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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Terahertz-Regime, Micro-VEDs: Evaluation of Micromachined TWT Conceptual Designs

Summary form only given. The Terahertz (THz) region of the electromagnetic spectrum (approx.300-3000 GHz) has enormous potential for high-data-rate communications, spectroscopy, astronomy, space research, medicine, biology, surveillance, remote sensing, industrial process control, etc. The most critical roadblock to full exploitation of the THz band is lack of coherent radiation sources that are powerful (0.01-10.0 W continuous wave), efficient (>1 %), frequency agile (instantaneously tunable over 1% bandwidths or more), reliable, and relatively inexpensive. Micro-machined Vacuum Electron Devices (micro-VEDs) represent a promising solution. We describe prospects for miniature, THz-regime TWTs fabricated using micromachining techniques. Several approx.600 GHz conceptual designs are compared. Their expected performance has been analyzed using SD, 2.51), and 3D TWT codes. A folded waveguide (FWG) TWT forward-wave amplifier design is presented based on a Northrop Grumman (NGC) optimized design procedure. This conceptual device is compared to the simulated performance of a novel, micro-VED helix TWT. Conceptual FWG TWT backward-wave amplifiers and oscillators are also discussed. A scaled (100 GHz) FWG TWT operating at a relatively low voltage (-12 kV) is under development at NGC. Also, actual-size micromachining experiments are planned to evaluate the feasibility of arrays of micro-VED TWTs. Progress and results of these efforts are described. This work was supported, in part by AFOSR, ONR, and NSF.

Booske, John H.↗
NASA NTRS2001

Micromachined TWTs for THz Radiation Sources

The Terahertz (THz) region of the electromagnetic spectrum (about 300 - 3000 GHz in frequency or about 0.1 - 1 mm free space wavelength) has enormous potential for high-data-rate communications, spectroscopy, astronomy, space research, medicine, biology, surveillance, remote sensing, industrial process control, etc. It has been characterized as the most scientifically rich, yet under-utilized, region of the electromagnetic spectrum. The most critical roadblock to full exploitation of the THz band is lack of coherent radiation sources that are powerful (0.001 - 1.0 W continuous wave), efficient (> 1%), frequency agile (instantaneously tunable over 1% bandwidths or more), reliable, and comparatively inexpensive. To develop vacuum electron device (VED) radiation sources satisfying these requirements, fabrication and packaging approaches must be heavily considered to minimize costs, in addition to the basic interaction physics and circuit design. To minimize size of the prime power supply, beam voltage must be minimized, preferably 10 kV. Solid state sources satisfy the low voltage requirement, but are many orders of magnitude below power, efficiency, and bandwidth requirements. On the other hand, typical fast-wave VED sources in this regime (e.g., gyrotrons, FELs) tend to be large, expensive, high voltage and very high power devices unsuitable for most of the applications cited above. VEDs based on grating or inter-digital (ID) circuits have been researched and developed. However, achieving forward-wave amplifier operation with instantaneous fractional bandwidths > 1% is problematic for these devices with low-energy (< 15 kV) electron beams. Moreover, the interaction impedance is quite low unless the beam-circuit spacing is kept particularly narrow, often leading to significant beam interception. One solution to satisfy the THz source requirements mentioned above is to develop micromachined VEDs, or "micro-VEDs". Among other benefits, micro-machining technologies provide superior high frequency wall conductivity as a result of superior surface smoothness compared with conventional mechanical or electric discharge machining approaches. Micro-VED technologies are already being applied to the development of millimeter-wave klystrons at Stanford Linear Accelerator Center and submillimeter-wave klystrons at the University of Leeds. We are investigating the use of micro-machining technologies to develop THz regime TWTs, with emphasis on folded-waveguide TWTs. The folded-waveguide TWT (FW-TWT) has several features that make it attractive for THz-regime micro-VED applications. It is a relatively simple circuit to design and fabricate, it is amenable to precision pattern replication by micro-machining, and it is has been demonstrated capable of forward-wave amplification with appreciable bandwidth. We are conducting experimental and computational studies of micro-VED FW-TWTs to examine their feasibility for applications at frequencies from 200 - 1000 GHz.

Booske, John H.↗
NASA NTRS2018

Next-Generation Sensing Technologies for Exploring Ocean Worlds

Dr. Ved Chirayath's plenary presentation will highlight two instrument technologies he invented at NASA including Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, and MiDAR (Multispectral Imaging, Detection and Active Reflectance), a next-generation active hyperspectral remote sensing and optical communications instrument. Fluid Lensing has been used to provide the first 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft, and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an object's non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Finally, Chirayath will present preliminary results from NASA NeMO-Net (Neural Multi-Modal Observation and Training Network), the first neural network for global coral reef classification using fluid lensing and MiDAR.

