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At least 919 records · Page 51

Seasonal Variability of Saturn's Atmosphere

The seasonal variability of Saturn's clouds and weather layer, currently displaying a variety of phenomena (convective storms, planetary waves, giant storms and lightning-induced events, etc.) is not yet fully understood. Variations of Saturn's radiance at 5.2 microns, a spectral region dominated by thermal emission in an atmospheric window containing weak gaseous absorption, contain a strong axisymmetric component as well as large discrete features at low and mid-latitudes that are several degrees colder than the planetary average and uncorrelated with features at shorter wavelengths that are dominated by reflected sunlight (Yanamandra-Fisher et al., 2001. Icarus, Vol. 150). The characterization of several fundamental atmospheric properties and processes, however, remains incomplete, namely: How do seasons affect (a) the global distribution of gaseous constituents and aerosols; and (b) temperatures and the stability against convection and large scale-atmospheric transport? Do 5-micron clouds have counterparts at other altitude levels? What changes occur during the emergence of Great White Storms? Data acquired at the NASA/IRTF and NAOJ/Subaru from 1995 - 2011; since 2004, high-resolution multi-spectral and high-spatial imaging data acquired by the NASA/ESA Cassini mission, represents half a Saturnian year or two seasons. With the addition of detailed multi-spectral data sets acquired by amateur observers, we study these dramatic phenomena to better understand the timeline of the evolution of these events. Seasonal (or temporal) trends in the observables such as albedo of the clouds, thermal fields of the atmosphere as function of altitude, development of clouds, hazes and global abundances of various hydrocarbons in the atmosphere can now be modeled. We will present results of our ongoing investigation for the search and characterization of periodicities over half a Saturnian year, based on a non-biased a priori approach and time series techniques (such as Principal Component Analysis, PCA and Lomb-Scargle periodograms, LSP).

Yanamandra-Fisher, Padma A.↗

Composition and Petrology of HED Polymict Breccias: The Regolith of (4) Vesta

The polymict breccias of the howardite, eucrite and diogenite (HED) clan of meteorites preserve records of regolith processes that occur on Vesta, their putative home world. These breccias -- howardites, polymict eucrites and polymict diogenites -- are impact-engendered mixtures of diogenites and eucrites. The compositions of polymict breccias can be used to constrain the lithologic diversity of the vestan crust and the excavation depths of these materials. We have done petrological and compositional studies of multiple samples of 5 polymict eucrites and 28 howardites to investigate these issues. Older analyses were done on samples of approx 0.5 gram mass by INAA; newer analyses on samples of approx 5 gram mass by XRF and ICP-MS. We estimate the percentage of eucritic material (POEM) of polymict breccias by comparing their Al and/or Ca contents to those of average basaltic eucrite and diogenite. Our samples have POEM ranging from 28 to 98; adding two polymict diogenites from extends the range to POEM 10. One hypothesis is that ancient, well-mixed vestan regolith has POEM approx 67 and has a higher content of admixed impactor material. Several of our howardites have POEM of 59-74 (Al and/or Ca contents +/- 10% of POEM 67); about a third have Ni contents >300 micro g/g suggesting they contain >2% chondritic material (CM and/or CR). These may be regolithic howardites. Only one (LEW 85313) contains Ne dominated by a solar wind (SW) component. PCA 02066 is dominated by impact-melt material of polymict parentage and petrologically appears to be a mature regolith breccia, yet it does not contain SW-Ne. GRO 95602 falls within the POEM window, contains SW-Ne], yet has a Ni content of 193 micro g/g. Its petrologic characteristics suggest it was formed from immature regolith (no polymict breccia clasts; no glass). Trace element characteristics of the polymict breccias demonstrate the dominance of main-group eucrites as the basaltic component. Mixing diagrams of Zr, Nb, Ba, Hf and Ta with Al show no evidence for a significant contribution from Stannern-trend eucrites. An exception is polymict eucrite LEW 86001 (POEM 92), which is dominated by Stannern-trend basaltic debris. Howardite LAP 04838 (POEM 84) has higher incompatible trace concentrations than other polymict breccias (excluding LEW 86001), and either contains a Stannern-trend basaltic component, or has a significant contributions from evolved eucrites like Nuevo Laredo.

