Hampapuram K. Ramapriyan

Automated Matching of Pairs of SIR-B Images for Elevation Mapping

During the SIR-B mission in October 1984, a significant number of overlapping synthetic aperture radar (SAR) images of various ground areas was collected. This has offered the first opportunity to perform stereo analyses on images from space that cover large ground areas to determine elevation information. This paper presents the preliminary results of an investigation to obtain elevation data from stereo pairs of SIR-B images. First, the accuracy with which elevation information can be derived from SIR-B image pairs is evaluated theoretically. It is shown that elevation accuracy is a function of the slant range resolution, the incidence angles with which the stereo pair is obtained, the accuracies in spacecraft state estimation, and determination of corresponding pixels in the stereo pair. Next, a hierarchical method is developed to match the corresponding pixels. This method involves iterative removal of local distortions and correlations of pairs of local neighborhoods in the two images. Since it is necessary to perform the matching at every pixel in the image, it is very computationally intensive. Therefore, it has been implemented on the Massively Parallel Processor (MPP) at the Goddard Space Flight Center (GSFC). The MPPs speed permits two iterations of this technique to operate on a pair of 512 × 512 images within 7 s. Results of applying this algorithm to SIR-B images of Mount Shasta, CA, are shown. The matching algorithm performs well in regions of the image with significant features.

Evolution of the Earth Observing System (EOS) Data and Information System (EOSDIS)

The earth observing system (EOS) data and information system (EOSDIS) has been serving a broad user community since 1994. Most of NASAs Earth science data are currently being archived, managed and distributed by EOSDIS. Also, EOSDIS commands and controls EOS spacecraft and instruments, captures data from the instruments and processes them into a set of standard products. As of March 2006, the archives of EOSDIS held over 4.3 petabytes of data from over 90 instruments and over 2000 distinct science products. The distribution of data to end users amounts to approximately 2 TB a day. The community receiving data from EOSDIS is on the order of 200,000 distinct users from a diverse set of organizations and scientific disciplines. While EOSDIS is effectively managing a large amount of data and successfully serving a broad user community, it is a system whose design and development originated more than 10 years ago during which many advances have occurred in information technology. Although there has been an on-going process of technology infusion, incremental improvements in processing and performance, and new functionality added in areas of user access, distribution, and archive management over the years, the underlying design has remained essentially the same. During this time frame, data volumes have grown dramatically and the science community has gained considerable experience in processing and analyzing their data. More recently, through examination of current operations and a series of lessons learned, there has been a desire to re-examine current operations for significant improvements in a variety of areas. The overall objectives of the EOSDIS evolution are to: increase end-to-end data system efficiency and autonomy while decreasing operations costs, increase data interoperability and usability by the science research, application, and modeling communities, improve data access and processing, and ensure safe stewardship.

Geoscience Data Provenance: An Overview

The advancement of Earth observing sensors, data, and information systems enhances significantly the capabilities to access and process large volumes of geoscience data, which are often consumed by scientific workflows and processed in a distributed information environment. Consequently, data provenance becomes important since it allows users to determine the usability and reliability of data products. Motivation for capturing and sharing provenance also comes from the distributed data and information infrastructure that has been benefiting the Earth science community in the past decade, such as spatial data and information infrastructure, e-Science, and cyberinfrastructure. This paper provides an overview of geoscience data provenance in supporting provenance-aware geoscience data and information systems by summarizing state-of-the-art technologies and methodologies of geoscience data provenance and highlighting key considerations and possible solutions for geoscience data provenance.

Evolving a ten year old data system

The Earth Observing System (EOS) Data and Information System (EOSDIS) is a comprehensive distributed system designed to support NASAs Earth Science missions. Designed in the early 1990s, EOSDIS has been archiving, managing, and distributing Earth science data since 1994. Over the life of EOSDIS an on-going process of technology updates and improvements in user access, distribution mechanisms, and archive management has attempted to keep the system current. However, data volumes have grown rapidly and the science community has gained experience and capability in processing and analyzing their data. The result is a growing desire to re-examine the current operations for gains and improvements in a variety of areas. The objectives of the evolution of EOSDIS are to: increase end-to-end data system efficiency while decreasing operations costs, increase data interoperability and usability by the science research, application, and modeling communities, improve data access and processing, and ensure safe stewardship

Advances in spatial data infrastructure, acquisition, analysis, archiving & dissemination

The authors review recent contributions to the state-of-thescience and benign proliferation of satellite remote sensing, spatial data infrastructure, near-real-time data acquisition, analysis on high performance computing platforms, sapient archiving, multi-modal dissemination and utilization for a wide array of scientific applications. The authors also address advances in Geoinformatics and its growing ubiquity, as evidenced by its inclusion as a focus area within the American Geophysical Union (AGU), European Geosciences Union (EGU), as well as by the evolution of the IEEE Geoscience and Remote Sensing Societys (GRSS) Data Archiving and Distribution Technical Committee (DAD TC).

