Rapid Prototyping Capability
for Earth-Sun System Sciences
GEORESOURCES INSTITUTE
MISSISSIPPI STATE UNIVERSITY
ROBERT J. MOORHEAD, ASSOCIATE DIRECTOR
AND
DAVID R. SHAW, DIRECTOR
GEORESOURCES INSTITUTE
Table of Contents
Abstract ........................................................................................................................................................ 3
Project Plan .................................................................................................................................................. 4
Background.............................................................................................................................................. 4
Objectives ................................................................................................................................................ 4
Project Team ........................................................................................................................................... 4
Technical Approach ............................................................................................................................... 5
Introduction ........................................................................................................................................ 5
The Grid Infrastructure .................................................................................................................... 8
Grid Computing Environments ...................................................................................................... 8
Baseline system (Preliminary Design) ...........................................................................................10
Existing and Evolving Tools to be added to Baseline System ..................................................13
Model Manager Support .................................................................................................................13
Technical Challenges .......................................................................................................................13
Evaluation Tasks...................................................................................................................................15
Management Approach .......................................................................................................................15
Developing a Capabilities Document ...........................................................................................17
Developing Preliminary Design .....................................................................................................18
Conducting Preliminary Design Review (PDR) ..........................................................................18
Developing Implementation Plan..................................................................................................18
Developing System Design Document.........................................................................................19
Implementing RPC nodes...............................................................................................................19
Testing the RPC node .....................................................................................................................19
Status Review ....................................................................................................................................19
Deliverables.......................................................................................................................................19
Example RPC tests ...............................................................................................................................20
NPOESS and VIIRS .......................................................................................................................20
NASA Land Information System (LIS) ........................................................................................20
Schedule ......................................................................................................................................................22
Performance Measures .............................................................................................................................22
Reference List ............................................................................................................................................23
Total Budget ......................................................................................... Error! Bookmark not defined.
Budget (subcontract to the University of Mississippi) .................. Error! Bookmark not defined.
Budget (subcontract to the University of Mississippi) .................. Error! Bookmark not defined.
Budget Justification .............................................................................. Error! Bookmark not defined.
Mississippi State University ............................................................ Error! Bookmark not defined.
The University of Mississippi ........................................................ Error! Bookmark not defined.
Letters of Commitment, Support, and Collaboration .........................................................................24
Resumes ......................................................................................................................................................30
Appendix A: Capabilities of the Stennis Team ....................................................................................60
Appendix B: Systems Engineering Documentation Standards .........................................................64
Abstract
The goal of a Rapid Prototyping Capability (RPC) is to speed the evaluation of potential uses of
NASA research products and technologies to improve future operational systems by reducing the
time to access, configure, and assess the effectiveness of NASA products and technologies. The
developed RPC will accomplish this goal and contribute to NASA’s Strategic Objective to “advance
scientific knowledge of the Earth system through space-based observation, assimilation of new
observations, and development and deployment of enabling technologies, systems and capabilities
including those with potential to improve future operational systems.” (NASA, 2005)
The proposed RPC will use a network of researchers at the University of Mississippi, Mississippi
State University, NASA centers, United States Geological Survey, and other partners to provide the
capability to rapidly assess the efficacy of proposed applications in Earth-Sun System Science to
provide input to most if not all of the twelve National Application areas. The proposed RPC will
provide the capability to integrate and provide access to the tools needed to evaluate the use of a
wide variety of current and future NASA sensors and research results, model outputs, and
knowledge. Competing methods will be examined, evaluated, and benchmarked quickly and
creatively by a multi-disciplinary team of scientists. The team of scientists will be assembled from
across the community. The scientists will work with a core group of researchers with broad
expertise in grid computing, rapid prototyping capability, software engineering, image processing,
programming, mathematical modeling, image analysis, and statistical analysis.
The proposed RPC will provide the capability to rapidly evaluate innovative methods of linking
science observations from current and near-term sensors and output from NASA models. The
same capability will facilitate the demonstration and evaluation of improvements in future sensor
systems and models and will provide a systematic way to extend the benefits of Earth system science
research for society.
The capability will begin with a distributed system with nodes at the University of Mississippi,
Mississippi State University, and NASA Stennis Space Center and evolve into an integrated network
with capabilities to support most, if not all, data or model product, research results, and technology
from NASA and their partners. The RPC will access existing NASA tools and resources to build a
network of organizations to address a wide range of research questions.
The expected results of the development of an RPC will be a distributed system capable of
evaluating, validating and verifying, and benchmarking research results to improve the ability to
transition NASA developed research results and observations to operational agencies.
Project Plan
Background
A Rapid Prototyping Capability within NASA is needed to close the gap in the transition from
research to operations. Operational models have strict requirements that must be verified and
validated before they can become accepted by the operational agency. In the current environment,
Operational Spacecraft Simulation Experiments (OSSE’s) are completed before any potential uses
are investigated. This leads to an insufficient time to investigate, verify and validate, and benchmark
the science results from a sensor. By beginning the rapid prototyping during the development of the
OSSE’s, the time for transition from research to operations is decreased.
Rapid prototyping has been used in the manufacturing and sensor development areas for many
years. In manufacturing, the term rapid prototyping refers to a class of technologies that can
automatically construct physical models from Computer-Aided Design (CAD) data. The rapid
prototyping process is a way to quickly build operational models of a device or component, to
provide visualization and testing of the manufacturing process, and to help document the
improvements to be realized by the new component.
In March 1998, the Committee for Space Weather (CSW) proposed the development of a
Community Coordinated Modeling Center (CCMC) (National Space Weather Program
Implementation Plan). The center was designed as a place where researchers could try out new
models while still in the development stage. The concept was designed and reviewed by the space
weather community, which consisted of representatives from NOAA, DoD, NASA, and NSF.
The CCMC is designed to fill a gap between the research community and the operational
agencies that need to use research results in an operational system. The concept is to merge
operational and science requirements and to provide a mechanism to develop and test research
models with the potential to transition to operations. The CCMC uses Rapid Prototyping Centers
(RPC) at partner agencies, NOAA and the DoD.
The proposed Rapid Prototyping Capability is designed to fill a similar gap in the NASA
research to operation flow. By developing a network of researchers and scientists that includes the
University of Mississippi (UM), Mississippi State University (MSU), NASA Stennis Space Center
(SSC), NASA Marshall Space Flight Center (MSFC), NASA Goddard Space Flight Center (GSFC)
and other university and government partners, similar results can be achieved. The following
section discusses the steps to be taken in the development of the proposed RPC.
Objectives
The proposed Rapid Prototyping Capability node will allow model developers and owners to
systematically evaluate research capabilities, based on the use of specific NASA Earth-Sun system
science research results in a simulated operational environment in order to evaluate components
and/or configurations that could be considered for verification, validation, and benchmarking for
transition from research to operations and/or into an integrated system solution (ISS). The results
of NASA Earth-Sun system science include, but are not limited to:
 NASA research spacecraft and their observations of the Earth-Sun system;
 NASA models and their predictive capabilities for weather, climate and natural hazards; and
 published improvements to scientific knowledge of the Earth-Sun system.
Project Team
The team will consist of faculty, research staff, and graduate students at MSU and UM, as well as
several nationally recognized organizations, laboratories, and centers. The project PI will be Dr.
Robert Moorhead, who, in his role as Enabling Technologies lead in the DoD HPCMP
Programming Environment and Training contract, directs one of two crosscutting functional areas
and is presently PI of a project that is developing an ESMF-compliant analysis tool. The Co-PI will
be Dr. David Shaw, Director of the GeoResources Institute. Dr. Ed Allen (MSU), a nationallyrecognized expert in system engineering, will lead the system engineering effort. Dr. Tomasz Haupt
(MSU), a proven grid computing developer, will lead the development team that will implement the
nodes. Dr. Greg Easson, Director of the University of Mississippi Geoinformatics Center will lead
the Evaluation Management team. The development team will involve proven scientists from
varied disciplines. Dr. Chuck O’Hara (MSU) has expertise in agriculture efficiency and water
management. Valentine Anantharaj (MSU) has expertise in meteorology and storm modeling. Dr.
T.J. Jankun-Kelly (MSU) does research in information visualization and Dr. Joel Kuszmaul (UM)
will support the RPC with statistical analysis of model results to ensure that the improvements from
the integration of NASA data are valid.
The Stennis Team, which includes Dr. George May, Dr. David Lewis, and Dr. Robert Ryan,
brings expertise in many areas, including application-specific expertise and satellite image processing.
The USGS contract provides us access to GEOLEM and its developers and will allow us to more
rapidly develop a mechanism to capture model-specific expertise or knowledge and encode it in a
generic format, improving the ability to maintain, exchange, and systematically evaluate these
knowledge sets. The relationship with the modeling group at NASA/GSFC provides us access to
NASA modelers.
Most of the team has worked with another part of the team before. For example, Dr.
Moorhead, Dr. Haupt, and Mr. Anantharaj worked together in the DMEFS project discussed below.
Dr. Easson has collaborated with Dr. Kuszmaul on many projects that used imagery for applications
ranging from coastal wetland loss to landslide detection. Dr. O’Hara and the GEOLEM team have
a long-standing working relationship.
We intentionally designed the team so that one university is lead on development and the other
is lead on evaluation, but we are committed to work together on both parts. It will be a delicate
balance to maintain objectivity, if it was not for the nationally recognized organizations, laboratories,
and centers involved.
Technical Approach
Introduction
The operation of the proposed RPC is similar to a sensor development laboratory. In a sensor
development scenario, a customer or client has established a need for detection of parameter, such
as soil moisture. That need defines what the sensor must detect, the precision of the detection
system, upper and lower limits of detection, and the conditions of operation. The conditions of
operation include communication with other devices, the operational environment and the life
expectancy of the sensor.
Once all these conditions are defined, engineers go to the sensor laboratory and begin to
assemble a prototype. This prototype may use off the shelf components and/or custom designed
and built components. These components, including power supplies, antennae, data recorders, and
other devices, are assembled on a “bench” into a workable prototype. This purpose of this
prototype is to test the feasibility of the sensor design. The prototype on the “bench” does not look
like the final sensor will, nor is it elegant in its appearance and engineering. However, it does
provide the design engineers with a method to determine if the design is feasible. If the design is
proven to be feasible, it is then forwarded to the manufacturing engineers who will turn that design
into a compact, elegant, and well-engineered sensor.
The RPC proposed is being designed with this process in mind. The customer or clients are the
agency partners that operate the models and decision support tools as part of their mission. These
models and DST’s have very specific and sometimes rigid operation requirements, such as the
parameter to be detected or modeled, the accuracy required of the model or sensor, communication
with other systems and devices and the operational environment.
However, in building this RPC, the sensor laboratory setting is a research environment that
includes researchers at the University of Mississippi, Mississippi State University, other university
researchers, NASA project leaders, and operational agency partners. In the case of the proposed
RPC, university faculty, full-time research staff, post-doctoral researchers, graduate students, and
subcontractors with different skill sets that form an interdisciplinary team will assemble the
components.
The result from the RPC analysis of a question will be working design that may have a less than
user-friendly interface, little or no error trapping routines, or data transfer techniques that use flash
memory cards. However, the results are valid, can be statistically measured and will be published to
the user community. The proposed RPC will take into account the operational requirements of the
agency partner during the development and testing of the model.
The mission of the proposed RPC is to bridge the “valleys of death and lost opportunities” in
the current scenario of the pathway from research to operations. (NRC, 2003) This mission will use
tools such as Operation Spacecraft Simulation Experiments (OSSE), Metis, JCSDA tools, and the
Space Time Toolkit to help overcome the gap in the pathway between the push of NASA sensor
and system researchers and the pull of the operational data providers, such as NOAA and DoD. In
addition, the proposed RPC will rely data and information provided by NASA Distributed Active
Archive Centers (DAAC), the Earth-Sun System Gateway, and the Knowledge Base.
The steps in rapid prototyping are:
1.
2.
3.
4.
5.
Create a conceptual model of the process,
Convert the conceptual model into requirements specifications,
Divide the requirements into operational components,
Develop and integration the operational components into a prototype, and
Measure the improvement of the process from the rapid prototype.
Our conceptual model is shown in Figure 1. This conceptual model is discussed in great detail
in the Management section. Working with the NASA Technical Monitor and the NASA Program
Managers we will convert the conceptual model into requirements specifications to support the
IBPD metrics available at http://aiwg.gsfc.nasa.gov. We will build a distributed, OGC-compliant
system with links to NASA observations, models, analysis tools, databases, and other relevant
information and components. Although the system is designed to support any of the twelve
national application areas, we expect to focus on the application areas of disaster management,
coastal management, and health, due to the recent hurricane activities. We will systematically
evaluate how the
data from the
deployed and
future spacecraft
listed in Tables 1a
and 1b of the
SOW, as well as
specific research
results, can
improve
integrated system
solutions and/or
be transitioned
from research to
operations. We
propose the
NASA assets, not
the owners of the
decision support
tools, be the
primary driver in
driving the RPC
focus.
We propose
to build a set of
Computational
Rapid
Prototyping
Nodes (CRPNs)
to instantiate this
system as a grid
computing
environment.
This is motivated,
justified, and
explained in the
following
Figure 1: The System Architecture
sections.
The Grid Infrastructure
The Grid introduces the concept of Service Oriented Architecture (SOA) and virtualization of
resources. As shown in Fig. 2, the services comprising the Grid infrastructure are organized in
layers. At the lowest layer, there are services providing access to virtualized Grid resources:
computational services (GRAM), file systems and databases (DAIS), and file transfer (GridFTP).
Other services providing access to resources specific to a user community or an application domain,
such as sensors, instruments, data servers or knowledge management can be added here, as well.