Mittlefehldt, David W.↗

Hard X-ray Variations in the Crab Nebula

In the first two years of science operations of the Fermi Gamma-ray Burst Monitor (GBM), August 2008 to August 2010, approximately 7% (70 mcrab) decline was discovered in the overall Crab Nebula flux in the 15 - 50 keV band, measured with the Earth occultation technique. This decline was independently confirmed with four other instruments: the RXTE/PCA, Swift/BAT, INTEGRAL/IBIS, and INTEGRAL/SPI. The pulsed flux measured with RXTE/PCA from 1999-2010 was consistent with the pulsar spin-down, indicating that the observed changes were nebular. From 2001 to 2010, the Crab nebula flux measured with RXTE/ PCA was particularly variable, changing by up to approximately 3.5% per year in the 15-50 keV band. These variations were confirmed with INTEGRAL/SPI starting in 2003, Swift/BAT starting in 2005, and Fermi GBM starting in 2008. Before 2001 and since 2010, the Crab nebula flux has appeared more stable, varying by less than 2% per year. I will present updated light curves in multiple energy bands for the Crab Nebula, including recent data from Fermi GBM, Swift/BAT, INTEGRAL and MAXI, and a 16-year long light curve from RXTE/PCA.

Wilson-Hodge, Colleen A.↗

Extraterrestrial Amino Acids Identified in Metal-Rich CH and CB Carbonaceous Chondrites from Antarctica

Carbonaceous chondrites contain numerous indigenous organic compounds and could have been an important source of prebiotic compounds required for the origin of life on Earth or elsewhere. Extraterrestrial amino acids have been reported in five of the eight groups of carbonaceous chondrites and are most abundant in CI, CM, and CR chondritesbut are also present in the more thermally altered CV and CO chondrites. We report the abundance, distribution, and enantiomeric and isotopic compositions of simple primary amino acids in six metal-rich CH and CB carbonaceous chondrites that have not previously been investigated for amino acids: Allan Hills (ALH) 85085 (CH3), Pecora Escarpment(PCA) 91467 (CH3), Patuxent Range (PAT) 91546 (CH3), MacAlpine Hills (MAC) 02675(CBb), Miller Range (MIL) 05082 (CB), and Miller Range (MIL) 07411 (CB). Amino acid abundances and carbon isotopic values were obtained by using both liquid chromatography time-of-flight mass spectrometry and fluorescence, and gas chromatography isotope ratiomass spectrometry. The (delta D, delta C-13, delta N-15) ratios of multiple amino acids fall outside of the terrestrial range and support their extraterrestrial origin. Extracts of CH chondrites were found to be particularly rich in amino acids (1316 parts per million, ppm) while CB chondrite extracts had much lower abundances (0.22 ppm). The amino acid distributions of the CH and CB chondrites were distinct from the distributions observed in type 2 and 3 CM and CR chondrites and contained elevated levels of beta-, gamma-, and delta-amino acids compared to the corresponding alpha-amino acids, providing evidence that multiple amino acid formation mechanisms were important in CH and CB chondrites.

Burton, Aaron S.↗

Classical Accreting Pulsars with NICER

Soft excesses are very common center dot Lx > 1038 erg/s - reprocessing by optically thick material at the inner edge of the accretion disk center dot Lx < 1036 erg/s - photoionized or collisionally heated diffuse gas or thermal emission from the NS surface center dot Lx ~ 1037 erg/s - either or both types of emission center dot NICER observations of soft excesses in bright X‐ray pulsars combined with reflection modeling will constrain the ionization state, metalicity and dynamics of the inner edge of the magnetically truncated accretion disk Reflection models of an accretion disk for a hard power law - Strong soft excess below 3 keV from hot X‐ray heated disk - For weakly ionized case: strong recombination lines - Are we seeing changes in the disk ionization in 4U1626‐26? 13 years of weekly monitoring with RXTE PCA center dot Revealed an unexpectedly large population of Be/X‐ray binaries compared to the Milky Way center dot Plotted luminosities are typical of "normal" outbursts (once per orbit) center dot The SMC provides an excellent opportunity to study a homogenous population of HMXBs with low interstellar absorption for accretion disk studies. Monitoring with NICER will enable studies of accretion disk physics in X‐ray pulsars center dot The SMC provides a potential homogeneous low‐absorption population for this study center dot NICER monitoring and TOO observations will also provide measurements of spinfrequencies, QPOs, pulsed fluxes, and energy spectra.