Technologies↗
NASA NTRS2020

NASA Next-Generation Sensing Technologies for Earth & Planetary Science

Dr. Ved Chirayath's seminar will highlight new sensing technologies and airborne platforms he is developing for Earth & Space Science as director of the NASA Silicon Valley Laboratory for Advanced Sensing. His talk will feature recent work on Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, MiDAR, a next-generation active hyperspectral remote sensing and optical communications instrument, airborne gas sensing of multipollutant combustion sources, and a plasma actuated unmanned aerial vehicle (UAV) that utilized high-voltage dielectric discharge devices to achieve the first plasma controlled flight in history.Fluid Lensing and the NASA FluidCam instrument have been used extensively to provide distortion-free 3D multispectral imagery from UAVs of shallow marine systems around the world. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments as an analog for future Ocean Worlds robotic exploration missions. Finally, Chirayath will present preliminary results from NeMO-Net, a supercomputer-based neural network that uses a citizen science video game for global multimodal coral reef benthic habitat mapping, fusing remote sensing data from Fluid Lensing, MiDAR, NASA's Earth Observing System, and commercial satellites, to better understand the present and past dynamics of shallow marine systems. Together, these maturing technologies present promising new ways in which to explore terrestrial, marine, and aerial systems on Earth, and, ultimately, aid in the search for extraterrestrial life within our solar system and beyond.

Chirayath, Ved↗
NASA NTRS1983

An evaluation of the accuracy of some radar wind profiling techniques

Major advances in Doppler radar measurement in optically clear air have made it feasible to monitor radial velocities in the troposphere and lower stratosphere. For most applications the three dimensional wind vector is monitored rather than the radial velocity. Measurement of the wind vector with a single radar can be made assuming a spatially linear, time invariant wind field. The components and derivatives of the wind are estimated by the parameters of a linear regression of the radial velocities on functions of their spatial locations. The accuracy of the wind measurement thus depends on the locations of the radial velocities. The suitability is evaluated of some of the common retrieval techniques for simultaneous measurement of both the vertical and horizontal wind components. The techniques considered for study are fixed beam, azimuthal scanning (VAD) and elevation scanning (VED).

Koscielny, A. J.↗
NASA NTRS1993

Orbiting transmitter and antenna for spaceborne communications at ELF/VLF to submerged submarines

An orbital emplacement for the transmitter and the antenna of a communications link at ELF (30 to 300 Hz) and VLF (3 kHz to 30 kHz) to submerged submarines has been considered since the very inception of the space age. However, only recently has space technology reached a sufficient level of maturity for system designers to undertake serious studies of this link configuration. The optimistic outlook stems from recent space technology developments, such as the design and construction by NASA of long orbiting tethers, and the testing, onboard Shuttle Orbiter ATLANTIS, of the first spaceborne 20 km metal wire. This is known as the Tethered Satellite System-1 (TSS-1), a space mission that might be possibly followed by other flights, with tether lengths that could reach 100 km. Once deployed at a height of, say, 300 km, from a Shuttle Orbiter, or from another suitable platform, a long, thin tether aligns itself along the local vertical by virtue of the gradient of the Earth gravity field. If made of metal, the tether can function as a VED (Vertical Electric Dipole) transmitting antenna at ELF and VLF.

Bannister, P. R.↗
NASA NTRS2002

A Low Cost Traveling Wave Tube for Wireless Communications

Demand for high data rate wireless communications is pushing up amplifier power, bandwidth and frequency requirements. Some systems are using vacuum electron devices again because solid-state power amplifiers are not able to efficiently meet the new requirements. The traveling wave tube is the VED of choice because of its excellent broadband capability as well as high power efficiency and frequency. But TWTs are very expensive on a per watt basis below about 200 watts of output power. We propose a new traveling wave tube that utilizes cathode ray tube construction technology and electrostatic focusing. We believe the tube can be built in quantity for under $1,000 each. We discuss several traveling wave tube slow wave circuits that lend themselves to the new construction. We will present modeling results and data on prototype devices.

Vancil, Bernard Kenneth↗
NASA NTRS2008

Realism Assessment of Sonic Boom Simulators

Developments in small supersonic aircraft design are predicted to result in low-intensity sonic booms. Booms generated by current aircraft are similar to those that led to the ban on commercial supersonic fli ght over the US, so are unsuitable for parametric studies of psychoac oustic response to low-intensity booms. Therefore, simulators have be en used to study the impact of predicted low-intensity sonic booms. H owever, simulators have been criticized because, when simulating conv entional-level booms, the sounds were observed to be unrealistic by p eople experienced in listening to sonic booms. Thus, two studies were conducted to measure the perceived realism of three sonic boom simul ators. Experienced listeners rated the realism of conventional sonic boom signatures when played in these simulators. The effects on percei ved realism of factors such as duration of post-boom noise, exclusion of very low frequency components, inclusion of ground reflections, a nd type of simulator were examined. Duration of post-boom noise was f ound to have a strong effect on perceived realism, while type of simu lator had a weak effect. It was determined that post-boom noise had t o be at least 1.5 seconds long for the sound to be rated very realist ic. Loudness level did not affect realism for the range of sounds pla yed in the tests (80-93 dB ASEL).