Above that layer there are generic Grid middleware services providing support for security,
information services, resource discovery, and monitoring mechanisms. The implementation of these
services conform to the current industry standards (WSDL, WSRF, WS-Security, etc) established by
IETF, W3C, OASIS and GGF, and are part of the standard distribution of the Globus toolkit, and
thus the NSF Middleware Initiative (NMI).
The Computational Rapid Prototyping Node (CRPN) will be deployed over the generic Grid
middleware. This layer comprises high-level CRPN-specific services and will provide application and
workflow management, as well as incorporation of models (Model Manager in Fig. 1), tools for
observing systems simulation experiments (OSSE), site to site data movement, distributed and
reliable metadata access (Science Data Manager in Fig. 1), and data aggregation and filtering
(Interoperable Geoprocessing Environment in Fig. 1). The top-most layer is the application layer
realized as a CRPN portal, which will provide a convenient, intuitive, browser-based interface to
CRPN services. In addition, the portal will give access to user-level tools for data publication and
assorted clients for data analysis and visualization (Performance Metrics Workbench in Fig. 1).
Figure 2: Service Oriented Architecture of a Grid environment [Foster2005]
Grid Computing Environments
Grid Computing has already been deployed by numerous research and commercial organizations
and has changed the way research is being done. Grid computing has provided unprecedented
global-scale scientific collaboration within flexible “virtual organizations” by connecting
geographically dispersed collaborators to complex and large-scale instrumentation, data, computing,
and visualization resources. Seamless while secure access to remote resources – often across
administrative boundaries - including support for discovery and tracking the status and capabilities
of resources within a grid not only makes it possible to obtain more accurate results faster – they
make it possible to achieve what was perceived unachievable just a few years ago. Furthermore, the
Grid is very dynamic and its Service Oriented Architecture (SOA) creates an environment where the
resources are available on demand – when the users need them – making the Grid very well suited
for rapid prototyping of complex systems. There are numerous examples of very successful grid
applications, or more precisely, grid computing environments (GCE). A few representative projects
are discussed here.
The Earth Systems Modeling Framework (ESMF) collaboration is building high-performance,
flexible software infrastructure to increase ease of use, performance portability, interoperability, and
reuse in climate, numerical weather prediction, data assimilation, and other Earth science
applications. The ESMF defines an architecture for composing multi-component applications and
includes data structures and utilities for developing model components. It aims to create a
framework usable by individual researchers as well as major operational and research centers, and
seeks to engage the community in its development. ESMF is being developed by a multi-agency
collaboration that includes many of the major climate, weather and data assimilation efforts in the
U.S.
The Geosciences Network [GEON] enables advanced query interfaces to distributed,
semantically-integrated databases, Web-enabled access to shared tools, and seamless access to
distributed computational, storage, and visualization resources and data archives. The ultimate goal
of GEON is to significantly impact large multi-scale geosciences research programs, as well as
individuals and smaller groups of researchers with the goal of facilitating the development of a
culture in which data are shared, archived, and rapidly disseminated. The task of creating a
cyberinfrastructure for the geosciences (and most other scientific disciplines) is daunting due to the
large volume and diversity of data, as well as the extreme difference in data formats, storage and
computing systems, and differing conventions, terminologies, and ontological frameworks across
disciplines. One way to think about this is that the ultimate goal is to provide the researcher with the
tools and data that he or she needs to do better, more creative science by minimizing the effort
needed to look for data, research the background of a topic, and make software run properly.
Another consideration is that when data and information are entered into an organized system, they
can be easily found and unexpected relationships can be discovered via queries in a Goggle-like
fashion.
The Grid Physics Network [GriPhyN] provides a cyberinfrastructure for four physics
experiments that are about to open a new era of exploration of the fundamental forces of nature and
the structure of the universe. The CMS and ATLAS experiments at the Large Hadron Collider will
search for the origins of mass and probe matter at the smallest length scales; LIGO (Laser
Interferometer Gravitational-wave Observatory) will detect the gravitational waves of pulsars,
supernovae and in-spiraling binary stars; and SDSS (Sloan Digital Sky Survey) will carry out an
automated sky survey enabling systematic studies of stars, galaxies, nebulae, and large-scale
structures. The data analysis for these experiments presents enormous IT challenges. Communities
of thousands of scientists, distributed globally and served by networks of varying bandwidths, need
to extract small signals from enormous backgrounds via computationally demanding analyses of
datasets that will grow from the 100 Terabyte to the 100 Petabyte scale over the next decade. The
required computing and storage resources will be distributed for both technical and strategic
reasons, across national centers, regional centers, university computing centers, and individual
desktops. The scale of this task far outpaces our current ability to manage and process data in a
distributed environment, requiring fundamental advances in many areas of computer science.
The cyberinfrastructure for Network for Earthquake Engineering Simulations [NEES] links
earthquake researchers across the U.S. with leading edge computing resources and large-scale
experimental facilities such as shake tables, centrifuges and tsunami simulators. Through the
NEESgrid, researchers can remotely participate in the experiments utilizing the support for teleobservation and tele-operation; publish and make use of a curated data repository using standardized
markup; access computational resources and open-source analytical tools; access collaborative tools
for experiment planning, execution, analysis, and publication; and perform remotely numerical
simulations. The capability of NEESgrid is best illustrated by the MOST (Multi-Site Online
Simulation Test) experiments. In one such demonstration experiment, a test structure (1-story, 2bay frame, like one that would come from the interior of a multi-story building) had been divided
into three components, each tested in a different location throughout the United States. One
column of the frame was physically tested at UIUC in the Newmark Lab while another column was
physically tested at the University of Colorado at Boulder. The response of the remaining portion of
the structure was simulated via a computational model at NCSA.
Finally, the Distributed Marine Environment Forecast System [Haupt2001], developed at
Mississippi State University, is an attempt to integrate the expertise of computer scientists with
meteorology and oceanography (METOC) modelers and application developers to produce an
information technology infrastructure (ITI) that can expedite progress in computational METOC.
Exploiting leading edge developments in computational grid, web, and component software
technologies, DMEFS integrates some of their best features to provide a multi-tiered infrastructure
that links a user on a desktop via a network application to model execution on high end computing
platforms that can be distributed globally. DMEFS is an attempt to integrate the expertise of
computer scientists with METOC modelers and applications developers to produce the ITI that can
expedite progress in the disciplines of atmospheric and ocean sciences. In particular, DMEFS
provides support for easy integration of legacy applications, such as the numerical forecast models
COAMPS, WAM or NCOM, and organizes them into a distributed computing environment
spanning administrative boundaries. DMEFS, for example, has been demonstrated on a grid
comprised of machines at ERDC MSRC, NAVO MSRC, and Mississippi State University. The
services that can be provided by the DMEFS infrastructure are tailored to minimize the complexity
of model research, development, transition, and operation or execution.
Baseline system (Preliminary Design)
The development of CRPN will extend the current grid infrastructure developed and deployed
at Mississippi State University [Haupt2002, Haupt2003], which is used to support the NEESport
[Haupt2005a], SPURport [Haupt2005b] and DMEFS [Haupt2001] systems, as schematically shown
in Figure 3.
The most prominent features of the baseline system are high-level grid services, or façades, that
capture common patterns of accessing the Grid resources. For example, the Data Services combine
metadata, data access replica locator and file transfer services, and provide a simple interface for
retrieving files of known signature but unknown location. The user selects a file by searching the
metadata repository (a keyword-based query) for the selected file and the Data Services seamlessly
determine its location (i.e., URL) from the Replica Locator. Then the URL is passed to the File
Transfer Service to start streaming the data to the user. The User Space is another application of the
Data Service that allows the user to store information on his or her activities (i.e., provenance), such
as job descriptors and values of parameters used for a particular run. Provenance is critical for
linking the model parameters with the model output datasets and thus achieving convergence of
models and data [ESMF]. Finally, the Job Submission Services (c.f. Fig. 4) invokes several base
services to stage the data, retrieves information needed for generation GRAM RSL request, set the
environment on the target machine, submits the job and registers itself as a target for GRAM
notifications, so that it can forward the status changes to the Job Monitoring Service. The Job
Monitoring Service provides access to all jobs submitted through the portal, their status and their
descriptor. Through the job descriptor, the user has access to all results generated by the job.
The Grid Services are implemented as WSDL/WSRF services, invoked by Service Providers
running inside the GridSphere portal server for the Web Browser-based clients or Service Providers
added as modules to stand-alone applications such as Abacus and Matlab.
Figure 3: The baseline system. This four-tier architecture follows OGSA recommendations (c.f. Fig. 2)
The CRPN will be designed to be fault tolerant and to support multiple users working
concurrently. To achieve fault tolerance, the CRPN needs to maintain persistence of user objects
that hold all necessary information (scripts, configurations, etc.) needed to perform all the
simulations and optimizations [Haupt2003]. The information stored in the user space will be also
used by a job submission service that automatically formats resource allocation requests using the
syntax of the basic Globus services. The user space service safely stores all user data on the server
side. Thus, if the user connection with the servers is broken, voluntarily or not, all information is
still available when the user connects back to the server.
Each user owns his or her data container, referred to as a “private” user space. CRPN accesscontrol mechanisms will ensure that only the owner has access to the private space. Conversely,
since each user operates on his/her private container (an independent instance) there will be no
interference between the users, e.g., they do not overwrite each other’s files. Since the user may
want to perform many different simulations at the same time, the CRPN will provide means to
organize the user work into “projects,” and a project into “tasks.”
The user private space provides support for the accountability of the users as well as “privacy”
in the sense that the developer can test and modify codes without affecting other users. However, by
adjusting the access-control policies, the user space can also be used for publishing the codes and
results by putting them into shared projects. In particular, projects created for particular tasks can
be used to run workflows comprising codes provided by several members of a design team.
The Job Monitoring Service gathers information on the status of the workflow execution,
including all jobs submitted through the CRPN. It provides a connection between a job instance
(running or completed), the corresponding job descriptor and the user who submitted it (for access
control purposes). Through a corresponding GUI, the user can monitor the progress of his or her
jobs, and for each job, the user can access its workflow descriptor. With the descriptor in hand, the
user can access all output files (view or download), including standard output, standard error and log
files, as well as input files, values of application parameters and other configuration parameters
Figure 4: Job submission mechanism of the
baseline system. This high-level grid service
orchestrates User Space, Metadata, GRAM, File
Transfer and Job Monitoring services to submit
and monitor a legacy application specified by the
job descriptor stored in the user space.
Each high-level service provides a distinct Portal functionality by custom orchestrating the
generic Grid services following the common usage patterns. For example, all services make use of
the metadata service. The repository service uses the metadata service in conjunction with file
transfer service and replica locator. The workspace service uses the metadata service to maintain
information about the user applications. Job submission service retrieves the application descriptors
from the repository of descriptors, and the job table is implemented using metadata as well.
Similarly, the File Transfer service is used by the repository, job submission and job table services.
The standardized interfaces of the façades make implementation of the Portal front-end easy.
The façades serve as data models; the front-end implementation just needs to add custom
controllers and viewers satisfying a particular application domain needs. The façades are reusable as
long as the interfaces for the Grid-level services are standardized. Even though there is an ongoing
GGF effort to achieve that, at this time this assumption is not true: there are many alternative
implementations of the Grid services. For example, there are many different possible
implementations of the metadata service, from generic DAIS to custom developed services based on
J2EE, SRB, or other. To accommodate the differences in the interfaces the CRPN introduces an
additional layer that virtualizes Grid-level services. For the Portals developed so far, five virtual
services are needed: job submission, file service, replica locator, metadata service and XML-database
service. These services act as client to the concrete back-end, Grid-level services, and the
differences between the interfaces between the virtual and concrete services are accommodated
using adaptors. For example, if Globus GRAM is used as the job submission service, the adapter
generated the RSL string. If NMDS (NEESgrid Metadata Service) is used, the virtual call to the
metadata service is translated to the concreted interface of NMDS, etc. The advantage of such
approach is that the Portal can be easily ported to whatever back-end services are available by just
modifying the adapters (c.f. Figure 5).
Existing and Evolving Tools to be added to Baseline System
The Stennis Team proposes to build upon the Atmospheric Correction Tool (ACT), the
Application Research Toolbox (ART), and the Time Series Product Tool (TSPT) to establish a high
performance Visible Infrared Imager Radiometer Suite (VIIRS) simulation capability that can be
used to rapidly prototype products for evaluation. The VIIRS simulator will be a systems level
simulator that will emulate the spatial, spectral, radiometric properties defined by the VIIRS
specification. Products will be simulated through system level modification of existing similar
products as well as the use of Algorithm Theoretical Basis Documents. The TSPT will be modified
to account for the differences in potential cloud statistics and quality assurance flags. The ACT will
be upgraded to accept the Atmospheric Infrared Sounder (AIRS), Humidity Sounder for Brazil
(HSB), and the Ozone Monitoring Instrument (OMI) products. The tools will be rehosted and
modified to support RPC architecture. Preliminary examples show that some products may require
further consideration before becoming operational. This foreknowledge of expected performance
level is crucial to the future applicability and uptake of NASA data products into operational use by
partner organizations, model owners, and decision support tools. The ART and analog data systems
can be built upon to create Advanced Technology Microwave Sounder (ATMS) and Ozone
Mapping and Profiler Suite (OMPS) simulation capability. Capability for other systems can be added
when specific evaluations determine such need exists. A description of ACT, ART, and TSPT are in
Appendix A.
