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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Automated Segmentation and Classification of Coral Using Fluid Lensing from Unmanned Airborne Platforms

In recent years, there has been a growing interest among biologists in monitoring the short and long term health of the worlds coral reefs. The environmental impact of climate change poses a growing threat to these biologically diverse and fragile ecosystems, prompting scientists to use remote sensing platforms and computer vision algorithms to analyze shallow marine systems. In this study, we present a novel method for performing coral segmentation and classification from aerial data collected from small unmanned aerial vehicles (sUAV). Our method uses Fluid Lensing algorithms to remove and exploit strong optical distortions created along the air-fluid boundary to produce cm-scale resolution imagery of the ocean floor at depths up to 5 meters. A 3D model of the reef is reconstructed using structure from motion (SFM) algorithms, and the associated depth information is combined with multidimensional maximum a posteriori (MAP) estimation to separate organic from inorganic material and classify coral morphologies in the Fluid-Lensed transects. In this study, MAP estimation is performed using a set of manually classified 100 x 100 pixel training images to determine the most probable coral classification within an interrogated region of interest. Aerial footage of a coral reef was captured off the coast of American Samoa and used to test our proposed method. 90 x 20 meter transects of the Samoan coastline undergo automated classification and are manually segmented by a marine biologist for comparison, leading to success rates as high as 85. This method has broad applications for coastal remote sensing, and will provide marine biologists access to large swaths of high resolution, segmented coral imagery.

algorithms segments classifications↗
NASA NTRS2017

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↗
NASA NTRS1979

Applications of remote sensing for water quality and biological measurements in coastal waters

Potential applications of remote sensing technology to the study of coastal marine environments are reviewed, emphasizing water quality and biological measurements. Parameters measurable by airborne or spaceborne remote sensors include particulates, measured by visual or multispectral photography, chlorophyll a, measured by the Ocean Color Scanner or Coastal Zone Color Scanner, temperature distributions, by IR or microwave sensors, and salinity, by means of microwave radiometers. Research projects in which wide area synoptic or repetitive remote sensing can make a major contribution include the study of estuarine and continental shelf sediment transport dynamics, marine pollutant transport, marine phytoplankton dynamics and ocean fronts.

Johnson, R. W.↗
NASA NTRS1972

Basic research in meteorology and atmospheric physics

A survey is reported of methods for sounding the atmospheric temperature profile by remote measurements. The emphasis for this period was placed on sounding in the microwave region of the spectrum, sounding in cloudy atmosphere, and measuring sea temperatures remotely. Summaries of the research in the following areas are included: orbital detection of stratospheric aerosols, monthly precipitation charts for the world, determining planetary cloud structure by remote polarization measurement, analysis of Mariner 6 and 7 multicolor photometric photographs of Mars, and techniques for photometric detection of extrasolar planets.

Opstbaum, R.↗
NASA NTRS1975

LIFES: Laser Induced Fluorescence and Environmental Sensing

A laboratory investigation has been conducted to evaluate the detection and identification capabilities of laser induced fluorescence as a remote sensing technique for the marine environment. The relative merits of fluorescence parameters including emission and excitation profiles, intensity and lifetime measurements are discussed in relation to the identification of specific targets of the marine environment including crude oils, refined petroleum products, fish oils and algae. Temporal profiles displaying the variation of lifetime with emission wavelength have proven to add a new dimension of specificity and simplicity to the technique.

Houston, W. R.↗
NASA NTRS1989

A multi-sensor remote sensing approach for measuring primary production from space

It is proposed to develop a multi-sensor remote sensing method for computing marine primary productivity from space, based on the capability to measure the primary ocean variables which regulate photosynthesis. The three variables and the sensors which measure them are: (1) downwelling photosynthetically available irradiance, measured by the VISSR sensor on the GOES satellite, (2) sea-surface temperature from AVHRR on NOAA series satellites, and (3) chlorophyll-like pigment concentration from the Nimbus-7/CZCS sensor. These and other measured variables would be combined within empirical or analytical models to compute primary productivity. With this proposed capability of mapping primary productivity on a regional scale, we could begin realizing a more precise and accurate global assessment of its magnitude and variability. Applications would include supplementation and expansion on the horizontal scale of ship-acquired biological data, which is more accurate and which supplies the vertical components of the field, monitoring oceanic response to increased atmospheric carbon dioxide levels, correlation with observed sedimentation patterns and processes, and fisheries management.

