Hyperspectral Feature Detection Onboard the Earth Observing One Spacecraft
No abstract available
hyperspectral imagery↗
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Results for “Hyperspectral imaging”
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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Status of the Prototype HyspIRI Thermal Infrared Radiometer (PHyTIR) for the HyspIRI TIR Instrument Concept
No abstract available
Hyperspectral Infrared Imager (HyspIRI)↗
Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar
Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.
Michael Alonzo↗
The Characterization of a DIRSIG Simulation Environment to Support the Inter-Calibration of Spaceborne Sensors
Climate change studies require long-term, continuous records that extend beyond the lifetime, and the temporal resolution, of a single remote sensing satellite sensor. The inter-calibration of spaceborne sensors is therefore desired to provide spatially, spectrally, and temporally homogeneous datasets. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool is a first principle-based synthetic image generation model that has the potential to characterize the parameters that impact the accuracy of the inter-calibration of spaceborne sensors. To demonstrate the potential utility of the model, we compare the radiance observed in real image data to the radiance observed in simulated image from DIRSIG. In the present work, a synthetic landscape of the Algodones Sand Dunes System is created. The terrain is facetized using a 2-meter digital elevation model generated from NASA Goddard's LiDAR, Hyperspectral, and Thermal (G-LiHT) imager. The material spectra are assigned using hyperspectral measurements of sand collected from the Algodones Sand Dunes System. Lastly, the bidirectional reflectance distribution function (BRDF) properties are assigned to the modeled terrain using the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product in conjunction with DIRSIG's Ross-Li capability. The results of this work indicate that DIRSIG is in good agreement with real image data. The potential sources of residual error are identified and the possibilities for future work are discussed..
Ambeau, Brittany L.↗
Wide‐Field Bond Quality Evaluation Using Frequency Domain Thermoreflectance with Deep Neural Network Feature Reconstruction
Heterogeneous integration of microelectronic components provides a pathway to improve circuit/component performance; however, this comes with assembly challenges, in particular due to complex interfaces via subsurface bump bonds. The ability of these bonds to transmit electrical signals and conduct heat to the carrier substrate limits component performance. In this work, hyperspectral frequency‐domain thermoreflectance (FDTR) imaging is demonstrated as a robust technique for evaluating the quality of subsurface indium bump bonds in a surrogate microelectronic sample. By performing microscale FDTR imaging with coarse motion image stitching, thermal phase maps that cover a 4 mm by 4 mm field‐of‐view with subsurface feature sensitivity at depths greater than 50 µm are obtained. The resulting FDTR hyperspectral data contains more than three million pixels and reveal the quality of subsurface microbump arrays. Wide‐field analysis of bonded versus gap regions is enabled by deep neural network feature reconstruction, that after training, rapidly provides an interpretable representation of bond quality. Utility of noisy higher frequency FDTR phase maps, i.e., near the computationally predicted sensing depth limit, results in an average prediction error of 11%. Taken together, FDTR with neural network‐based analysis demonstrates subsurface bond monitoring at length scales relevant for heterogeneously integrated microelectronics.
FDTR↗
Sharp, high numerical aperture (NA), nanoimprinted bare pyramid probe for optical mapping
The ability to correlate optical hyperspectral mapping and high resolution topographic imaging is critically important to gain deep insight into the structure–function relationship of nanomaterial systems. Scanning near-field optical microscopy can achieve this goal, but at the cost of significant effort in probe fabrication and experimental expertise. To overcome these two limitations, we have developed a low-cost and high-throughput nanoimprinting technique to integrate a sharp pyramid structure on the end facet of a single-mode fiber that can be scanned with a simple tuning-fork technique. The nanoimprinted pyramid has two main features: (1) a large taper angle (~70°), which determines the far-field confinement at the tip, resulting in a spatial resolution of 275 nm, an effective numerical aperture of 1.06, and (2) a sharp apex with a radius of curvature of ~20 nm, which enables high resolution topographic imaging. Optical performance is demonstrated through evanescent field distribution mapping of a plasmonic nanogroove sample, followed by hyperspectral photoluminescence mapping of nanocrystals using a fiber-in-fiber-out light coupling mode. Through comparative photoluminescence mapping on 2D monolayers, we also show a threefold improvement in spatial resolution over chemically etched fibers. These results show that the bare nanoimprinted near-field probes provide simple access to spectromicroscopy correlated with high resolution topographic mapping and have the potential to advance reproducible fiber-tip-based scanning near-field microscopy.