Roland Viger (USGS) and his colleagues will implement GEOLEM within the Interoperable
Geoprocessing Environment (IGE). This will directly benefit not only NASA, but all nine of the
federal participating agencies on the Interagency Steering Committee on Multimedia Environmental
Models (http://iscmem.org/). This is consistent with multi-agency directions and their desire to
develop semantic interaction between the "Modeling Domain" and the "GeoProcessing Domain."
Model Manager Support
Per the letters of support, the Office of Science Utilization at NASA/GSFC has an interest in
participating, has committed to work with us on integrating NASA models and participating in the
use of the RPC to provide a "Systems Evaluation" of their modeling efforts.
Technical Challenges
The CRPN Portal and its infrastructure will extend the current capabilities of the baseline
system. While the ultimate functionality will be determined at the Preliminary Design Review, some
general ideas of implementation strategy can by laid out now. To support rapid prototyping of
applications, it is necessary to provide access to existing data sources (such as those provided by
ESMF, DODS, etc). This means creation of new adapters for the baseline metadata and file
services, and most likely, extensions of our data access and repository façades. We will incorporate
solutions or build on experiences of other researchers making advances in that field (ESMF,
GEON, etc.). Also, we will adapt existing tools for data pre- and post-processing. Another
necessary extension to our baseline system is support for workflows. Here we can build on the
experience of GriPhyN, LEAD [Lead], GridAnt [GridAnt], and others. We anticipate more changes
and additions resulting from the problem analysis and formal design process.
Figure 5: The baseline system. The front end invokes methods of the façades that orchestrate virtual grid
services. The virtual services invoke the concrete back-end services through adapters that accommodate the
possible differences between the virtual and concrete service interfaces.
Even though the general approach is to build on the existing infrastructure and to incorporate
existing solutions and in particular, provide access to existing NASA assets, this project poses
several challenges beyond just software engineering. A partial list includes:
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On-demand, transparent access to resources vs. security (authentication, authorization,
confidentiality, auditing, and non-repudiation); interoperable access control mechanisms,
Support for computations and data sharing across administrative domains,
Data integrity in distributed, multi-user environments,
Data discovery: semantic metadata, interoperable ontologies, semantic Grid
Capturing of the knowledge and mechanisms for querying knowledge databases and data
mining,
Resource discovery: centralized Grid vs. dynamical, adaptive peer-to-peer networks,
Resource Negotiations and Service Level Agreements (SLA),
Self-Management in distributed, heterogeneous environments: automating the tasks of
configuration, healing and optimizations needed to keep the Grid operating correctly and
meeting its SLA, as well as adaptive resource management to accommodate the various
available resources and dynamically changing resource requests,
Fine-grain vs. coarse-grain software integration for simulations and workflow specifications,
Virtualization of legacy software tools,
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Technology transfer from R&D to operational use, and
User interfaces that adapt to the capabilities and capacities of the users and their client
hardware, as well as support for mobile users.
Solving all of the above problems is beyond the scope of this project. During the analysis phase
we will select the issues that are critical for the success of this project and address them.
Evaluation Tasks
The tools described in the previous section can provide a rich data set to evaluate NASA SunEarth System Sensors. In order to show the utility of these simulated and enhanced Sun-Earth
Systems data sets, several evaluations will be performed. These evaluations will treat a variety of
science questions within the national applications of Agricultural Efficiency, Aviation, Air Quality,
Carbon Management, Coastal Management, Disaster Management, Ecological Forecasting, Energy
Management, Homeland Security, Invasive Species, Public Health, or Water Management.
The evaluations will be characterized by a quick assessment of the ability of NASA products and
models to improve the effectiveness of Decision Support Tools. The objective of enhancing
Decision Support Tools is to move research results into operational use. To that end, government
agencies interested in the operational capabilities of Earth-Sun Sensors will be engaged in the
process.
The evaluation process will use the data assimilation, modeling and analysis tools available within
the RPC node. Interaction with the relevant government agency will provide input to assess the
requirements of the evaluation. Included in the final evaluation for a given focus area will be reports
which are built around the System Engineering Evaluation Process.
Management Approach
The management of the proposed RPC will use the architecture proposed in Fig. 1. This
architecture will guide the overall management of the entire RPC as well as the development of each
evaluation report. By using the same architecture for both individual rapid prototyping projects and
the management of the RPC nodes, we are assured of conformity among the projects and
compliance with the NASA Enterprise Architecture (EA). The following paragraphs will discuss the
management approach for the proposed RPC.
Moorhead and Easson will be the institutional PIs. Both will utilize the expertise of an
experienced senior Co-PI and a project manager for day-to-day management.
The architecture in Fig. 1 closely parallels the current knowledge base developed by the Applied
Sciences Directorate. The knowledge base outlines the Earth-Sun Observation Sources that are used to
obtain the Geophysical Parameters needed for the Models and Analysis Systems that produce the Model
Outputs/Predictions that are then used by the Decision Support Tools (DST’s) that are owned/developed
and operated by government partners. The involvement of these partners will begin at the start of
the prototyping process. It is proposed that a portion of the budget be obligated to subcontracts to
these partners to ensure their involvement in the entire process. These partners will be determined
as part of the Capabilities Document development. Example RPC tests are delineated below.
In our proposed architecture, the Science Data Manager (SDM) function accesses the data from the
NASA Earth-Sun Observation Sources. These sources include the DAAC’s, OSSE’s and other data that
may be simulated through the Application Research Toolkit and similar systems. In this component
of the architecture, the metadata will be developed and maintained and the compliance with the
NASA EA begun. The SDM will supply input to the Earth Observation section of the Earth-Sun
System Architecture Tool. The proposed RPC will focus on the data from spacecraft and sensors in
Table 1a of the Statement of Work. This work will be led by Haupt and involve O’Hara,
Anantharaj, and at least one researcher at the Univeristy of Mississippi, as well as the contracted data
providers.
The Interoperable Geoprocessing Environment (IGE) part of the proposed architecture will be
responsible for the development of the data products that measure the appropriate Geophysical
Parameter needed for the Models and Analysis Systems being prototyped. The data from the NASA
sources will be processed to extract the specific information needed for the prototype in
development. The IGE includes two sub-components; Model Data Resource Manager and Data
Assimilation Tools. This portion will be lead by O’Hara and involve Haupt, Anantharaj, Easson, a
UM Visiting Assistant Professor, the Stennis Team, and USGS. We see this as the critical piece to
create a truly Rapid Prototyping Capability.
The Data Assimilation Tools part of the processing environment will include all steps, such as,
transformation, manipulation, extraction and other pre-processing, needed to produce the Geophysical
Parameter input needed for the Model Manager component of the architecture. The model partner will
supply in the requirements of the parameter and work with the RPC in the process to extract that
parameter from a NASA resource. The requirements of the model and the processing steps
required to produce the input will be stored in a database and used to ensure the quality of the data
produced.
In the Model Data Resources Manager part of the environment, the model requirements will be
documented and cataloged in a database. By documenting and cataloging these requirements early
in the prototyping process, the staff of the RPC will be able to identify requirements that are
common to multiple models. This will speed the development of additional prototypes that may
require the same input data. The database developed will supply input to the Data Products section
of the NASA EA tool.
In a just-in-time capability, the order of these 2 components (Data Assimilation Tools and Model
Data Resources Manager) would be swapped. However, for a RPC, there needs to be a just-in-case
environment. In other words, once products are created, they are saved to be used in future RPC
tests.
The Model Manager component of the proposed architecture also has two sub-components that
correspond with the Knowledge Base: the Model Base Manager and Model Results Resoure Manager.
These components correspond to the Model & Analysis Systems and Model Outputs/Predictions parts of
the Knowledge Base. Haupt and his team will develop this portion, but other team members will
assist model owners/developers with specifying their SDM and IGE needs and prototyping their
model within the RPC. Which owners/developers will be determined as part of the Capabilities
Document development. The Model Manager component will be coordinated with the Earth System
Modeling Framework (ESMF) to contribute to the building of an infrastructure designed to increase
the performance, portability, interoperability, and coordination in the modeling community.
In the Model Base Manager, the status of the model runs in the prototyping process are managed
to ensure valid results from the model runs. The manager catalogs the configuration of the model
run, the other parameters that influence the model performance and the data used in each model.
The agency partner that developed or operates the model will supply the model configuration
parameters and assist in the evaluation of model runs. Without effective management of model
runs, the results may not be valid and could become unusable in measuring the improvement
realized in a model through the incorporation of NASA Earth System Science data.
The Model Results Resource Manager records and catalogs the results of the model runs and
provides the information that will be needed to benchmark the improvement in the model
performance. These results will be analyzed by the agency partner and RPC staff and then fed back
to the Model Base Manager to re-initialize the model and begin another run, if necessary. At this step
in the prototyping process, the feedback from the owner/developer of the model being tested will
be part the process to evaluate model performance. The outcomes from the Model Manager
component of the architecture form the basis of input for the model outputs section of the EarthSun System Architecture Tool.
Once the model has produced the designed results, these results must be evaluated to determine
if there is significant improvement in the model’s performance. The results of the model using
NASA data will be compared to the current results and the difference evaluated in the Performance
Metrics Workbench (PMW). A key component of the PMW will be the visualization of the model
results.
The Model Scenario Comparison Toolkit includes the quantitative tools to evaluate model
performance. These tools include the statistical analysis of the numerical results to determine the
significance of any differences in outcome. This portion will be led by Moorhead and will involve
Jankun-Kelly (MSU) and Kuszmaul (UM).
The Evaluation Team will be led by Easson and will involve a core group of post doctoral
researchers with experience in application development and testing. This group will work with the
Performance Metrics Workbench to provide unbiased evaluation of the results of a rapid prototyping
evaluation report. The Evaluation Team will function with the architecture in Fig. 1, but will remain
independent in the evaluations of models and model results.
Through the use of the proposed architecture, the feasibility of any prototype can be rapidly
assessed. We propose to follow the incremental lifecycle model for the development and testing of
prototypes. The incremental lifecycle model divides delivery of the prototype into a series of
increments over time. Each incremental step in the prototype implements a useful capability that is
added to the Knowledge Base. Once the complete prototype is delivered, the capability is finally
delivered to the Knowledge Base in its completed form. The benefits of the incremental lifecycle
for this project are twofold: (1) each increment will give management a tangible indication of
progress, reducing development risk, and (2) each increment’s delivery will support evaluation of
certain rapid prototype applications earlier than would otherwise be possible.
Developing a Capabilities Document
The capabilities of the proposed RPC are based on the expertise of the staff at each node.
These capabilities are aligned with the subset of NASA’s 12 National Applications assigned to
Stennis Space Center; Homeland Security, Coastal Management, Agricultural Efficiency, Water
Management and Disaster Management. However, in light of the recent events in the Gulf Coast,
we expect significant efforts to be directed at the Coastal Management, Disaster Management, and
Public Health applications.
The capabilities of the proposed RPC are aligned with NASA objectives as specified in the
IBPD objectives for the Applied Sciences Program. These objectives are related to NASA’s 12
National Application areas and the proposed RPC will focus on those applications specified in the
Integrated Systems Solutions (ISS) portion of the Statement of Work. These objectives, stated in
the IBPD goals that will be addressed as part of this RPC, are:
 Agricultural Efficiency - Evaluate ESMF predictions into USDA CADRE DST
 Carbon Management - Evaluate or verify potential of carbon sequestration forecasts into
USDA DST
 Coastal Management - Evaluate potential of NPP products to serve coastal DST
 Disaster Management - Evaluate the potential of NPP sensor data into NOAA AWIPS DST
 Energy - Evaluate capacity to assimilate NASA observations into energy DST's.
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Homeland Security - Benchmark the assimilation of ESMF predictions into DHS
Interagency Modeling and Atmospheric Assessment Center
Water Management - Benchmark the assimilation of Land Information System products into
DoI Bureau of Reclamations Riverware/AWARDS DST.
The capabilities document will be developed and made available to NASA and partnering
agencies. The initial project partners are those identified in this proposal. The list of partners will
grow as the capabilities document is developed and as the RPC matures and becomes more fully
integrated into the Research to Operations process.
The sources of data used in the rapid prototyping process will include all available sources of
data from current NASA assets and simulated data from sources such as ART and OSSEs.
Additional sources of data include the Earth-Sun Science labs, Distributed Active Archive Centers
(DAACs), and other modeling centers. Through the use of the Storage Resource Broker, all data
sources will be accessible to all researchers at the distributed nodes (Fig. 1). The nodes at University
of Mississippi, Mississippi State University, and Stennis Space Center will access data in seamless
fashion. The use of this technology will also make the data sets used in prototyping available to the
larger Earth-Sun Science community.
The models to be used by the proposed RPC include those listed in Table 3 of the Statement of
Work, along with others that may be identified in Preliminary Design Review. Through a
subcontract with Goddard Space Flight Center the proposed RPC will obtain access to models,
model developers, and the end users of the models.
Developing Preliminary Design
The preliminary design, presented in the Technical Approach section above, builds on three grid
computing projects that are mature: DMEFS, SPURport, and NEESport. Key components of
DMEFS are deployed at the Naval Oceanographic Office at SSC. The Capabilities Document will
identify project partners, the sources of data, models, and tools, as well as other required resources,
which will refine and enhance the preliminary design. The preliminary design document will
describe planned interactions with the Applied Science Program, Earth-Sun System Division funded
entities, spacecraft mission and instrument principal investigators, partner agencies, and other
potential users or contributors to the system.
Conducting Preliminary Design Review (PDR)
The PDR should include any partners identified in the Preliminary Document and reviewers as
identified by NASA. During this review workshop, outside reviewers will evaluate the proposed
structure, data, and models to be used and suggest changes. All comments will be considered before
the final design document is produced.