Wilson-Hodge, Colleen A.↗

Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Niño, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations.

Roberts, J. Brent↗

High Performance EVA Glove Collaboration: Glove Injury Data Mining Effort

Human hands play a significant role during Extravehicular Activity (EVA) missions and Neutral Buoyancy Lab (NBL) training events, as they are needed for translating and performing tasks in the weightless environment. Because of this high frequency usage, hand and arm related injuries are known to occur during EVA and EVA training in the NBL. The primary objectives of this investigation were to: 1) document all known EVA glove related injuries and circumstances of these incidents, 2) determine likely risk factors, and 3) recommend interventions where possible that could be implemented in the current and future glove designs. METHODS: The investigation focused on the discomforts and injuries of U.S. crewmembers who had worn the pressurized Extravehicular Mobility Unit (EMU) spacesuit and experienced 4000 Series or Phase VI glove related incidents during 1981 to 2010 for either EVA ground training or in-orbit flight. We conducted an observational retrospective case-control investigation using 1) a literature review of known injuries, 2) data mining of crew injury, glove sizing, and hand anthropometry databases, 3) descriptive statistical analyses, and finally 4) statistical risk correlation and predictor analyses to better understand injury prevalence and potential causation. Specific predictor statistical analyses included use of principal component analyses (PCA), multiple logistic regression, and survival analyses (Cox proportional hazards regression). Results of these analyses were computed risk variables in the forms of odds ratios (likelihood of an injury occurring given the magnitude of a risk variable) and hazard ratios (likelihood of time to injury occurrence). Due to the exploratory nature of this investigation, we selected predictor variables significant at p≤0.15. RESULTS: Through 2010, there have been a total of 330 NASA crewmembers, from which 96 crewmembers performed 322 EVAs during 1981-2010, resulting in 50 crewmembers being injured inflight and 44 injured during 11,704 ground EVA training events. Of the 196 glove related injury incidents, 106 related to EVA and 90 to EVA training. Over these 196 incidents, 277 total injuries (126 flight; 151 training) were reported and were then grouped into 23 types of injuries. Of EVA flight injuries, 65% were commonly reported to the hand (in general), metacarpophalangeal (MCP) joint, and finger (not including thumb) with fatigue, abrasion, and paresthesia being the most common injury types (44% of total flight injuries). Training injuries totaled to more than 70% being distributed to the fingernail, MCP joint, and finger crotch with 88% of the specific injuries listed as pain, erythema, and onycholysis. Of these training injuries, when reporting pain or erythema, the most common location was the index finger, but when reporting onycholysis, it was the middle finger. Predictor variables specific to increased risk of onycholysis included: female sex (OR=2.622), older age (OR=1.065), increased duration in hours of the flight or training event (OR=1.570), middle finger length differences in inches between the finger and the EVA glove (OR=7.709), and use of the Phase VI glove (OR=8.535). Differentiation between training and flight and injury reporting during 2002-2004 were significant control variables. For likelihood of time to first onycholysis injury, there was a 24% reduction in rate of reporting for each year increase in age. Also, more experienced crewmembers, based on number of EVA flight or training events completed, were less likely to report an onycholysis injury (3% less for every event). Longer duration events also found reporting rates to occur 2.37 times faster for every hour of length. Crewmembers with larger hand size reported onycholysis 23% faster than those with smaller hand size. Finally, for every 1/10th of an inch increase in difference between the middle finger length and the glove, the rate of reporting increased by 60%. DISCUSSION: One key finding was that the Series 4000 glove had a lower injury risk than the Phase VI, which provides a platform for further evaluation. General interventions that reduce hand overexertion and repetitive use exposure through tool development, procedural changes and shorter exposures may be one mitigation path, but due to the way the training event times were reported, we cannot provide a guideline for a specific event duration change. When the finger length was different from the glove length, the risk of injury increased indicating that the use of larger finger take-ups could be contributing to injury and therefore may not be recommended. Prior to this investigation, there was one previous investigation indicating hand anthropometry may be related to onycholysis. We found different hand anthropometry variables indicated by this investigation as compared to the prior, specifically differences in middle finger length compared to glove finger length, which point more towards a sizing issue than a specific anthropometry issue. Additionally, although this investigation has identified sizing as an issue, the force and environmental-related variables of the EVA glove that could also cause injury were not accounted for.