47 OTHER INSTRUMENTATION↗
Chemical and Mineralogical Characterization of a Hematite-bearing Ridge on Mauna Kea, Hawaii: A Potential Mineralogical Process Analog for the Mount Sharp Hematite Ridge
The Mars Science Laboratory (MSL) rover Curiosity landed in Gale Crater in August 2012 and is currently roving towards the layered central mound known as Mount Sharp [1]. Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) hyperspectral data indicate Mount Sharp contains an ~5 km stratigraphic sequence including Fe-Mg smectites, hematite, and hydrated sulfates in the lower layers separated by an unconformity from the overlying anhydrous strata [1,2,3]. Hematite was initially detected in CRISM data to occur in the lower sulfate layers on the north side of the mound [2]. [3] further mapped a distinct hematite detection occurring as part of a ~200 m wide ridge that extends ~6.5 km NE-SW, approximately parallel with the base of Mount Sharp. It is likely a target for in-situ analyses by Curiosity. We document here the occurrence of a stratum of hematite-bearing breccia that is exposed on the Puu Poliahu cinder cone near the summit of Mauna Kea volcano (Hawaii) (Fig.1). The stratum is more resistant to weathering than surrounding material, giving it the appearance of a ridge. The Mauna Kea hematite ridge is thus arguably a potential terrestrial mineralogical and process analog for the Gale Crater hematite ridge. We are acquiring a variety of chemical and mineralogical data on the Mauna Kea samples, with a focus on the chemical and mineralogical information already available or planned for the Gale hematite ridge.
Graff, T. G.↗
Longitudinal Single‐Cell Imaging of Engineered Strains with Stimulated Raman Scattering to Characterize Heterogeneity in Fatty Acid Production
Abstract Understanding metabolic heterogeneity is critical for optimizing microbial production of valuable chemicals, but requires tools that can quantify metabolites at the single‐cell level over time. Here, longitudinal hyperspectral stimulated Raman scattering (SRS) chemical imaging is developed to directly visualize free fatty acids in engineered Escherichia coli over many cell cycles. Compositional analysis is also developed to estimate the chain length and unsaturation of the fatty acids in living cells. This method reveals substantial heterogeneity in fatty acid production among and within colonies that emerges over the course of many generations. Interestingly, the strains display distinct types of production heterogeneity in an enzyme‐dependent manner. By pairing time‐lapse and SRS imaging, the relationship between growth and production at the single‐cell level are examined. The results demonstrate that cell‐to‐cell production heterogeneity is pervasive and provides a means to link single‐cell and population‐level production.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Fast Hyperspectral Neutron Tomography
Hyperspectral neutron computed tomography is a tomographic imaging technique in which thousands of wavelength-specific neutron radiographs are measured for each tomographic view. In conventional hyperspectral reconstruction, data from each neutron wavelength bin are reconstructed separately, which is extremely time-consuming. These reconstructions often suffer from poor quality due to low signal-to-noise ratios. Consequently, material decomposition based on these reconstructions tends to produce inaccurate estimates of the material spectra and erroneous volumetric material separation. In this paper, we present two novel algorithms for processing hyperspectral neutron data: fast hyperspectral reconstruction and fast material decomposition. Both algorithms rely on a subspace decomposition procedure that transforms hyperspectral views into low-dimensional projection views within an intermediate subspace, where tomographic reconstruction is performed. The use of subspace decomposition dramatically reduces reconstruction time while reducing both noise and reconstruction artifacts. We apply our algorithms to both simulated and measured neutron data and demonstrate that they reduce computation and improve the quality of the results relative to conventional methods.
Chowdhury, Mohammad Samin Nur [Purdue University]↗
New York Ecological Forecasting: Utilizing NASA Earth Observations to Map Ash Distribution and Inform Emerald Ash Borer Control
Since their first sightings in the U.S. in 2002, emerald ash borer beetles (Agrilus planipennis; EAB) have killed millions of native ash (Fraxinus spp.) trees across 35 states. Infected ash stands frequently exhibit complete mortality, with the predicted result being the functional extinction of native ash in U.S. forests. In August of 2020, EAB was discovered in the 6.1-million-acre Adirondack Park. The team’s partners at the Adirondack Park Invasive Plant Program (APIPP) desired ash tree distribution and EAB susceptibility information to help improve EAB bio-control efficiency and apply the methodology to future invasive programs. To assist, the team mapped ash tree distribution using NASA Earth observations from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Shuttle Radar Topography Mission (SRTM), along with hyperspectral imagery from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data from the Monitoring and Managing Ash (MaMA) project, iMapInvasives and iNaturalist databases, and the New York State Department of Environmental Conservation (NYSDEC) provided ground truthing for mapping and modeling. Results indicate that for ash detection, the team’s Spectral Angle Mapping (SAM) hyperspectral classification is slightly more sensitive but less accurate than multispectral Random Forest (RF) classification, though neither method was above a ~20% detection rate. End products include maps of ash extent derived from both imagery types, a model forecasting future spread scenarios based on current EAB presence, and outreach materials. These products inform APIPP’s management decisions and facilitate public awareness of EAB’s threat to communities within the region.