Developing Implementation Plan
The Implementation Plan will be finalized after the PDR, will reflect the reviews obtained from
the PDR, will include a means for continuous testing via application partners, will account for
subsequent development activities, and will state our approach to transition and network the
capabilities to a system node at SSC. Our initial plans for continuous testing are to re-run any tested
models at least every 6 months. One of final evaluation reports is likely to be a re-run of all models
used in rapid prototyping tests. Our leverage with model developers/owners will be either via a
contractual relationship or via the value of the RPC to their development and deployment efforts.
The Stennis Team will be lead on standing up the SSC node.
Developing System Design Document
This document will be developed and delivered after the PDR. It will be developed
collaboratively between the MSU and UM team members.
Implementing RPC nodes
We intend to perform all the work defined in the Implementation Plan on schedule and within
budget using recognized management and systems engineering techniques. Unless directed
otherwise by NASA, we propose that software-development documentation produced by this
project conform to the IEEE Standards listed in Appendix B. The software documentation will be
written to directly support efficient implementation and testing of the Rapid Prototyping Capability
Node, and to facilitate the evolution of the software after delivery.
Testing the RPC node
The testing of the RPC at each node will be a continual process that will evaluate the efficacy of
the system design on a regular schedule. This evaluation may be after the completion of each
evaluation report or on a different schedule. The Statement of Work calls for ten evaluation reports
as the primary objective for this task. While at least ten evaluation reports will be completed during
the two-years of this project, it is important to note that not all reports will take the same amount of
time. Some prototypes will be developed, tested, and evaluated in less time than others.
The testing of the efficacy of the individual evaluation reports is more well established than the
testing of the efficacy of the systems design. The individual reports will be evaluated for schedule,
cost and the improvement to the operation model or system. The improvement in the operation of
a model or system will be conducted in the Performance Metrics Workbench. In this component,
the model results with the incorporation of NASA data will be evaluated and statistically compared
to the model results without NASA data. These evaluations will be discussed with NASA and the
operational partner that owns/developed the model.
Testing the efficacy of the system design of the RPC will involve evaluations of external
interfaces, connections, and operational capability of the entire capacity. The system design of the
RPC will be evaluated in the early stages of the proposed project. Through a workshop or series of
workshops, discussed above, the system design of the RPC will be evaluated by an outside panel of
reviewers.
After completion of at least three (3) evaluation reports, the operation of the RPC will be
evaluated, with input from NASA, operational partners, other cooperators, and the scientists at the
nodes. This evaluation will be used to determine if any changes are needed in the RPC system
design, component configurations, external interfaces, and operational capability.
Status Review
Once the RPC is operational, quarterly reports will be prepared and submitted to the NASA
Program Officer and to headquarters personnel. These quarterly reports will be of sufficient detail
to explain evaluations completed and in-progress. The reports will also alert staff of the RPC and
NASA of any projects not on schedule and what action will be taken.
Deliverables
 A Capabilities Document and a Preliminary Design will be developed in the first 90 days
after award and delivered at least two weeks in advance of a Preliminary Design Review
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(PDR) to be held no sooner than 90 days from the date of award and no later than 120 days
from the date of award.
An Implementation Plan that is reflective of the reviews obtained from the PDR. The
Implementation Plan will include a means for continuous testing via application partners,
will account for subsequent development activities, and will provide for the approach to
transition and network the capabilities to a node for the Applied Sciences Program (ASP) at
Stennis Space Center (SSC).
System Design Document
Test Evaluation Document
Twelve (12) Reports evaluating the efficacy of the system design, component configurations,
external interfaces and connections, and sensors, data, and/or other research capabilities.
These 12 reports will provide evidence of a robust system design. Approximately three
evaluation reports will be delivered every 6 months. These reports will document the
suitability of the RPC for further study of candidate configurations of NASA research results
to serve specific National Applications.
Monthly status reports that include task completion status will be submitted via email to the
NASA Technical Monitor.
Documents for quarterly technical design and management reviews, which will be delivered
at least one week before the review.
Quarterly status teleconferences with project team and NASA personnel.
Participation in semi-annual peer review workshops
Implementation of a system node at SSC
Final Design Document
Example RPC tests
NPOESS and VIIRS
An example of the proposed Visible Infrared Imager Radiometer Suite (VIIRS) simulations can
be shown for a crop surveillance system that was originally developed based on MODIS remote
sensing measurements in the red and near-infrared spectral bands. Because the future VIIRS
instruments will have lower spatial resolution than the current MODIS sensors—400 m versus 250
m—the question is this: how will the change in resolution affect the crop surveillance application?
By using the simulated VIIRS measurements, this question may be answered before the VIIRS
instruments are deployed in space. This Observing System Simulation Experiment (OSSE) will
enable timely transition of VIIRS observations into operational use for crop surveillance when the
sensors are launched on the NPP and NPOESS satellites.
Effective response to crop disease outbreaks requires rapid identification and diagnosis of an
event. A near-daily vegetation index product, such as a Normalized Difference Vegetation Index
(NDVI), at moderate spatial resolution may serve as a good method for monitoring quick-acting
diseases. NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument flown on
the Terra and Aqua satellites has the temporal, spatial, and spectral properties to make it an excellent
wide-area data source for rapid, comprehensive surveillance of agricultural areas.
NASA Land Information System (LIS)
NASA has sponsored the development of the NASA Land Information System (LIS), “a
functional Land Data Assimilation System (LDAS).” It currently is comprised of a LIS core, three
land models, data servers, and visualization systems – integrated in a high-performance computing
environment (Peters-Lidard et al., 2004; Kumar et al., 2005). LIS is implemented under the Earth
Systems Modeling Framework (ESMF), an interoperable computational framework that facilitates
the coupled interactions between other computational models of weather, climate and the
environment. The land models in LIS incorporate surface parameters of temperature, snow/water,
vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are
available from NASA platforms at various spatial and temporal resolutions, and can be used to
validation and incorporation into LIS. For example, the MODIS sensor onboard the Terra and
Aqua platforms provide vegetation parameters of Leaf Area Index (LAI), surface albedo, surface
temperature, evapotranspiration, and radiation. High resolution topography is available from the
SRTM mission and the GTOPO30 database. Vegetation structure can be extracted from the
ICESAT and ESSP LIDAR missions. Other parameters are available from AMSR, ASTER, and
TRMM.
The Weather Research and Forecasting (WRF) model (Michalakes et al., 2004), developed by the
atmospheric community and supported by NASA, is being adopted as the model of choice by the
GeoResources Institute (GRI). The WRF has been successfully coupled to the LIS via the ESMF.
The WRF model has been demonstrated to improve precipitation estimates when coupled to the
LIS (using ESMF) on the same grid. The atmospheric fields and products generated using WRF can
be used to contribute vital environmental information for several decision support systems for
several cross-cutting national applications projects, including USDA JAWF (for agricultural
efficiency), IMAAC atmospheric assessment (homeland security), and the USDA USFS Wildland
Fire Assessment System (WFAS), a disaster management application. Other potential applications
include the National Weather Service (NWS) Automated Weather Information Processing System
(AWIPS) and Integrated Weather Effects Decision Aid (IWEDA), a DSS used by the U. S. Army
and being adopted by the USFS for wild fire and smoke management decisions. The Coupled
Ocean/Atmosphere Mesoscale Prediction System (COAMPS) NWP model (Hodur, 1997) is
currently being operationally used by the U. S. Navy. Effort is underway to incorporate COAMPS
into the ESMF. GRI has adopted COAMPS to utilize MODIS landuse classifications.
The RPC will be exercised using a series of modeling experiments using: (a) COAMPS; (b)
uncoupled WRF; and (d) the coupled LIS-WRF system at various spatial resolutions, both with and
without NASA data. Model sensitivity experiments will be performed to evaluate the system.
Schedule
We anticipate a start data of January 1, 2006 for the proposed effort. With this start date, it is
anticipated that the first three to four months of the project will be utilized to prepare the initial
design documents, conduct the design review workshop and, put the necessary architecture
components in place. However, we do anticipate conducting rapid prototypes as part of the
evaluation of the architecture. The architecture will be ready before the PDR to add access to
existing NASA data assets. We anticipate a preliminary RPC test before the PDR to demonstrate
capability.
Since NPOESS/VIIRS appear to be the new assets of greatest interest to potential ISSs, we
propose first to determine if we can provide improved data and information source content and
delivery to the USDA FAS, provide forward-looking synthetically derived products with
characteristics similar to the planned VIRS for analysis of global crop and vegetation condition
monitoring and for crop surveillance and bioproductivity estimation, as well as work with the USDA
FAS, NASA SSC, and the Stennis Team to determine the usefulness of these efforts to meet the
objectives of the USDA—NASA MOU and the identified priorities of the Agriculture Management
interagency focus area working group. The proposed effort will employ NASA tools and
technologies such as OSSEs and ART to create or synthesize forward-looking data products from
NASA science data streams (synthetic VIIRS), work with the DAAC Alliance to define data-flow
and network requirements, and the METIS Enterprise Architecture to explore optimum data
sources that may be employed to provide enhanced data streams to USDA program-level modeling
and decision support needs. This will be our Agriculture Efficiency phase.
In the second 6 months, we will focus on integrating NASA Earth-Sun System Science results
that are needed for DST and DDS that utilize atmospheric models. We expect to be assisted in this
endeavor by Dr. Pat Fitzpatrick (MSU), Dr. Maribeth Stolzenburg (UM), and Mr. Valentine
Anantharaj (MSU), as well as personnel at UAH, NASA/MSFC and NASA/GSFC in this endeavor.
In the third 6-month period, we propose to focus on Coastal and Water Management, and
involve Dr. Mark Slattery (UM), Dr. William McAnally (MSU), and Dr. Jim Martin (MSU), as well as
personnel at NASA/GSFC.
In the fourth 6-month period, we propose to focus on Disaster Management models and
involve at least Dr. William Cooke (MSU) and Dr. Pat Fitzpatrick (MSU), as well as personnel at
NASA/GSFC.
Performance Measures
The performance of the proposed RPC will be measured against the IBPD goals established for
NASA. These goals were described earlier and will guide the implementation of the RPC nodes at
the three locations. The objective of ten (10) evaluation reports is stated in the Statement of Work
and how well the proposed RPC meets this objective will be a key performance measure.
Another key performance measure will be the acceptance of the results of rapid prototyping by
the scientific community and the model owners/developers. There are two ways proposed to
measure this acceptance. One measure will be the number of evaluation reports that are developed
into articles for scientific journals. It is anticipated that each of the ten evaluation reports will be
developed into a scientific journal article and that at least eight will be accepted for publication
during the two years of this project. In addition to journal articles, the submission of results to the
appropriate scientific conferences will also be encouraged.
It is also proposed that the collaborators in the proposed RPC conduct a workshop at an
appropriate scientific conference to present the design of the RPC for review by the scientific
community at large. We propose to conduct this workshop at the Pecora Symposium in 2007.
Reference List
EFMS: Earth System Modeling Framework, http://www.esmf.ucar.edu
Foster I., Keynote address at Globus World 2005, Boston, MA
GEON: Geosciences Network, http://www.geongrid.html
GridAnt, http://www.cogkit.org/
GriPhyN: Grid Physics Network, http://www.griphyn.org
Haupt2001: Haupt T., Bangalore P., Henley G., “A Computational Web Portal for the Distributed Marine
Environment Forecast System”, in the proceedings of International Conference on High Performance
Computing and Networking, Amsterdam'01.
Haupt2002: Haupt T., Bangalore P., Henley G., “Mississippi Computational Web Portal”, Concurrency
and Computation: Experience and Practice, Grid Computing Environment Special Issue 13-14 (2002)
Haupt2003: Haupt T., Pierce M., book chapter in "Grid Computing: Making the Global Infrastructure a
Reality", Fran Berman, Geoffrey Fox and Tony Hey eds, John Wiley and Sons, 2003.
Haupt2005a: Haupt T., Kalyanasundaram, A., Ammari N., Chandra K., “NEESport: Grid Portal for
Earthquake Engineering Community”, in the proceedings of the IASTED International Conference on
Modeling and Simulation, MS 2005, May 18-20, 2005, Cancun, Mexico
Haupt2005b: Haupt T., Kalyanasundaram A., Ammari N. , Chandra K. , Das K., Durvasula S.,
“SPURport: Grid Portal for Earthquake Engineering Simulations” in the proceedings of the
International Conference on Computational Science, 2005, May 22-25, 2005, Atlanta, GA
Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction
System. Mon. Wea. Rev., 125, 1414-1430.
Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R. Houser, J. Geiger, S. Olden, L. Lighty, J. L. Eastman,
B. Doty, P. Dirmeyer, J. Adams, K. Mitchell, E. F. Wood and J. Sheffield, 2004. Land Information
System - An Interoperable Framework for High Resolution Land Surface Modeling. Submitted to
Environmental Modelling & Software.
LEAD: Linked Environments for Atmospheric Discovery, http://lead.ou.edu/
Michalakes, J., J. Dudhia, D. Gill, T. Henderson, J. Klemp, W. Skamarock, and W. Wang, 2004: "The
Weather Reseach and Forecast Model: Software Architecture and Performance," 11th ECMWF
Workshop on the Use of High Performance Computing In Meteorology, 25-29 October 2004, Reading
U.K. Ed. George Mozdzynski.
National Space Weather Program Implementation Plan, 2nd Edition, July 2000 http://www.ofcm.gov/
NEES: George E. Brown Jr. Network for Earthquake Engineering Simulations, http://www.nees.org
OASIS Web Services Business Process Execution Language (WSBPEL) TC, http://www.oasisopen.org/committees/tc_home.php?wg_abbrev=wsbpel
Peters-Lidard, C. D., S. Kumar, Y. Tian, J. L. Eastman, and P. Houser, 2004. Global Urban-Scale LandAtmosphere Modeling with the Land Information System, Symposium on Planning, Nowcasting, and
Forecasting in the Urban Zone, 84th AMS Annual Meeting 11-15 January 2004 Seattle, WA, USA.