Reid, C. R.↗

The Mineralogy and Petrology of Anomalous Eucrite Emmaville

It has long been known that certain basaltic achondrites share similarities with eucrites. These eucrite-like achondrites have distinct isotopic compositions and petrologic characteristics indicative of formation on a separate parent body from the howardite-eucrite-diogenite (HED) clan (e.g., Ibitira, Northwest Africa (NWA) 011). Others show smaller isotopic variations but are otherwise petrologically and compositionally indistinguishable from basaltic eucrites (e.g., Pasamonte, Pecora Escarpment (PCA) 91007). The Emmaville eucrite has a delta O-17 value of -0.137 plus or minus 0.024 per mille (1 sigma), which is substantially different from the eucrite mean of -0.246 plus or minus 0.014 per mille (2 sigma), but similar to those of A-881394 and Bunburra Rockhole (BR). Currently little data exist for Emmaville in terms of petrology or bulk composition. Studying anomalous eucrites allows us to more completely understand the numbers of asteroids represented by eucrite- like basalts and thus constrain the heterogeneity of the HED suite. In this study, we present our preliminary petrological and mineral composition results for Emmaville.

Barrett, T. J.↗

Optimization of Adaboost Algorithm for Sonar Target Detection in a Multi-Stage ATR System

JPL has developed a multi-stage Automated Target Recognition (ATR) system to locate objects in images. First, input images are preprocessed and sent to a Grayscale Optical Correlator (GOC) filter to identify possible regions-of-interest (ROIs). Second, feature extraction operations are performed using Texton filters and Principal Component Analysis (PCA). Finally, the features are fed to a classifier, to identify ROIs that contain the targets. Previous work used the Feed-forward Back-propagation Neural Network for classification. In this project we investigate a version of Adaboost as a classifier for comparison. The version we used is known as GentleBoost. We used the boosted decision tree as the weak classifier. We have tested our ATR system against real-world sonar images using the Adaboost approach. Results indicate an improvement in performance over a single Neural Network design.

Adaboost↗

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗

Optimization of a Multi-Stage ATR System for Small Target Identification

An Automated Target Recognition system (ATR) was developed to locate and target small object in images and videos. The data is preprocessed and sent to a grayscale optical correlator (GOC) filter to identify possible regionsof- interest (ROIs). Next, features are extracted from ROIs based on Principal Component Analysis (PCA) and sent to neural network (NN) to be classified. The features are analyzed by the NN classifier indicating if each ROI contains the desired target or not. The ATR system was found useful in identifying small boats in open sea. However, due to "noisy background," such as weather conditions, background buildings, or water wakes, some false targets are mis-classified. Feedforward backpropagation and Radial Basis neural networks are optimized for generalization of representative features to reduce false-alarm rate. The neural networks are compared for their performance in classification accuracy, classifying time, and training time.