Liam Megraw↗
Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites
This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.
Raut, Bipin↗
Quantifying Mangrove Canopy Regrowth and Recovery After Hurricane Irma With Large-Scale Repeat Airborne Lidar in the Florida Everglades
Hurricane Irma caused significant damages to mangrove forested wetlands in south Florida, including defoliation, tree snapping, and uprooting. Previous studies have used optical satellite imagery to estimate large-scale forest disturbance and resilience patterns. However, satellite images alone cannot provide measurements of vertical mangrove structure. In this study, we used dense point cloud data collected by NASA Goddard’s LiDAR, Hyperspectral, and Thermal (G-LiHT) airborne imager before (March 2017) and after (December 2017 and March 2020) Hurricane Irma to quantify the recovery, or lack thereof, of the three-dimensional (3D) mangrove forest structure. Recent resilience and vulnerability models developed from Landsat time series following the storm were used to group the lidar data into distinct disturbance-recovery classes. We then analyzed lidar-based forest canopy within each of the recovery classes to test a suite of forest structural characteristics. Our results indicate that 77.0 % of the survey area experienced canopy height loss three months after Hurricane Irma, whereby the majority of canopy height loss occurred in areas with the tallest mangrove forests (i.e., 15–25 m tall). Our analysis shows that the mangrove canopy height in South Florida increased by an average 0.26 m from December 2017 to March 2020, with most of the forest (84.7 % of the survey area) experiencing canopy height regrowth. However, only 38.1 % of the survey area has recovered to pre-storm canopy height. The distribution of canopy height was significantly altered by Hurricane Irma in the low and intermediate resilience classes, but were not significantly different 2.5 years later. Indeed, in areas of low resilience, little to no vertical change has occurred suggesting the absence of canopy regrowth and natural regeneration. Conversely, mangroves in high resilience class, which are dominated by shorter canopies (<5 m), were not heavily damaged by the storm and have maintained the same structural attributes as those before Hurricane Irma. Our findings highlight that hurricane disturbances significantly alter mangrove forest canopy structure, but recovery of vertical structure varies by resilience classes, species composition, and canopy height.
Lidar↗
Fallon, NV FORGE well 21-31 Lithology, Mineral, Image Log, and Injection Test data
Attached is 5 datasets collected from Fallon FORGE well 21-31 located in Churchill County, Nevada collected and interpreted between Feburary 2018-January 2020. This submission includes 1) new lithology interpretation derived from petrographic thin sections of sidewall cores taken from well 21-31, 2) X-Ray Diffraction (XRD) interpretation of sidewall cores and cuttings from well 21-31, 3) Hyperspectral (Short-Wave Infrared and Long-Wave Infrared) interpretation of cuttings and sidewall cores from well 21-31, 4) Interpretation of Formation MicroImager (FMI) and Borehole Televiewer (BHTV) image logs for natural fractures and induced structures for well 21-31, 5) Injection test data and associated analytical model parameters from well testing of well 21-31.
15 GEOTHERMAL ENERGY↗
A Deep Neural Network for Achieving Spectrally Consistent and Seamless Infrared Radiance Measurements Across Geostationary Satellite Domains
The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides the scientific community with observed top-of-atmosphere (TOA) shortwave and longwave fluxes for climate monitoring and climate model validation. To achieve this goal, CERES relies on TOA broadband fluxes derived from geostationary satellite (GEO) imagery to account for the diurnal flux variations between the CERES observation intervals. Consistent global flux derivation depends on accurate and consistent cloud retrievals. Scene-dependent spectral measurement inconsistency of the instruments that make up the contiguous ring of GEO observations (GEO-Ring), as well as limb darkening effects, can cause discontinuities in derived cloud properties and radiative fluxes at the boundaries of adjacent imager domains. Although the algorithms utilize radiative transfer models to account for instrument-band-dependent atmospheric correction and viewing zenith angle (VZA) dependency, small discontinuities may persist due to uncertainties inherent to the multiple imager-specific algorithms. Furthermore, while hyperspectral-instrument-based spectral band adjustment factors may effectively account for spectrally induced bias, they are less effective at reducing variance owed to the specific composition of the viewed scene, which is challenging to robustly characterize. As such, this article highlights the use of a deep neural network (DNN) to resolve spectral-and VZA-induced biases between GEO-Ring imagers. The DNN uses available infrared (IR) channels from the GEO instruments, along with viewing and solar illumination geometry, to estimate homogenized, VIIRS-like IR radiances for use in the GEO cloud algorithm. This approach is effective at mitigating scene-dependent spectral variance and VZA dependency, resulting in consistent radiance measurements across the GEO-Ring, thereby leading toward a more seamless global cloud assessment.