Letters of Commitment, Support, and Collaboration
GEORESOURCES INSTITUTE
MISSISSIPPI STATE UNIVERSITY
Letters of Commitment, Support, and Collaboration
October 7, 2005
To: Whom It May Concern
I am writing this letter in strong support of Mississippi State University’s “Improving Watershed
Modeling through Enhanced Data Inputs from NASA Remote Sensing Products” proposal. The
Environmental Protection Agency (EPA’s) Office of Water and NASA’s Earth-Sun science program
(formerly NASA Earth Science Enterprise) entered in to a MOU to study the use of NASA remote
sensing and modeling information to support EPA’s water quality related programs. We at
NASA/GSFC are in the second year of a five-year project with the EPA towards work to provide
an operational approach of implementing NASA data in the current BASINS water quality decision
support tool (http://aiwg.gsfc.nasa.gov/esappdocs/projplans/BASINS_WMProject.doc). For our
BASINS project we have approximately 1.0 FTE (CS and contractor) along with working with two
universities (1.0 FTE graduate student and 0.1 PI FTE for one year). We also have another
equivalent supporting activity (i.e., 1.0 GSFC FTE with two university collaborations) to study water
supply, demand, and forecast with the US Bureau of Reclamation. Relatedly, we are forging strong
bonds with the USDA Natural Resources Conservation Service, NOAA National Weather Service
and NOAA Office Hydrology Development. We think our activities complements the watershed
modeling activities associated with the Mississippi State University (MSU) effort led by Dr. Chuck
O’Hara. Further, the current and proposed activities of MSU will significantly enhance our water
availability, quality and prediction work at GSFC. We look forward to working with MSU to
further our collaborative ISS Benchmarking. We also plan to provide MSU direct support and
inputs to the NASA Rapid Prototyping Capability (RPC). We think our work when combined with
MSU will significantly enhance the RPC. In addition, we also look forward to working with MSU
on the interagency working groups on watershed modeling (ISCMEM). We believe the NASA
GSFC and MSU activities are strongly complementary and collaborative. In particular, the ISS and
RPC work in water quality and watershed modeling is important and broad ranging where
collaborative activities between NASA/GSFC and MSU are strongly recommended and beneficial.
Please contact me for any additional information.
Sincerely,
David Toll
NASA/GSFC BASINS Project Manager
October 7, 2005
Dr. David Shaw, Director
GeoResources Institute
2 Research Blvd, Room 219
Mississippi State University, MS 39762
Dear Dr. Shaw:
The Institute for Technology Development (ITD) and Science Systems and Applications Inc. (SSAI)
are pleased to team with Mississippi State University and University of Mississippi in proposing to
NASA Stennis Space Center for establishing an Applied Sciences Systems Engineering Capacity.
The complementary expertise and capabilities of our four organizations will provide the needed
outcomes to support NASA’s goals and objectives.
ITD and SSAI, hereafter referred to as the Stennis Team, will commit people, hardware and
software to perform work in the Rapid Prototyping Capability functional area defined in NASA’s
solicitation. The Stennis Team’s capability has been built over recent years from funded innovative
research from numerous organizations such as: EPA, USDA, NASA, and the private sector. Skills
and tools generated from these efforts directly apply to conducting work outlined in our proposal to
meet NASA’s needs.
The Stennis Team’s personnel assigned to this proposal represent over 200 years of experience in
NASA research and applications programs in the Earth-Sun science community. Over the last 25
years, Stennis Team personnel have worked at SSC, JSC, and MSFC to provide engineering and
other systems support to build, evaluate and benchmark decision support tools and systems. These
efforts used NASA assets including Landsat, MODIS, MATLAB, AVIRIS, and dozens of
engineering and analysis tools created by NASA’s space and Earth science programs.
The Stennis Team is committed to providing support for the Rapid Prototyping Capability in three
specific areas. First, we will develop tools for data simulation and assimilation. This will provide
simulated VIIRS and other future Earth-Sun System Sensor data sets as well as enhancement of
current Earth-Sun System Sensor data. Secondly, Stennis Team will establish the RPC system node
at the Stennis Space Center. Finally, we will engage in a variety of evaluation projects of the RPC
system.
The expected duration of this task is 24 months with 8 full time equivalents (FTEs) at a “not to
exceed” cost of $800,000. This cost includes labor, software, and travel. The partnership of
Mississippi State University, University of Mississippi, and the Stennis Team provides a tremendous
capability to fulfill NASA’s requirements.
Sincerely,
George May
President/CEO
Resumes
GEORESOURCES INSTITUTE
MISSISSIPPI STATE UNIVERSITY
Resumes
Edward B. Allen
Associate Professor and Graduate Coordinator
Department of Computer Science and Engineering
Mississippi State University
Box 9637, Mississippi State, MS 39762
Phone: 662-325-7449 Fax: 662-325-8997
Edward.allen@msstate.edu
Education
Ph.D., Computer Science, Florida Atlantic University, Boca Raton, FL, August 1995
M.S., Systems Engineering, University of Pennsylvania, Philadelphia, PA, December 1973
B.S., Operations research and electrical engineering, Brown University, Providence, RI, June 1971
Employment
Associate Professor, Department of Computer Science and Engineering, Mississippi State University,
2004present
Assistant Professor, Department of Computer Science and Engineering, Mississippi State University,
20002004
Research Associate, Department of Computer Science and Engineering, Florida Atlantic University,
19952000
Adjunct faculty, Department of Computer Science and Engineering, Florida Atlantic University, 1997
Research Assistant, Department of Computer Science and Engineering, Florida Atlantic University,
19941995
Teaching Assistant, Department of Computer Science and Engineering, Florida Atlantic University,
19931994
Systems Engineer, Glenbeigh, Inc., Jupiter, Florida, 19831992
Principal Engineer, Sperry Corp., Reston, Virginia, 1978–1983
Senior Associate, Planning Research Corp., McLean, Virginia, 1974–1978
Programmer, US Army Computer Systems Command, Fort Belvoir, Virginia, 1972–1974 (on active
duty)
Synergistic Activities —Awards (Selected)
Upsilon Pi Epsilon, 1994
Certificate in Data Processing (C.D.P.) from Institute for Certification of Computer Professionals, 1985
Ford Foundation Fellowship, 1971
cum laude, 1971
Tau Beta Pi, 1971
Sigma Xi Associate Member, 1971
Synergistic Activities — Professional Activities
Senior Member of Institute of Electrical and Electronics Engineers (IEEE) (1970present) and IEEE
Computer Society (1974present)
Member of the Association for Computing Machinery (ACM) (1980present)
Member of the American Scientific Affilation (1974–present)
Affiliated researcher of the NSF Center for Empirically Based Software Engineering (2001–present)
Affiliated researcher of the MSU Center for Computer Security Research (2003–present)
Affiliated researcher of the MSU Institute for Neurocognitive Science and Technology (2004–present)
Member of Program Committee and Session Chair, IEEE International Conference on Tools with
Artificial Intelligence, 2004
Member of Program Committee, International Symposium on Software Reliability Engineering
(ISSRE) sponsored by the IEEE Computer Society, 1999, 2001, 2002
Member of Program Committee, ACM Southeastern Conference, 2004
Member of Organizing Committee, Southeastern Software Engineering Conference (SESE), sponsored by
National Defense Industrial Association —Tennesee Valley Chapter, 2002
Reviewer for NSF, panels: 2001, 2002, 2003; individual proposal: 2001
Reviewer for the IEEE Transactions on Software Engineering, IEEE Transactions on Systems, Man, and
Cybernetics, Empirical Software Engineering, Annals of Software Engineering, Software Quality
Journal, Data and Knowledge Engineering, Transactions of the South African Institute of Electrical
Engineers, the International Symposium on Software Reliability Engineering (ISSRE), and Hawaii
International Conference on System Sciences.
Selected Publications
Amit A. Phadke and Edward B. Allen, “Predicting risky modules in open-source software for highperformance computing,” in Proceedings of the Second International Workshop on Software
Engineering for High Performance Computing System Applications, St. Louis, Missouri, May 2005,
Association for Computing Machinery, pp. 60–64.
Wei Li and Edward B. Allen, “An access control model for secure cluster-computing environments,” in
Proceedings of the Thirty-Eighth Annual Hawaii International Conference on System Sciences, Big
Island, Hawaii, Jan. 2005, University of Hawaii, p. 309, Full paper available on proceedings CD.
Edward B. Allen, “Measuring graph abstractions of software: An information-theory approach,” in
Proceedings: Eighth IEEE Symposium on Software Metrics, Ottawa, Canada, June 2002, IEEE
Computer Society, pp. 182–193.
Taghi M. Khoshgoftaar, Edward B. Allen, and Jianyu Deng, “Using regression trees to classify faultprone software modules,” IEEE Transactions on Reliability, vol. 51, no. 4, pp. 455–462, Dec. 2002.
Taghi M. Khoshgoftaar and Edward B. Allen, “A practical classification rule for software quality
models,” IEEE Transactions on Reliability, vol. 49, no. 2, pp. 209–216, June 2000.
Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, and John P. Hudepohl, “Classification-tree
models of software-quality over multiple releases,” IEEE Transactions on Reliability, vol. 49, no. 1,
pp. 4–11, Mar. 2000.
Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio, and Ronald Flass, “Process
measures for predicting software quality,” Computer, vol. 31, no. 4, pp. 66–72, Apr. 1998.
Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl, and Stephen J. Aud, “Applications of neural
networks to software quality modeling of a very large telecommunications system,” IEEE
Transactions on Neural Networks, vol. 8, no. 4, pp. 902–909, July 1997.
John P. Hudepohl, Stephen J. Aud, Taghi M. Khoshgoftaar, Edward B. Allen, and Jean Mayrand,
“Emerald: Software metrics and models on the desktop,” IEEE Software, vol. 13, no. 5, pp. 56–60,
Sept. 1996.
Taghi M. Khoshgoftaar, Edward B. Allen, Kalai S. Kalaichelvan, and Nishith Goel, “Early quality
prediction: A case study in telecommunications,” IEEE Software, vol. 13, no. 1, pp. 65–71, Jan. 1996.
Taghi M. Khoshgoftaar and Edward B. Allen, “Applications of information theory to software
engineering measurement,” Software Quality Journal, vol. 3, no. 2, pp. 79–103, June 1994.
Valentine Anantharaj
GeoResources Institute - Mississippi State University
Box 9652
Mississippi State, MS 39762-9652
Ph: (662)325-5135 Fax: (662)325-7692
VAL@GRI.MSSTATE.EDU
Education:
Ph. D Computational Engineering, Mississippi State University, Starkville, MS (2000 - Currently
Enrolled)
M.S. Meteorology, South Dakota School of Mines and Technology, Rapid City, SD, 1989
B.S. Physics, V.O. Chidambaram College – Madurai Kamaraj University, Tuticorin, India, 1979
Professional Experience:
Research Associate, GeoResources Institute, Mississippi State University, MS, 2002 - Present
Research Associate, Engineering Research Center, Mississippi State University, Stennis Space
Center, MS, 1999 - 2002
Senior Research Assistant and Research Assistant II, Center for Air Sea Technology,
Mississippi State University, Stennis Space Center, MS, 1992 - 1999
Associate Scientist II and I, Institute for Naval Oceanography, Stennis Space Center, MS, 1990 1992
Graduate Research Assistant, South Dakota School of Mines and Technology, Rapid City, SD,
1987 - 1989
Current Activities:
Aug 2003 – Aug 2005:

NASA EIGS Graduate Research Fellow.
July 2000 – Present:

Data Support Coordinator at the MSU GRI

Remote Sensing Data Processing and Analysis

Atmospheric modeling

Biosphere-Atmosphere Interaction Studies
Some Relevant Publications:
1. Anantharaj, V., P. J. Fitzpatrick, Y. Li, E. Johnson, and R. King, 2006 (manuscript in
preparation): An analysis of MODIS landuse data on a Gulf Coast sea breeze simulation.
Extended abstract to be submitted to the Proc. 10th Symposium on Integrated Observing and
2.
3.
4.
5.
6.
7.
8.
Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS), 29 January -2
February 2006, Atlanta, GA, Amer. Meteor. Soc.
Anantharaj, V. G., P. J. Fitzpatrick, R. L. King, and L. Wasson, 2004: Incorporation of MODIS
Landcover Data to Improve Land Surface Parameterization in the COAMPS Numerical
Weather Prediction Model. Proceedings of the Geoscience and Remote Sensing Symposium, 2004. IGARSS
'04, Anchorage, AK, v6: pp. 4095-4098.
Pradhan, P., R. King, T. Haupt, and V. Anantharaj, 2004: The Cyberinfrastructure and Image
Information Mining. Proceedings of the Geoscience and Remote Sensing Symposium, 2004. IGARSS '04,
Anchorage, AK.
Anantharaj, V.G.; Younan, N.H.; King, R.L, 2004: Nonparametric detection of cloudy pixels in
AVHRR NDVI data. Proceedings of the Geoscience and Remote Sensing Symposium, 2004. IGARSS '04,
Anchorage, AK, pp. 1037-1040.
Alper, N., R. Siquig, C. Stein, J. Kent, S. Lowe, J. Corbin, V. Anantharaj, B. Chambless and E.