Gautier, Catherine↗
NASA NTRS2017

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↗
NASA NTRS2018

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. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
DOE OSTI2025

RENEWABLE MARINE-SOURCE INTEGRATED HEAT PUMP FOR SPACE CONDITIONING, WATER HEATING, AND FRESH WATER HARVESTING IN REMOTE COASTAL AND ISLAND COMMUNITIES

US coastal and island communities have vulnerable energy infrastructure and high energy costs, which are exacerbated by climate change. A unique opportunity exists to use thermal energy from year-round mild temperature variations to satisfy the broad energy needs of these communities. The US Department of Energy’s Oak Ridge National Laboratory designed a small-scale marine-source integrated heat pump (MS-IHP) that, as a single appliance, combines space conditioning, dehumidification (including fresh water harvesting from humid air), and water heating for home and commercial building applications in remote coastal and island communities. The unique, innovative features of the MS-IHP offer improved efficiency and substantially reduced costs for space conditioning, dehumidification, and water heating by seamlessly integrating a heat pump, heat pump water heater, and dehumidifier by using a single compressor; optimal year-round space cooling and heating, dehumidification, water harvesting, and water heating; and improved thermal comfort and reliable operation without frosting and defrosting because of the ocean’s warm temperatures and high heat capacity. Preliminary testing was conducted by retrofitting a commercial heat pump, demonstrating an outperformed performance compared with traditional air-source heat pumps. The MS-IHP technology can help decarbonize energy systems in remote coastal and island communities.

Gao, Zhiming [ORNL] (ORCID:0000000271397995)↗
NASA NTRSDate not supplied

Potential Use of Spaceborne Differential Absorption Radar Measurements of Marine Surface Pressure to Improve Weather and Tropical Cyclone Forecasting

Differential absorption radar (DAR) has been identified as a method to remotely sense surface air pressure, particularly over the marine surface. Such an instrument would provide an estimate of the total atmospheric column oxygen content from which the surface air pressure can be calculated. NASA recently funded the development of a demonstrator instrument, the airborne Microwave Barometric Radar and Sounder (MBARS) to test and evaluate this retrieval. The potential impact of surface pressure observations from a spaceborne sensor is evaluated using the updated NASA/GMAO global Observing System Simulation Experiment framework. Initial experiments showing the impact of different orbital configurations of DAR surface pressure measurements on overall NWP will be shown, including Forecast Sensitivity Observation Impact metrics. Case studies of satellite-based surface air pressure retrievals on tropical cyclones will also be discussed.

DAR↗
NASA NTRS1982

The intercrater plains of Mercury and the Moon: Their nature, origin and role in terrestrial planet evolution. Remote sensing and physical data and the Moon

Imagery data from Mariner 10 and Lunar Orbiter IV form the major base of observations analyzed. But a variety of other information aids in constraining the composition and structure of the Moon and Mercury, and in particular, provides input to the problem of the nature and origin of their intercrater plains. This information for Mercury is remotely sensed from Earth or from the Mariner 10 spacecraft. Lunar data includes, of course, ground truth information from the Apollo landing sites. Since neither intercrater region was sampled, lunar and Mercurian data are similar in type and limitations. Constraints on surface and interior composition and structure are reviewed.

Leake, M. A.↗
NASA NTRS1992

Micro weather stations for in situ measurements in the Martian planetary boundary layer

Viking Lander meteorology measurements show that the Martian planetary boundary layer (PBL) has large diurnal and seasonal variations in pressure, wind velocity, relative humidity, and airborne dust loading. An even larger range of conditions was inferred from remote sensing observations acquired by the Mariner 9 and Viking orbiters. Numerical models indicate that these changes may be accompanied by dramatic vertical and horizontal wind shears (100 m/s/km) and rapid changes in the static stability. In-situ measurements from a relatively small number surface stations could yield global constraints on the Martian climate and atmospheric general circulation by providing ground truth for remote sensing instruments on orbiters. A more complete understanding of the meteorology of the PBL is an essential precursor to manned missions to Mars because this will be their working environment. In-situ measurements are needed for these studies because the spatial and temporal scales that characterize the important meteorological processes near the surface cannot be resolved from orbit. The Mars Environmental Survey (MESUR) Program will provide the first opportunity to deploy a network of surface weather stations for a comprehensive investigation of the Martian PBL. The feasibility and utility of a network of micro-weather stations for making in-situ meteorological measurements in the Martian PBL are assessed.