Preservation of data for Earth system science - Towards a content standard

Various remote sensing agencies of the world have created a data rich environment for research and applications over the last three decades. Especially over the last decade, the volume and variety of data useful for Earth system science have increased quite rapidly. One of the key purposes of collecting these data and generating useful digital products containing derived geophysical parameters is to study the long-term trends in the Earths behavior. Long-term observational data and derived products are essential for validating results from models that predict the future behavior of the Earth system. Given the significant resources expended in gathering the observational data and developing the derived products, it is important to preserve them for the benefit of future generations of users. Preservation involves maintaining the bits with no loss (or loss within scientifically acceptable bounds) as they move across systems as well as over time, ensuring readability over time, and providing for long-term understandability and repeatability of previously obtained results. In order to ensure long-term understandability and repeatability, it is necessary to identify all items of content that must be preserved and plan for such preservation. This paper discusses the need for a standard enumerating and describing such content items and reports on the progress made by NASA and the Federation of Earth Science Information Partners (ESIP Federation) in the U.S. towards such a standard.

NASA EOSDIS Data Identifiers: Approach and System

NASAs Earth Science Data and Information System (ESDIS) Project began investigating the use of Digital Object Identifiers (DOIs) in 2010 with the goal of assigning DOIs to various data products. These Earth science research data products produced using Earth observations and models are archived and distributed by twelve Distributed Active Archive Centers (DAACs) located across the United States. Each data center serves a different Earth science discipline user community and, accordingly, has a unique approach and process for generating and archiving a variety of data products. These varied approaches present a challenge for developing a DOI solution. To address this challenge, the ESDIS Project has developed processes, guidelines, and several models for creating and assigning DOIs. Initially the DOI assignment and registration process was started as a prototype but now it is fully operational. In February 2012, the ESDIS Project started using the California Digital Library (CDL) EZID for registering DOIs. The DOI assignments were initially labor-intensive. The system is now automated, and the assignments are progressing rapidly. As of February 28, 2017, over 50% of the data products at the DAACs had been assigned DOIs. Citations using the DOIs increased from about 100 to over 370 between 2015 and 2016.

Large scale data mining to improve usability of data - an intelligent archive testbed

Research in certain scientific disciplines-including Earth science, particle physics, and astrophysics-continually faces the challenge that the volume of data needed to perform valid scientific research can at times overwhelm even a sizable research community. The desire to improve utilization of this data gave rise to the Intelligent Archives project, which seeks to make data archives active participants in a knowledge building system capable of discovering events or patterns that represent new information or knowledge. Data mining can automatically discover patterns and events, but it is generally viewed as unsuited for large-scale use in disciplines like Earth science that routinely involve very high data volumes. Dozens of research projects have shown promising uses of data mining in Earth science, but all of these are based on experiments with data subsets of a few gigabytes or less, rather than the terabytes or petabytes typically encountered in operational systems. To bridge this gap, the Intelligent Archives project is establishing a testbed with the goal of demonstrating the use of data mining techniques in an operationally-relevant environment. This paper discusses the goals of the testbed and the design choices surrounding critical issues that arose during testbed implementation.

Ensuring and Improving Information Quality for Earth Science Data and Products

Quality of products is always of concern to users regardless of the type of products. The focus of this paper is on the quality of Earth science data products. There are four different aspects of quality – scientific, product, stewardship and service. All these aspects taken together constitute Information Quality. With increasing requirement on ensuring and improving information quality, there has been considerable work related to information quality during the last several years. Given this rich background of prior work, the Information Quality Cluster (IQC), established within the Federation of Earth Science Information Partners (ESIP) has been active with membership from multiple organizations. Its objectives and activities, aimed at ensuring and improving information quality for Earth science data and products, are discussed briefly.

Realizing the Value of a National Asset: Scientific Data

“We have a shared responsibility to create and implement strategies to realize the full potential of digital information for present and future generations,” according to the Electronic Geophysical Year (eGY) Declaration [CoBabe-Ammann et al., 2007]