Clark (1997). MEL: An Internet Based Distributed Geospatial Data Discovery and Retrieval
System, Proceedings of the 1997 Simulation Multi-conference-Atlanta, GA, SCS Simulation Series,(29)
N(4), Michael J. Chinni (Ed.), pp.183-188.
Kantha, L.H., P.E. Pontius, and V. Anantharaj (1993). Tides in Marginal, Semi-Enclosed and
Coastal Seas Part I: Sea Surface Height. University of Colorado Department of Aerospace Engineering
Sciences Technical Report, 31 Dec 1993, 300 pp.
Anantharaj, V., J.R. Miller, Jr., and P.C. Smith (1992). Scans of Summertime Convective Clouds
by a Ground-Based Dual-Channel Microwave Radiometer, Proceedings of the Specialist Meeting on
Microwave Radiometry and Remote Sensing, pp. 323-328.
Anantharaj, V. (1989). An Exploratory Study of the Summertime Observations by a Dual
Wavelength Microwave Radiometer, M.S. Thesis, South Dakota School of Mines and
Technology.
Shravan Kumar Durvasula
Research Associate II
Center for Advanced Vehicular Systems - Engineering Research Center
Mississippi State University
Box 9618, Mississippi State, MS 39762-9618
Phone: 662-325-5459 Fax: 662-325-5433
shravan@cavs.msstate.edu
Education
M.S., Computer Science and Engineering, Mississippi State University, Starkville, MS, Aug 2003
B.S., Computer Science and Systems Engineering, Andhra University, Visakhapatnam, A.P., India,
May 2000
Employment
Research Associate II, Center for Advanced Vehicular Systems, Engineering Research Center,
Mississippi State University, Aug 2003-Present
Research Assistant, Engineering Research Center, Mississippi State University, Aug 2002-Aug 2003
Java Programmer, Remote Sensing Technologies Center, Mississippi State University, May 2002-Aug
2002
Teaching Assistant, Department of Computer Science, Mississippi State University, Aug 2000-May 2002
Publications
T. Haupt, A. Kalyanasundaram, N. Ammari, K. Chandra, K. Das and S. Durvasula, “SPURport: Grid
Portal for Earthquake Engineering Simulations,” in International Conference, Atlanta, GA, USA, May 2225, 2005, Proceedings, Part I. SpringerNotes.
T. Haupt, S. Durvasula, D. Shulga, B. Sura, “Grid-based Simulation Environment for Onboard Fire
Network Model”, High Performance Computing Symposium 2005
T. Haupt, D. Shulga, B. Sura, S. Durvasula, P. Tatem, F. Williams, "Simulation Environment for Onboard
Fire Network Model Version 1.0 - User Manual", Naval Research Technical Report NRL/MR-MM/618004-8801
T. Haupt, D. Shulga, B. Sura, S. Durvasula, P. Tatem, F. Williams, "Simulation Environment for Onboard
Fire Network Model Version 1.0 -Theory Manual", Naval Research Technical Report NRL/MRMM/6180-04-8800
Gregory Lee Easson, Ph.D.
Department of Geology and Geological Engineering
The University of Mississippi
118E Carrier Hall
University, MS 38677
662-915-5995
Education
University of Missouri—Rolla, Department of Geological and Petroleum Engineering
Ph.D. in Geological Engineering, Spring 1996.
University of Missouri—Rolla, Master of Science, Geology, May 1984.
Southwest Missouri State University, Springfield, Missouri, Bachelor of Science, Geology
Employment
Teaching Experience
The University of Mississippi, Department of Geology and Geological Engineering
Associate Professor ,
July 2001 – present
Assistant Professor,
August 1995 – June 2001
 Director of the University of Mississippi Geoinformatics Center (UMGC); a NASAfunded center promoting research and education in the geospatial information
technologies. The UMGC is a multi-disciplinary endeavor that includes faculty and
graduate students from Biology, Computer and Information Science, Geology and
Geological Engineering, and Sociology and Anthropology;
 Associate Director of the Enterprise for Innovative Geospatial Solutions (EIGS); a
University of Mississippi initiative to coordinate the Geospatial Information Science and
Technology (GIS&T) programs on campus. The EIGS works with other research centers
and private companies to promote the development of a GIS&T research and business
cluster in Mississippi;
 Co-Principal Investigator on the City of Oxford Intelligent Transportation System (ITS)
project, designed to use advanced transportation modeling systems in a rural
environment;
 Teaching responsibilities include: introductory classes in GIS and RS, graduate courses in
spatial analysis and advanced remote sensing, and introductory geology for geology and
geological engineering students;
 Currently directing 6 Master’s and 4 Doctoral graduate research projects;
 Teaching responsibilities include: introductory and advanced classes in GIS/RS,
introductory Geology classes for majors and non-majors,
 GIS/RS research topics include: coastal change, coral reef assessment and mapping, and
integration of GIS/RS systems with decision support systems and models,
Recent Publications
Yarbough, Lance D., Greg Easson, Joel Kuszmaul, 2005, DN Based Tasseled Cap
Transform Coefficients for the ASTER Sensor Level 1-B Data, submitted to Remote
Sensing and the Environment
Hossain, A.K.M. Azad and Greg Easson, 2005, Detection of Levee Slides using
Commercially Available Remotely Sensed Data, accepted for publication Environmental
and Engineering Geoscience
Delozier, Scott and Greg Easson, 2004, Interpreting Spatio-Temporal Attributes fo
Dynamic Vehicles through Optical Satellite Imaging, presented at the American Society
of Photogrammetry and Remote Sensing meeting, Sept. 12-16, 2004, Kansas City, MO
Yarbrough, Lance and Greg Easson, 2004, Tasseled Cap Transform Coefficients for
the ASTER Sensor, presented at the American Society of Photogrammetry and Remote
Sensing meeting, Sept. 12-16, 2004, Kansas City, MO
Hossain, A.K.M. Azad, Greg Easson, Khaled Hasan, 2004, Inventory of Levee
Slides with Potential of Prediction using Commercially Available Remotely-sensed Data,
accepted for presentation at the American Society of Photogrammetry and Remote
Sensing Annual meeting, May 23-28, 2004, Denver, CO
Harris, Jesse L. and Greg Easson, 2004, A Superfund Environmental Evaluation
using Historical Aerial Photography and Imagery, accepted for presentation at the
American Society of Photogrammetry and Remote Sensing Annual meeting, May 23-28,
2004, Denver, CO.
Ingram, Stephen L., Greg Easson, Khaled Hasan, 2004, Remote Sensing Methods to
Integrate Landuse and Topographic Data to Map Surface Geology, accepted for
presentation at the American Society of Photogrammetry and Remote Sensing Annual
meeting, May 23-28, 2004, Denver, CO.
Delozier, Scott, Lance Yarbrough, Greg Easson, 2004, GIS in a Small Town;
Inventive Resources for Construction and Maintenance, Geoworld, March, 2004, Vol. 17,
No. 3, p. 42 – 45.
Robinson, Harold D., Greg Easson, Stephen Threlkeld, 2003, Using Remote Sensing
to Map Crop Distribution in a Diverse Agricultural Environment, American Society of
Photogrammetry and Remote Sensing Annual meeting, May 5-9, 2003, Anchorage, AK.
Jackson, Patrick, Greg Easson, Dawn Wilkins, 2003, Comparison of a Graph-based
Clustering Algorithm to k-means, k-medoid, and Isodata Algorithms Applied to Multispectral Imagery, American Society of Photogrammetry and Remote Sensing Annual
meeting, May 5-9, 2003, Anchorage, AK.
Tomasz A. Haupt
Associate Research Professor
Center for Advanced Vehicular Systems
Mississippi State University
Box 9652, Mississippi State, MS 39762-9652
Phone: 662-325-4524 Fax: 662-325-5433
haupt@cavs.msstate.edu
Education
Ph.D, Physics, of Nuclear Physics, Krakow, Poland, 1985
M.S., Physics, Jagiellonian University, Krakow, Poland, 1980
Employment
Research Associate Professor, Center for Advanced Vehicular Systems, MSU, May 2002-present
Research Associate Professor, Engineering Research Center, Mississippi State University, May 20012002
Research Engineer I, Engineering Research Center, Mississippi State University, May 2000–May 2001
Research Scientist, Northeast Parallel Architectures Center at Syracuse University, Jan. 1992 – April
2000
Research Associate, Physics Department, Syracuse University, December 1989-December 1991
Research Associate, NIKHEF (Dutch Institute for Nuclear and High Energy Physics), Nijmegen, the
Netherlands, May 1987 – December 1989
Research Associate, Institute of Nuclear Physics, Krakow, Poland, April 1981 – November 1987
Synergistic Activities – Professional Affiliations and Activities (selected)
Member of the program committees: 5th Int. Conf. on Innovative Internet Community Systems
(J2CS’05), Paris, France; 13th Mardi Gras Conference “Frontiers of Grid Applications and
Technologies”, Baton Rouge, LA High Performance Computing Symposium (HPC’05) San Diego, CA.
Program Chair, High Performance Computing Symposium (HPC’06) Huntsville, AL
Member of the Global Grid Forum (Nomination Committee), and Institute of Electrical and Electronic
Engineers (IEEE)
Selected Publications
T. Haupt, M. Pierce, “Distributed object-based grid computing environments”, book chapter in “Grid
Computing: Making the Global Infrastructure a Reality”, Fran Berman, Geoffrey Fox and Tony Hey eds,
John Wiley and Sons, 2003.
T. Haupt, P. Bangalore, G. Henley, “Mississippi Computational Web Portal”, Concurrency and
Computation: Experience and Practice, Grid Computing Environment Special Issue 13-14 (2002)
T. Haupt, E. Akarsu, G. Fox, Choon-Han Youn, “The Gateway System :uniform web based access to
remote resources”, Concurrency: Practice and Experience, 12 (2000) 629.
Tomasz Haupt, E. Akarsu, G. Fox, "WebFlow: a Framework for Web Based Metacomputing", Future
Generation Computer Systems, 16 (2000) 445-451
T. Haupt, E. Akarsu, G. Fox, W. Furmanski, "Web based metacomputing", Future Generation Computer
Systems, 15 (1999) 735-743.
.
T. Haupt, A. Kalyanasundaram, ”Using Secure Web Services for Development of a Grid Computing
Environment” in the proceeding of the High Performance Computing Symposium 2003, Orlando, FL
Tomasz Haupt, Purushotham Bangalore, Gregory Henley, “A Computational Web Portal for the
Distributed Marine Environment Forecast System” in the proceedings of International Conference on
High Performance Computing and Networking, Amsterdam’01.
T. Haupt, S. Durvasula, D. Shulga, B. Sura, “Grid-based Simulation Environment for Onboard Fire
Network Model” in the proceedings of High Performance Computing Symposium (HPC 2005), April 37, 2005, San Diego, 2005.
T. Haupt, A. Kalyanasundaram, N. Ammari, K. Chandra, “NEESport: Grid Portal for Earthquake
Engineering Community”, in the proceedings of the IASTED International Conference on Modeling and
Simulation, MS 2005, May 18-20, 2005, Cancun, Mexico
T. Haupt, A. Kalyanasundaram, N. Ammari, K. Chandra, K. Das, S. Durvasula, “SPURport: Grid Portal
for Earthquake Engineering Simulations” in the proceedings of the International Conference on
Computational Science, 2005, May 22-25, 2005, Atlanta, GA
T.J. Jankun-Kelly
Department of Computer Science and Engineering and GeoResources Institute
Mississippi State University
Box 9637, Mississippi State, MS 39762
Phone: 662-325-7504 Fax: 662-325-8997
tjk@cse.msstate.edu
Education
Ph.D., Computer Science, University of California, Davis, CA, June 2003
M.S., Computer Science, University of California, Davis, CA, September 1999
B.S., Physics and Computer Science, Harvey Mudd College, Claremont, CA, May 1997
Employment
Assistant Professor, Computer Science and Engineering, Mississippi State University, 2003-present
Synergistic Activities – Professional Affiliations and Activities (selected)
Institute of Electrical and Electronic Engineers (IEEE), Member
Association for Computing Machinery (ACM), Member
IEEE Visualization Conference (Contest Co-Chair 2004—2005)
National Science Foundation Workshop on Visualization for Computer Security (Program Committee
2005)
Selected Publications
M. J. Mohammadi-Aragh and T. J. Jankun-Kelly. MoireTrees: Visualization and Interaction for
Multi-Hierarchical Data. In Proceedings of the Joint Eurographics/IEEE vgtc Symposium on Data
Visualization 2005, pp. 231–238, 2005 (37/101 acceptance rate).
S. T. Teoh, T. J. Jankun-Kelly, K.-L. Ma, and S. F. Wu. Visual Data Analysis for Detecting Flaws and
Intruders in Computer Networks Systems. IEEE Computer Graphics and Applications, 24(5):27–35,
Sep./Oct. 2004.
T. J. Jankun-Kelly and K.-L. Ma. MoireGraphs: Radial Focus+Context Visualization and Interaction
for Graphs with Visual Nodes. In Proceedings of the IEEE Symposium on Information Visualization
2003, pp. 59–66, 2003 (23/90 acceptance rate).
T. J. Jankun-Kelly, O. Kreylos, J. M. Shalf, K.-L. Ma, B. Hamann, K. I. Joy, and E. Wes Bethel. Deploying Web-based Visual Exploration Tools on the Grid. IEEE Computer Graphics and Applications,
23(2):40–50, Mar./Apr. 2003.
T. J. Jankun-Kelly, K.-L. Ma, and M. Gertz. A Model for the Visualization Exploration Process. In
Proceedings of 13th IEEE Conference on Visualization, pp. 323–330, 2002 (58/178 acceptance rate).