Crisp, D.↗
NASA NTRS2010

Understanding the Transport of Patagonian Dust and Its Influence on Marine Biological Activity in the South Atlantic Ocean

Modeling and remote sensing techniques were applied to examine the horizontal and vertical transport pathways of Patagonian dust and quantify the effect of soluble-iron- laden mineral dust deposition on marine primary productivity in the South Atlantic Ocean (SAO) surface waters. The global chemistry transport model GEOS-Chem, implemented with an iron dissolution scheme, was applied to evaluate the atmospheric transport and deposition of mineral dust and bioavailable iron during two dust outbreaks originating in the source regions of Patagonia. In addition to this "rapidly released" iron, offline calculations were also carried out to estimate the amount of bioavailable iron leached during the residence time of dust in the ocean mixed layer. Model simulations showed that the horizontal and vertical transport pathways of Patagonian dust plumes were largely influenced by the synoptic meteorological patterns of high and low pressure systems. Model-predicted horizontal and vertical transport pathways of Patagonian dust over the SAO were in reasonable agreement with remotely-sensed data. Comparison between remotely-sensed and offline calculated ocean surface chlorophyll-a concentrations indicated that, for the two dust outbreaks examined in this study, the deposition of bioavailable iron in the SAO through atmospheric pathways was insignificant. As the two dust transport episodes examined here represent typical outflows of mineral dust from South American sources, our study suggests that the atmospheric deposition of mineral dust is unlikely to induce large scale marine primary productivity and carbon sequestration in the South Atlantic sector of the Southern Ocean.

Johnson, Matthew↗
NASA NTRS1973

Mississippi Sound Remote Sensing Study

The Mississippi Sound Remote Sensing Study was initiated as part of the research program of the NASA Earth Resources Laboratory. The objective of this study is development of remote sensing techniques to study near-shore marine waters. Included within this general objective are the following: (1) evaluate existing techniques and instruments used for remote measurement of parameters of interest within these waters; (2) develop methods for interpretation of state-of-the-art remote sensing data which are most meaningful to an understanding of processes taking place within near-shore waters; (3) define hardware development requirements and/or system specifications; (4) develop a system combining data from remote and surface measurements which will most efficiently assess conditions in near-shore waters; (5) conduct projects in coordination with appropriate operating agencies to demonstrate applicability of this research to environmental and economic problems.

Atwell, B. H.↗
NASA NTRS1994

Gulf offshore satellite applications project (GOSAP) (ERS-1 Pilot Project PP-USA-1)

Project GOSAP is a multi organizational effort to determine how best to use remote sensing technology, and ERS-1 data in particular, to address offshore problems and operations faced by the exploration and marine engineering industries, in the Gulf of Mexico. Remotely sensed data integrated with sea truth are used to quantify meteorologic and oceanographic events, to detect and track ocean currents and gyres, to image the sea floor, map subsurface geology, or detect oil seeps from orbital altitudes. Participants are evaluating the potential for satellite based offshore exploration, ocean engineering, and environmental applications using combined satellite and airborne measurements constrained by real time sea truth.

Biegert, E. K.↗
NASA NTRS2016

Coastal High-resolution Observations and Remote Sensing of Ecosystems (C-HORSE)

Coastal benthic marine ecosystems, such as coral reefs, seagrass beds, and kelp forests are highly productive as well as ecologically and commercially important resources. These systems are vulnerable to degraded water quality due to coastal development, terrestrial run-off, and harmful algal blooms. Measurements of these features are important for understanding linkages with land-based sources of pollution and impacts to coastal ecosystems. Challenges for accurate remote sensing of coastal benthic (shallow water) ecosystems and water quality are complicated by atmospheric scattering/absorption (approximately 80+% of the signal), sun glint from the sea surface, and water column scattering (e.g., turbidity). Further, sensor challenges related to signal to noise (SNR) over optically dark targets as well as insufficient radiometric calibration thwart the value of coastal remotely-sensed data. Atmospheric correction of satellite and airborne remotely-sensed radiance data is crucial for deriving accurate water-leaving radiance in coastal waters. C-HORSE seeks to optimize coastal remote sensing measurements by using a novel airborne instrument suite that will bridge calibration, validation, and research capabilities of bio-optical measurements from the sea to the high altitude remote sensing platform. The primary goal of C-HORSE is to facilitate enhanced optical observations of coastal ecosystems using state of the art portable microradiometers with 19 targeted spectral channels and flight planning to optimize measurements further supporting current and future remote sensing missions.

Coasts↗
NASA NTRS1977

Mars surface properties observed by earth-based radar at 70-, 12.5-, and 3.8-cm wavelengths

In the present paper, the results are summarized of a study aimed at determining the small-scale surface properties of Mars (for the Viking project) from a thorough review of all available Martian radar data for the 1973 opposition. Analysis of the quasi-specular echo component showed changes in apparent reflectivity of at least 5 to 1. If attributed entirely to variations in surface material, these correspond to dielectric constants between 1.6 and 4.0. Values of rms surface slope on 1- to 100-m scales range from as low as 0.5 degrees in tablelands near Valles Marineris to more than 3.0 degrees in certain other areas. There is a weak correlation between the small-scale surface characteristics inferred from radar and those inferred from Mariner 9 images and other remote-sensing data sets.

Simpson, R. A.↗