false alarm rate↗

Aura OMI Observations of Global SO2 and NO2 Pollution from 2005 to 2013

The Ozone Monitoring Instrument (OMI), a NASA partnership with the Netherlands and Finland, flies on the NASA Aura satellite and uses reflected sunlight to measure the two critical atmospheric trace gases: nitrogen dioxide (NO2) and sulfur dioxide (SO2) characterizing daily air quality. Both gases and the secondary pollutants they produce (particulate matter, PM2.5, and tropospheric ozone) are USEPA designated criteria pollutants, posing serious threats to human health and the environment (e.g., acid rain, plant damage and reduced visibility). Our group at NASA GSFC has developed and maintained OMI standard SO2 and NO2 data products. We have recently released an updated version of the standard NO2 L2 and L3 products (SP v2.1) and continue improving the algorithm. We are currently in the process of releasing next generation pollution SO2 product, based on an innovative Principal Component Analysis (PCA) algorithm, which greatly reduces the noise and biases. These new standard products provide valuable datasets for studying anthropogenic pollution on local to global scales. Here we highlight some of the OMI observed changes in air quality over several regions. Over the US average NO2 and SO2 pollution levels had decreased dramatically as a result of both technological improvements (e.g., catalytic converters on cars) and stricter regulations of emissions. We see continued decline in pollution over Europe. Over China OMI observed an increase of about 60 percent in NO2 pollution between 2005 and 2013, despite a temporal reversal of the growing trend due to both 2008 Olympic Games and the economic recession in 2009. Chinese SO2 pollution seems to have stabilized since peaking in 2007, probably due to government efforts to curb SO2 emissions from the power sector. We have also observed large increases in both SO2 and NO2 pollution particularly in Eastern India where a number of large new coal power plants had been built in recent years. We expect that further improvements in the OMI NO2 and SO2 products will allow more robust quantification of long-term trends in global air quality.

SO2↗

The Diversity of Anomalous HEDs: Isotopic Constraints on the Connection of EET 92023, GRA 98098, and Dhofar 700 With Vesta

The possibility for multiple parent bodies, instead of a common parent body of Vesta, for eucrites has been suggested based on the variable oxygen isotopic composition observed in some eucrites.. Recently, we added an extra dimension to the discussion based on the (epsilon)54Cr composition of the same eucrites with known (delta)17O to compare with the normal eucrites. The combined (delta)17O and (epsilon)54Cr isotope systematics for Pasamonte, PCA 91007, A-881394, and Ibitira indicate their likely origin from multiple different parent bodies than the normal eucrites. Often the qualifier anomalous is used to identify HEDs with (delta)17O values that deviate significantly (>3(sigma)) from the mean HED (delta)17O. However, variations in eucrites and diogenites also include unique geochemical characteristics such as bulk composition, trace element abundances, or volatile concentrations, in addition to (delta)17O. Here, we investigate three such geochemically anomalous HEDs: Elephant Moraine (EET) 92023, Graves Nunataks (GRA) 98098, and Dhofar 700. In addition, to verify the homogeneity of (epsilon)54Cr observed for normal HEDs thus far, a set of seven eucrites and diogenites considered normal samples were also investigated.

Sanborn, M. E.↗

Ultrafast High Accuracy PCRTM_SOLAR Model for Cloudy Atmosphere

An ultrafast high accuracy PCRTM_SOLAR model is developed based on PCA compression and principal component-based radiative transfer model (PCRTM). A fast algorithm for simulation of multi-scattering properties of cloud and/or aerosols is integrated into the fast infrared PCRTM. We completed radiance simulation and training for instruments, such as IASI, AIRS, CrIS, NASTI and SHIS, under diverse conditions. The new model is 5 orders faster than 52-stream DISORT with very high accuracy for cloudy sky radiative transfer simulation. It is suitable for hyperspectral remote data assimilation and cloudy sky retrievals.

Yang, Qiguang↗

A Global Catalogue of Large SO2 Sources and Emissions Derived from the Ozone Monitoring Instrument

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr(exp -1) to more than 4000 kt yr(exp -1) of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005- 2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30% of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80% over the 2005-2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr(exp -1) and not detected by OMI.

Emissions↗

Thermal Inspection of a Composite Fuselage Section Using a Fixed Eigenvector Principal Component Analysis Method

A composite fuselage aircraft forward section was inspected with flash thermography. The fuselage section is 24 feet long and approximately 8 feet in diameter. The structure is primarily configured with a composite sandwich structure of carbon fiber face sheets with a Nomex(Trademark) honeycomb core. The outer surface area was inspected. The thermal data consisted of 477 data sets totaling in size of over 227 Gigabytes. Principal component analysis (PCA) was used to process the data sets for substructure and defect detection. A fixed eigenvector approach using a global covariance matrix was used and compared to a varying eigenvector approach. The fixed eigenvector approach was demonstrated to be a practical analysis method for the detection and interpretation of various defects such as paint thickness variation, possible water intrusion damage, and delamination damage. In addition, inspection considerations are discussed including coordinate system layout, manipulation of the fuselage section, and the manual scanning technique used for full coverage.

Zalameda, Joseph N.↗

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