T. J. Jankun-Kelly and K.-L. Ma. A Study of Transfer Function Generation for Time-Varying Volume
Data. Volume Graphics 2001: Proceedings of the Joint IEEE TCVG and Eurographics Workshop, pp.
51–68, 2001 (27/45 acceptance rate).
T. J. Jankun-Kelly and K.-L. Ma. Visualization Exploration and Encapsulation via a Spreadsheet-like
Interface. IEEE Transactions on Visualization and Computer Graphics, 7(3):275–287, Jul./Sep. 2001.
M. Jankun, T. J. Kelly, A. Zaim, K. Young, R. W. Keck, S. H. Selman, and J. Jankun. Computer
Model for Cryosurgery of the Prostate. Computer Aided Surgery, 4(4):193–199, 1999.
Anand K Kalyanasundaram
Research Associate I
Center for Advanced Vehicular Systems
Mississippi State University
Box 9618, Mississippi State, MS 39762- 9618
Phone: 662-325-5458 Fax: 662-325- 5433
anand@cavs.msstate.edu
Education
M.S., Electrical & Computer Engineering, Mississippi State University, Mississippi State, MS, December
2003
B.E., Electronics & Communication Engineering, Bharathidasan University, Trichy, Tamil Nadu, India,
May 2000
Employment
Research Associate I, Center for Advanced Vehicular Systems, Engineering Research Center,
Mississippi State University, 2004-present
Graduate Research Assistant, Engineering Research Center, Mississippi State University, 2000-2003
Publications
Tomasz Haupt, Anand Kalyanasundaram, Nisreen Ammari, and Krish Chandra, "NEESport: Gridportal
for Earthquake Engg Community," in Proc. of The16th IASTED Intl Conf on Modeling and Simulation,
May 18-20, 2005 Mexico.
Tomasz Haupt, Anand Kalyanasundaram, Nisreen Ammari, Krish Chandra, Kamakhya Das and Shravan
Durvasula, “SPURport: Grid Portal for Earthquake Engineering Simulations,” in Intl Conf, Atlanta, GA,
May 22-25, 2005, Proceedings, Part I. SpringerNotes.
Tomasz Haupt, Anand Kalyanasundaram, Nisreen Ammari, Archana Chilukuri and Maxim Khutornenko,
“SPURport (Status report),” in Proc. of case studies on grid applications workshop at Global Grid Forum
10 at Berlin, 2004.
Tomasz Haupt and Anand Kalyanasundaram, “Using Secure Web Services for Development of a Grid
Computing Environment,” in Proc. of the High Performance Computing Symposium 2003 at Orlando,
FL.
JOEL S. KUSZMAUL
ADDRESS:
Department of Geology and Geological Engineering
University of Mississippi
P.O. Box 1848
University, Mississippi 38677
Phone: (662) 915-7499
Fax: (662) 915-5998
E-mail: kuszmaul@olemiss.edu
EDUCATION:
Michigan Technological University
University of Minnesota, Minneapolis
University of California at Berkeley
Geological Engineering, B.S., 1981
GeoEngineering, M.S., 1983
Engineering-Civil Engineering, Ph.D. 1993
PROFESSIONAL EXPERIENCE:
Department of Geology and Geological Engineering
University of Mississippi
Associate Professor (2002 – present)
Assistant Professor (1998 - 2002)
Department of Mining, Minerals, and Materials
Engineering, University of Queensland,
Brisbane, Australia
Senor Lecturer (1995-1998)
Department of Geology and Geological Engineering
University of North Dakota
Assistant Professor (1993-1995)
Sandia National Laboratories, Albuquerque,
New Mexico
Member of Technical Staff (1983-1987)
SELECTED RELEVANT PUBLICATIONS:
M. Eileen Glynn and Joel Kuszmaul, “Prediction of Piping Erosion Along Middle Mississippi River
Levees—An Empirical Model,” Technologies and Operational Innovations for Urban Watershed
Networks Research Program Technical Report (ERDC/GSL TR-04-12), (Washington: U.S. Army Corps
of Engineers, 2004), 28 pp.
Matthews, John C., Clayton McKay, Joel S. Kuszmaul, Charles T. Swann, and Rick L. Ericksen,
“Chemical and radiological analyses of Mississippi hydrocarbon production brines, scales, and sludges,”
Proceedings of the 10th Annual International Petroleum Environmental Conference, November 2003, 34
p.
E. K. H. Goh, T. O. Aspinall, and J. S. Kuszmaul, “Spoil dump design and rehabilitation management
practice,” International Journal of Surface Mining, Reclamation, and Environment 12(2):57-60 (1998).
C. A. Stone, J. S. Kuszmaul, A. Boontun, and Dae Young, “Comparison of an analytical and a numerical
approach to probabilistic keyblock analysis,’ in Rock Mechanics: Tools and Techniques, M. Aubertin, F.
Hassani, and H. Mitri, editors (Rotterdam: A.A. Balkema, 1996), pp. 1769-1775.
Fractured and Jointed Rock Masses, Meyer, L.R. et al. J. S. Kuszmaul and R. E. Goodman, “An analytical
model for estimating keyblock sizes in excavations in jointed rock masses,” in Fractured and Jointed
Rock Masses, Meyer, L. R. Meyers, editor (Rotterdam: A.A. Balkema, 1995), pp. 19-26.
OTHER SIGNIFICANT PUBLICATIONS
J. S. Kuszmaul, “The effect of varied joint set orientation on keyblock size,” in Pacific Rocks 2000—
Rock Around the Rim, G. Girard, J. Liebman, C. Breeds, and T. Doe, editors (Rotterdam: A. A. Balkema,
pp. 953-958), July 2001.
J. S. Kuszmaul, “Estimating keyblock sizes in underground excavations: accounting for joint set
spacing,” International Journal of Rock Mechanics and Mining Sciences, 36:217-232 (1999).
J.S. Kuszmaul, “The role of discontinuities in rock slope stability and the use of probability bounds to
address design uncertainty,” Australian Geomechanics Journal 28(December, 1995):87-96.
J. S. Kuszmaul, “A new constitutive model for fragmentation of rock under dynamic loading,” in
Proceedings of the Second International Symposium on Rock Fragmentation by Blasting, W.L. Fourney
and R.D. Dick, editors (Rotterdam: A.A. Balkema, 1987), pp. 412-423.
L. M. Taylor, E. P. Chen, and J. S. Kuszmaul, “Microcrack-induced damage accumulation in brittle rock
under dynamic loading, Computer Methods in Applied Mechanics and Engineering 55(3):301-320, 1986.
SYNERGISTIC ACTIVITIES:
ABET Geological Engineering Program Evaluator (representing the Society for Mining, Metallurgy and
Exploration) for the Engineering Accreditation Council of ABET, 2000 – present.
Chair of the Scientific Review Committee for the 2003 Mississippi Region VII Science Fair for 2003,
reviewing the student documentation to ensure compliance with the event rules and procedures, recruiting
volunteer judges, and coordinating the two Region VII Science Fairs involving over 700 students.
Participated in open forum meetings and presented research findings of my investigation of naturally
occurring radioactive material within produced waters generated by the petroleum industry of Mississippi.
Presented findings at a meeting of industry participants (May 22, 2003) and to members of the general
public (August 14, 2003).
M DAVID LEWIS
EDUCATION
PhD
MS
BS
BS
Scientific Computing
Computer Science
Computer Science
Mathematics 1982
2000
1993
1984
University of Southern Mississippi
University of Southern Mississippi
University of Oklahoma
University of Oklahoma
WORK HISTORY
2001 – Present
1997 – 2000
1986 – 1997
1984 - 1986
1981 - 1984
Vice-President
Research Scientist
Systems Administrator
Software Programmer
Research Assistant
Institute for Technology Development
Institute for Technology Development
Institute for Technology Development
Now Weather Inc
National Severe Storms Laboratory
RESEARCH FOCUS
Project management: Remote Sensing, Geographic Information Systems, Global Positioning Systems
research.
Meteorology: Meteorological research collaboration using Doppler radar includes studies in gust fronts,
ionization patterns following lightning strikes and mesocyclone vortex signatures.
Software Development: Includes implementing classification strategies using neural networks, image
distortion removal techniques and GIS vector based node transversal for commodity routing.
Remote Sensing/GIS/GPS: Direct project management includes systems administration, crop hybrid
classification and precision agriculture research for variable rate application of plant growth regulator
research in cotton. Additional airborne and satellite remote sensing research collaboration includes
precision agriculture research in insecticide, herbicide and defoliant applications in cotton.
PATENTS/AWARDS
March 2004 : Patent: “Method and Apparatus for Spatially Variable Rate Application of Agricultural
Chemicals Based on Remotely Sensed Vegetation Data.”
2005 Inducted into Space Technology Hall of Fame, U.S. Space Foundation
MEMBERSHIPS
American Society for Photogrammetry and Remote Sensing (ASPRS)
JOURNAL OR CONFERENCE ARTICLES
Hruska, Z., H. Yao, K. DiCrispino, K. Brabham, D. Lewis, J. Beach, R. L. Brown, and T. E. Cleveland.
2005. Hyperspectral Imaging of UVR Effects on Fungal Spectrum. Proc. of SPIE.
Yao, H., Z. Hruska, K. DiCrispino, K. Brabham, D. Lewis, J. Beach, R. L. Brown, and T. E. Cleveland.
2005. Differentiation of Fungi Using Hyperspectral Imagery for Food Inspection. ASAE meeting paper
No. 053127. St. Joseph, MI: ASAE.
Lewis, M.D., M. Seal, and K. DiCrispino. “Spatially Variable Plant Growth Regulator (SVPGR)
Applications Based on Remotely Sensed Imagery”, Proceedings of the 2002 Beltwide Cotton Conference.
Lewis. M.D., J Fridgen, J. Johnson, and K. Hood. “Image-based Site-specific Plant Growth
Regulator (SSPGR) Applications at Perthshire Farms.” Proceedings of the 2003 Beltwide Cotton
Conference.
Carter, G. A., T. M. Wells, and D. Lewis. 2004. Hyperspectral remote sensing of invasive
wetland plants in northern Mobile Bay. Abstracts of the Centennial Meeting, Association of
American Geographers, March 14-19, 2004, Philadelphia, PA, p. 68.
Bethel, M.T., M.D. Lewis, S. White, T. Sheely, B. Roberts, R. Hewitt, M. Paggi, and N. Groenenberg.
2004. Image-Based, Variable Rate Plant Growth Regulator Applied by Air to California Cotton. In
Proceedings of the 2004 Beltwide Cotton Conference. San Antonio, Texas.
Yao, H., H. Hruska, K. DiCrispino, D. Lewis, and J. Beach. 2004. Hyperspectral Imagery for
Characterization of Different Corn Genotypes. Proceedings of SPIE Conference Volume 5587,
October 26-27, 2004, Philadelphia, Pennsylvania.
Fridgen, J.J., D.B. Reynolds, M.D. Lewis, and K.B. Hood. 2003. Use of remotely sensed imagery for
variable rate application of cotton defoliants. [CD-ROM] In Proc. 2003 Beltwide Cotton Conf. National
Cotton Council of America, Memphis, TN.
Fridgen, Jon, Michael Seal, David Lewis, and Kenneth Hood, “Farm Level Spatially Variable Insecticide
Applications Based on Remotely Sensed Imagery”, Proceedings of the 2002 Beltwide Cotton Conference.
Seal, Michael, Kelly Dupont, Matthew Bethel, David Lewis, and Jim Johnson, Jeffrey L. Willers,
Kenneth Hood, Roger Leonard, and Ralph Bagwell, “Utilization of Remote Sensing Technologies in the
Development and Implementation of large-scale Spatially Variable Insecticide Experiments in Cotton”,
Proceedings of the 2001 Beltwide Cotton Conference, Anaheim, California
GEORGE A MAY
EDUCATION
Ph.D. 1976
M.S. 1973
B.S.
1971
Agronomy
Agronomy
Agronomy
Pennsylvania State University
Pennsylvania State University
Pennsylvania State University
WORK HISTORY
2003 – Present
1998-2003
1985-1998
1976-1985
President/CEO
Vice President
Division Director
Remote Sensing Specialist
Institute for Technology Development
Institute for Technology Development
Institute for Technology Development
USDA, National Agriculture Statistics
Service (NASS)
PROFESSIONAL AWARDS
2005
2004
2003
Inducted into Space Technology Hall of Fame, U.S. Space Foundation
Penn State College of Agriculture Sciences Outstanding Alumnus Award
Penn State Department of Crop and Soil Sciences Outstanding Alumni Award
PROFESSIONAL POSITIONS/MEMBERSHIP
2002-2003
1994-1995
1992-1993
Chairman, Board of Director, InTime, Inc.
Acting President, Resource21, Inc.
Board of Director, Automated Mapping Facilities Management International
PAPERS
May, G. 2005. (Invited Paper) Hyperspectral Imaging: Your Commercial Opportunity. World’s Best
Technologies 2005 Conference. March 29-31, Dallas, Texas.
May, G., R. McKellip, and M. Seal. 2005. Real Producer Profits Using Real-time Geospatial
Technologies. Global Conference on Geospatial Tools and Solutions, February 3-16, Vancouver, B.C.
May, G. and B. Mitchell. 2004. Imaging Beyond What Man Can See. Monitoring Science and
Technology Symposium. September 21-24, Denver, CO.
May, G.A. 1998. Making Sense of Remote Sensing, Precision Ag Illustrated. Vol. 2(1): 42-45.
Heitschmidt, J., M.A. Lanoue, C. May and G.A. May. Hyperspectral Analysis of Fecal Contamination:
A Case Study of Poultry. In Proceedings of the 1998 SPIE Photonics East Conference, November,
Boston, MA.
