Vuong Ly

NASA Goddards LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager

The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (~1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT’s data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA’s Data and Information policy.

Architectural Analysis of Systems Based on the Publisher-Subscriber Style

Architectural styles impose constraints on both the topology and the interaction behavior of involved parties. In this paper, we propose an approach for analyzing implemented systems based on the publisher subscriber architectural style. From the style definition, we derive a set of reusable questions and show that some of them can be answered statically whereas others are best answered using dynamic analysis. The paper explains how the results of static analysis can be used to orchestrate dynamic analysis. The proposed method was successfully applied on the NASA’s Goddard Mission Services Evolution Center (GMSEC) software product line. The results show that the GMSEC has a) a novel reusable vendor-independent middleware abstraction layer that allows the NASA’s missions to configure the middleware of interest without changing the publishers’ or subscribers’ source code, and b) a high-priority bug due to behavioral discrepancies, which were eluded during testing and code reviews, among different implementations of the same APIs for different vendors.

Monitoring Orbital Precession of EO-1 Hyperion With Three Atmospheric Correction Models in the Libya-4 PICS

Spaceborne spectrometers require spectral-temporal stability characterization to aid in validation of derived data products. Earth Observation 1 (EO-1) began orbital precession in 2011 after exhausting onboard fuel resources. In the Libya-4 pseudoinvariant calibration site (PICS), this resulted in a progressive shift from a mean local equatorial crossing time of ~10:00 A.M. in 2011 to ~8:30 A.M. in late 2015. Here, we studied precession impacts to Hyperion surface reflectance products using three atmospheric correction approaches from 2004 to 2015. Combined difference estimates of surface reflectance were <;5% in the visible near infrared (VNIR) and <;10% for most of the shortwave infrared (SWIR). Combined coefficient of variation estimates in the VNIR ranged from 0.025 to 0.095, and in the SWIR it ranged from 0.025 to 0.06, excluding bands near atmospheric absorption features. Reflectances produced with different atmospheric models were correlated (R2) in VNIR from 0.25 to 0.94 and in SWIR from 0.12 to 0.88 (p <; 0.01). The uncertainties in all the models increased with a terrain slope up to 15° and selecting dune flats could reduce errors. We conclude that these data remain a valuable resource over this period for sensor intercalibration despite orbital decay.

Hyperion: The first global orbital spectrometer, earth observing-1 (EO-1) satellite (2000–2017)

In February 2017, the Earth Observing One (EO-1) satellite mission successfully completed sixteen years and three months of Earth imaging by its two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments have served as prototypes for new orbital sensors. Hyperion has provided the only available global sample of the Earths surface with: (i) passive optical mid-morning observations at moderate spatial resolution (30 m) to match the Landsat series; and (ii) spectral coverage over almost the full optical spectrum in 10 nm contiguous bands, in visible through shortwave infrared (VSWIR, 0.4–2.5 μm) wavelengths. Consequently, Hyperion is a heritage platform for future full-spectrum VSWIR orbital spectrometers, including the German mission, EnMAP (2019), and the NASA pre-Phase A (yet unscheduled) mission, the Hyperspectral InfraRed Imager (HyspIRI), defined by the 2007 Decadal Survey conducted by the US National Research Council. We provide an overview of the missions lifetime and Hyperions scientific and application accomplishments, including calibration & validation activities, data quality evaluations during end of mission precession changes to the orbit and overpass time, and the development of a user-friendly science quality archive.

The Matsu Wheel: a reanalysis framework for Earth satellite imagery in data commons

Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for the cloud-based processing of Earth satellite imagery and for detecting fires and floods to help support natural disaster detection and relief. We describe a framework for efficient analysis and reanalysis of large amounts of data called the Matsu “Wheel” and the analytics used to process hyperspectral data produced daily by NASA’s Earth Observing-1 (EO-1) satellite. The wheel is designed to be able to support scanning queries using cloud computing applications, such as Hadoop and Accumulo. A scanning query processes all, or most, of the data in a database or data repository. In contrast, standard queries typically process a relatively small percentage of the data. The wheel is a framework in which multiple scanning queries are grouped together and processed in turn, over chunks of data from the database or repository. Over time, the framework brings all data to each group of scanning queries. With this approach, contention and the overall time to process all scanning queries can be reduced. We describe our Wheel analytics, including an anomaly detector for rare spectral signatures or anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. The resultant products of the analytics are made accessible through an API for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.

Georegistration of Earth Observing-1 (EO-1) data using Global Land Survey (GLS) maps

The method presented in this paper utilizes Global Land Survey (GLS) maps to register Earth Observing-1 (EO-1) data, either using entire scenes or utilizing chips extracted from the GLS maps. The automated registration algorithm is based on the optimization of wavelet or wavelet-like features extracted from both reference and input image data. After testing the method on several ALI scenes, results and conclusions are presented.