-seasonal approach allows much better separation of wetland types for both tidal and non-tidal wetlands than either season alone. Change detection is possible but generally misses the small acreages now impacted by man.

Hardin, D. L.↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