deep learning↗
Geologic Validation of EO-1 Hyperion using AVIRIS
Over the last few years, the field of imaging spectrometry has grown rapidly as new instruments and analysis techniques have been developed. The launch of Hyperion as part of NASA's EO-1 program represents a significant landmark in the progression of the technology; the first spaceborne imaging spectrometry system. AIG, in cooperation with CSIRO, is evaluating, validating, and demonstrating use of EO-1 Hyperion hyperspectral data for geologic applications. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) plays a pivotal role in this effort. This manuscript describes the geologic test sites being used and progress on preparing 'ground truth' for anticipated Hyperion data collects using AVIRIS data. Preliminary findings are extrapolated to predict the effect of instrument characteristics and performance on geologic mapping using Hyperion.
Kruse, F. A.↗
Depth Thermography: Noninvasive 3D Temperature Profiling Using Infrared Thermal Emission
Here, we introduce a technique based on infrared thermal emission, termed depth thermography, that can remotely measure the temperature distribution beneath the surface of certain objects. Depth thermography utilizes the thermal-emission spectrum in the semitransparent spectral region of the target object to extract its temperature as a function of depth, in contrast with conventional thermography, which uses the spectrally integrated thermally emitted power to measure the surface temperature. Coupled with two-dimensional imaging, for example using an infrared hyperspectral camera or scanning a single-pixel spectrometer, this technique can yield volumetric temperature distributions. Furthermore, we carried out a proof-of-concept experiment on an asymmetrically heated fused-silica window, extracting the temperature distribution throughout the sample. Depth thermography may enable noncontact volumetric temperature measurements of microscopic objects such as multilayer electronic devices or macroscopic volumes of liquids and gases, as well as simultaneous all-optical measurements of optical and thermal properties of materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Patterns and controls on island‐wide aboveground biomass accumulation in second‐growth forests of Puerto Rico
Abstract Understanding the heterogeneity of biomass accumulation in second‐growth tropical forests following land use abandonment is important for informing ecosystem carbon models and forest restoration efforts. There is an urgent need for a broad sample of second‐growth forests to enhance our knowledge of carbon accumulation in human‐dominated landscapes, especially for older forests. Puerto Rico has predominantly second‐growth forests, ranging in age from approximately 25 to more than 80 years. We used an island‐wide sample of airborne lidar from the NASA Goddard Lidar, Hyperspectral, and Thermal (G‐LiHT) Airborne Imager collected on March 2017, forest inventory data, and data on forest age, precipitation, soils, and land use to estimate aboveground biomass stocks in moist and wet, second‐growth tropical forests. Biomass accumulation rates in Puerto Rico were lower, on average, than in other Neotropical forests. Median biomass across >16,700 ha of older second‐growth forests was 105 Mg ha −1 , and sampled biomass rarely surpassed 250 Mg ha −1 . Differences in biomass by age were large and persistent across different substrates and land uses, with a plateau in the pattern of island‐wide biomass accumulation after about 33 years. A spatial regression model showed that multiple factors were related to biomass accumulation, including time since abandonment, geologic substrate, past land use as coffee or pasture, precipitation, topographic wetness index, and slope. Our findings have important consequences for the total carbon storage and expected climate mitigation benefits of large‐scale reforestation efforts, and highlight the value of airborne lidar for quantifying biomass variability in complex tropical landscapes. Abstract in Spanish is available with online material.
54 ENVIRONMENTAL SCIENCES↗
Earth observational research using multistage EOS-like data
This grant is funded as a part of a program in which both research and educational impact are intended. Research work under this grant is directed at the understanding and use of future hyperspectral data such as that from imaging spectrometers. Specifically, the objectives of the work are (1) to prepare suitable means for analyzing data from sensors which have large numbers of spectral bands, (2) to advance the fundamental understanding of the manner in which soils and vegetative materials reflect high spectral resolution optical wavelength radiation, and (3) to maximize the impact of the results on the educational community. Over the life of the grant, the work has thus involved basic Earth science research and information system technique understanding and development in a mutually supportive way, however, more recently it has become necessary to focus the work primarily on areas (1) and (3). During the last year, the level of effort on this grant has been reduced to half its previous value. We have also been advised that this grant will end with the current year, thus this will be the penultimate semiannual progress summary.
Johannsen, C. J.↗