Sullivan, Brenda M.↗
NASA NTRS2016

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 world's 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

Fluid Lensing and Applications to Remote Sensing of Aquatic Environments

The use of fluid lensing technology on UAVs is presented as a novel means for 3D imaging of aquatic ecosystems from above the water's surface at the centimeter scale. Preliminary results are presented from airborne fluid lensing campaigns conducted over the coral reefs of Ofu Island, American Samoa (2013) and the stromatolite reefs of Shark Bay, Western Australia (2014), covering a combined area of 15km2. These reef ecosystems were revealed with centimetre-scale 2D resolution, and an accompanying 3D bathymetry model was derived using fluid lensing, Structure from Motion and UAV position data. Data products were validated from in-situ survey methods including underwater calibration targets, depth measurements and millimetre-scale high-dynamic range gigapixel photogrammetry. Fluid lensing is an experimental technology that uses water transmitting wavelengths to passively image underwater objects at high-resolution by exploiting time-varying optical lensing events caused by surface waves. Fluid lensing data are captured from low-altitude, cost-effective electric UAVs to achieve multispectral imagery and bathymetry models at the centimetre scale over regional areas. As a passive system, fluid lensing is presently limited by signal-to-noise ratio and water column inherent optical properties to approximately 10 m depth over visible wavelengths in clear waters. The datasets derived from fluid lensing present the first centimetre-scale images of a reef acquired from above the ocean surface, without wave distortion. The 3D multispectral data distinguish coral, fish and invertebrates in American Samoa, and reveal previously undocumented, morphologically distinct, stromatolite structures in Shark Bay. These findings suggest fluid lensing and multirotor electric drones represent a promising advance in the remote sensing of aquatic environments at the centimetre scale, or 'reef scale' relevant to the conservation of reef ecosystems. Pending further development and validation of fluid lensing methods, these technologies present a solution for large-scale 3D surveys of shallow aquatic habitats with centimetre-scale spatial resolution and hourly temporal sampling.

Aquatic↗
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 NTRS2017

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

NeMO=NET↗
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 NTRS2018

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.

fluid lensing↗
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↗
NASA NTRS2017

Weathering the Storm: Unmanned Aircraft Systems in the Maritime, Atmospheric and Polar Environments

Remotely piloted aircraft (RPA) have the potential to revolutionize local to regional data collection for geophysicists as platform and payload size decrease while aircraft capabilities increase. In particular, data from RPAs combine high-resolution imagery available from low flight elevations with comprehensive areal coverage, unattainable from ground investigations and difficult to acquire from manned aircraft due to budgetary and logistical costs. Low flight elevations are particularly important for detecting signals that decay exponentially with distance, such as electromagnetic fields. Onboard data processing coupled with high-bandwidth telemetry open up opportunities for real-time and near real-time data processing, producing more efficient flight plans through the use of payload-directed flight, machine learning and autonomous systems. Such applications not only strive to enhance data collection, but also enable novel sensing modalities and temporal resolution. NASAs Airborne Science Program has been refining the capabilities and applications of RPA in support of satellite calibration and data product validation for several decades. In this paper, we describe current platforms, payloads, and onboard data systems available to the research community. Case studies include Fluid Lensing for littoral zone 3D mapping, structure from motion for terrestrial 3D multispectral imaging, and airborne magnetometry on medium and small RPAs.

data collection↗
NASA NTRS2018

NASA Fluid Lensing & MiDAR - Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

Piti's Tepungan Bay and Tumon Bay, two of five marine preserves in Guam, have not been mapped to a level of detail sufficient to support proposed management strategies. This project addresses this gap by providing high resolution maps to promote sustainable, responsible use of the area while protecting natural resources. Dr. Chirayath, a research scientist at the NASA Ames Laboratory, developed a theoretical model and algorithm called 'Fluid Lensing'. Fluid lensing removes optical distortions caused by moving water, improving the clarity of the images taken of the corals below the surface. We will also be using MiDAR, a next-generation remote sensing instrument that provides real-time multispectral video using an array of LED emitters coupled with NASA's FluidCam Imaging System, which may assist Guam's coral reef response team in understanding the severity and magnitude of coral bleaching events. This project will produce a 3D orthorectified model of the shallow water coral reef ecosystems in Tumon Bay and Piti marine preserves. These 3D models may be printed, creating a tactile diorama and increasing understanding of coral reefs among various audiences, including key decision makers. More importantly, the final data products can enable accurate and quantitative health assessment capabilities for coral reef ecosystems.

Aquatic↗
NASA NTRS2018

NASA NeMO-Net

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.

NASA↗