Gress, T., G. May, C. Mao, J. Heitschmidt and K. Copenhaver. 1996. “A Summary of Agricultural
Remote Sensing Activities Conducted at NASA’s Space Remote Sensing Center”. Proceedings of the
International Conference for the Use of Aircraft Remote Sensing Data for Agriculture, Bernburg,
Germany, September 4-7, Vol., p. 43-61.
Grace, J. and G. May. 1994. Real Time Environmental and Agricultural Monitoring from Aircraft and
Spacecraft. Remote Sensing of Soils and Vegetation. Elsevier Publications, Vol 3. 23-27.
May, G.A. and M.L. Holko. 1986. Landsat Large-Area Estimates for Land Cover. IEEE Geoscience and
Remote Sensing. GE 24(1): 175-184
May, G.A., M.L. Holko, and N.M. Jones. 1986. Missouri Crop and Landover Study. NASS, RSB-86-01,
USDA. 34pp.
May, G.A. and R.D. Allen. 1981. Non-Sampling Errors in Non-Agricultural Strata of 1980 June
Enumerative Survey. NASS, USDA. 12 pp.
Aaronson, A.C., G.A. May and L.L. Davis. 1979. Vegetation Index Correlation Study. Foreign
Agricultural Service, USDA. 70pp.
Petersen, G.W. and G.A. May. 1976. New Techniques Utilized for Land Cover Mapping. Science and
Agriculture. 23(2): 1-3.
May, G.A. and G.W. Petersen. 1975. Spectral Signature Selection for Mapping Unvegetated Soils.
Remote Sensing of Environment. 4(2): 211-220.
Robert James Moorhead II
Billie J. Ball Professor
Department of Electrical and Computer Engineering and GeoResources Institute
Mississippi State University
Box 9652, Mississippi State, MS 39762-9652
Phone: 662-325-2850 Fax: 662-325-7692
rjm@gri.msstate.edu
Education
Ph.D., Electrical & Computer Engineering, North Carolina State University, Raleigh, NC, August 1985
M.S., Electrical Engineering, North Carolina State University, Raleigh, NC, May 1982
B.S., Electrical Engineering, Geneva College, Beaver Falls, PA, May 1980
Employment
Deputy Director, Computational GeoSpatial Technologies Center, Engineering Research Center,
Mississippi State University, 2001-2002
Director, Visualization, Analysis, and Imaging Laboratory, Engineering Research Center, Mississippi
State University, 1999-present
Professor, Electrical and Computer Engineering, Mississippi State University, 1998-present
Associate Professor, Electrical and Computer Engineering, Mississippi State University, 1993-1998
Summer Faculty (Engineer), Naval Oceanographic Office, SSC, MS, 1991-1994, 1996-1998
Scientific Visualization Research Thrust Leader, National Science Foundation Engineering Research
Center for Computational Field Simulation, Mississippi State University, 1990-2001
Assistant Professor, Electrical and Computer Engineering, Mississippi State University, 1989-1993
Research Staff Member, IBM T. J. Watson Research Center, Image Technologies, 1985- 1988
Synergistic Activities – Awards (selected)
Centers and Institutes Faculty Research Award, Mississippi State University, 2005
IEEE Computer Society Golden Core Member, presented June 2005
IEEE Computer Society Meritorious Service Award, 2004
Co-Chair, NIH/NSF Panel on Visualization Research Challenges, 2004-2005
Associate Editor, IEEE Transactions on Visualization and Computer Graphics, 1999-2002
Associate Editor, Journal of Electronic Imaging, 1995-1999
Hearin-Hess Distinguished Professor, Mississippi State University, 1997-98, 96-97, 95-96, 94-95, 93-94.
Outstanding Engineering Research Award, College of Engineering, 1996
Synergistic Activities – Professional Affiliations and Activities (selected)
Institute of Electrical and Electronic Engineers (IEEE), Senior Member
IEEE Computer Society Technical Committee on Visualization and Computer Graphics (ExCom, 1998present; Vice-Chair for Conferences, 1997-1998; Chair, 1999-2000; Director, 2000-present)
IEEE Visualization Conference (Publicity Chair, 1995; Program Chair, 1996; Conference Chair, 1997;
Conference Co-Chair, 1998; Papers Co-Chair, 2002 and 2003)
Selected Publications
Jean Mohammadi-Aragh, Derek Irby, Ikuko Fujisaki, Robert Moorhead, David Evans, and Scott Roberts,
“Visualization of Computer-Modeled Forests for Forest Management,” EuroVis2005, pp. 183-190.
Zhanping Liu and Robert Moorhead, “A Texture-Based Hardware-Independent Technique for TimeVarying Volume Flow Visualization,” Journal of Visualization, Vol. 8, No. 3, July 2005.
Philip Amburn, Thomas Broadstock, Michael Chupa, Richard Luczak, Jamie Moyers, David R. Pratt,
Richard Schumeyer, Tim Menke, and Robert Moorhead, “Simplifying and Evaluating Threat and Target
Models for Constructive and Virtual Simulations,” ITEA Journal, (26)2, June/July 2005, pp. 73-78.
Joel P. Martin, Robert J. Moorhead, Michael A. Chupa, and Derek W. Irby, “Interactive, Cluster-based
Visualization,” in Proc. High Performance Computing Symposium 2005, M. Parashar (ed.), Soc. For
Modeling and Simulation International, San Diego, CA, pp. 203-208, April 2005.
Zhanping Liu and Robert Moorhead, “Accelerated Unsteady Flow Line Interval Convolution,” IEEE
Transactions on Visualization and Computer Graphics, Vol. 11, No. 2, pp. 113-125.
Michael A. Chupa, Jace A. Mogill, Derek W. Irby, Robert J. Moorhead, Jay F. Shriver, and Peter M.
Flynn, “EnVis/Hum: High-resolution Ocean Model Visualization and Display,” Association of American
Geographers 100th Annual Meeting, March 14-19, 2004, Philadelphia, PA.
Ikuko Fujisaki, David L. Evans, Robert J. Moorhead, Mahnas Jean Mohammadi-Aragh, Derek W. Irby,
and Scott D. Roberts, "Human Factor Integration into the Development of a Realistic Tree Rendering
System Based on LiDAR Remote Sensing," IS&T/SPIE 16th Annual Electronic Imaging Symposium,
San Clara, CA, January 18-22, vol. 5291, 2004.
S.B. Ziegeler, G.P. Gopal, E. Blades, R.J. Moorhead, D.L. Marcum, and Y.Guan, “Visualization of Fluid
Flows in Virtual Environments,” Journal of Visualization, Vol. 7, No. 1, 2004, Frontispiece (invited).
Mahnas Jean Mohammadi-Aragh, Robert J. Moorhead, Derek W. Irby, David L. Evans, Ikuko Fujisaki,
and Scott D. Roberts, "Rendering Realistic Pine Trees from LIDAR Data in a Virtual Forest,” ASPRS
Annual Conference, Fairbanks, Alaska, May 5-9, 2003.
R.J. Vickery, T.R. Keen, R.J. Moorhead, J. Meyer, R.J. Brou, A.M. Noble, J.P. Martin, and S.M. Doane,
“Particle Rendering for 5D Scalar Fields in a Virtual Environment: The Need for Speed,” High
Performance Computing Symposium, March 30 – April 3, 2003, Orlando, FL.
R. Scoggins, R. Machiraju, and R.J. Moorhead, “Approximate Shading for the Re-Illumination of
Synthetic Images,” IEEE Visualization 2001, Oct. 2001, pp. 379-386.
S. Ziegeler, R.J. Moorhead, P. Croft, and D. Lu, “The MetVR Case Study: Meteorological Visualization
in an Immersive Virtual Environment,” IEEE Visualization 2001, Oct. 2001, pp. 489-492.
R.J. Vickery, R. J. Brou, D. W. Carruth, T.R. Keene, R. J. Moorhead, and S. M. Doane, “Usability Issues
in the Development of an Immersive System for Visualization of 5D Sediment Models,” Ninth
International Conference on Human-Computer Interaction, New Orleans, LA, August 5-10, 2001, Poster
Sessions: Abridged Proceedings, pp. 62-64.
R. Scoggins, R. Machiraju, and R.J. Moorhead, “Enabling Level-of-Detail Selection for Exterior Scene
Synthesis,” IEEE Visualization 2000, Oct. 2000, pp. 171-178.
M. Watson, N. Eggleston, D. Irby, R. Moorhead, D. Evans, “A Virtual Reality Interface for Analyzing
Remotely Sensed Forestry Data,” SIGGRAPH 2000 Conference Abstracts and Applications, Sketches
and Applications, New Orleans, LA, July 23-28, 2000, p. 273.
R. Machiraju, Z. Zhu, B. Fry, and R.J. Moorhead, “Structure Significant Representation of Structured
Datasets,” IEEE Transactions on Visualization and Computer Graphics, April - June 1998, Vol. 4, No. 2,
pp. 117-133.
R.J. Moorhead and Z. Zhu, “Signal Processing Aspects of Scientific Visualization,” IEEE Signal
Processing Magazine, Vol. 12, No. 5, Sept. 1995, pp. 20-41.
A. Johannsen and R.J. Moorhead, “AGP: Ocean Model Flow Visualization,” IEEE Computer Graphics
and Applications, Vol. 15, No. 4, July 1995, pp. 28-33.
Charles G. O’Hara
Associate Research Professor
#2 Research Boulevard
Mississippi State, MS 39762
cgohara@GRI.MSState.Edu
662.325.2067
Education
B.S.
M.S.
Ph.D.
United States Merchant Marine Academy, Kings Point, New York, 1986
University of Mississippi, University, Mississippi, 1992
University of Mississippi, University, Mississippi, 1994
Principal Responsibilities
Associate Research Professor, Mississippi State University, GeoResources Institute
Consortium Manager, National Consortium for Remote Sensing in Transportation – Environmental
Assessment
Work Experience
2003 – 2005
Mississippi State University, GeoResources Institute
2000 - 2003
Mississippi State University, Engineering Research Center,
Computational Geospatial Technologies Center
1999 - 2000
U.S. Geological Survey, Water Resources Division,
Office of Information, Systems Support Unit, Acting Chief
1995 - 2000
University of Mississippi, Jackson Graduate Engineering Program, Adjunct Professor,
Geographic Information Systems and Remote Sensing Technology
1992 - 2000
U.S. Geological Survey, Project Chief, GIS Specialist and Hydrologist
1989 - 1992
University of Mississippi, Research Assistant
1989 - 1990
Hudson Bay Mining, Geological and Geophysical Engineering
1987 - 1988
Marine Drilling Company, Engineering
1986 - 1987
Offshore Mining Company, Ocean and Geophysical Engineering
Relevant Memberships/Committees
GIS – Transportation Symposium, Workshop Planning Committee
U.S. Geological Survey, Windows 2000 Investigation Team (WIT), Chairman
U.S. Geological Survey, Technical Advisory Committee (TAC), Chairman
U.S. Geological Survey, Future of Computing (FUTCOM), Committee
U.S. Geological Survey, National GIS Instructor and GIS-Help Member
Recent Honors and Awards
Department of Interior, Secretary’s Distinguished Science Unit Award, 2000
U.S. Geological Survey, STAR Award, 1999
Environmental Protection Agency, Scientific Excellence Award, 1999
Selected Publications
O’Hara, Charles G., 2005, “New NOAA Data Sources, Programs Enhance Coastal Zone Managemen.,”
EarthImaging Journal, July 2005
O’Hara, Charles G, (2005). “Managing Dynamic Data: Change Detections and Feature Extraction
Methods Supporting Census Bureau Map Updates.” EarthImaging Journal, March/April 2005, Vol. 2,
pp. 40-43.
Xudong Z., Younan, N.H., and O’Hara, C.G., 2005, “Wavelet Domain Statistical Hyperspectral Soil
Texture Classification.” IEEE Transactions in Geosciences and Remote Sensing, March 2005,
Volume 43, Number 3 (ISSN 0196-2892)
Willers, J.L., Milliken, G.A., O’Hara, C.G., Jenkins, J.N., 2004, “Information Technologies and the
Design and Analysis of Site Specific Experiments within Commercial Cotton Fields.” Proceedings of
the 2004 Conference on Applied Statistics in Agriculture, Kansas State University.
O’Hara, C.G. and Willers, J.L., and Milliken, 2004, “Spatial Measurement Parameters for Characterizing
Precision Agriculture.” Proceedings of the Conference on Applied Statistics in Agriculture, Kansas
State University.
Milliken, G.A., Willers, J.L., and O’Hara, C.G., 2004, “Design and Analysis of Experiments to Evaluate
Treatments for Precision Agriculture.” Proceedings of the Conference on Applied Statistics in
Agriculture, Kansas State University.
Repaka, S.R., Truax, D.D., Kolstad, E., and O’Hara, C.G., 2004, “Comparing Spectral and Object Based
Approaches for Classification and Transportation Feature Extraction from High Resolution
Multispectral Imagery.” Proceedings of the American Society of Photogrammetry and Remote
Sensing.
Kim, S.J., Olson, G.A., and O’Hara, C.G., 2004, “Multi-Temporal Satellite Image Normailzation for
Change Detection,” Proceedings of the American Society of Photogrammetry and Remote Sensing.
Mali, P., O’Hara, C.G., Viger, R., Mennis, J., Srestha, B, and Vijayraj, V., 2004, “Vegetation Index
Compositing and Analysis in Spatial and Temporal Dimensions.” Proceedings of the American
Society of Photogrammetry and Remote Sensing.
Vijayaraj, V., O’Hara, C.G., and Younan, N.H., 2004, “Quality Metrics for Multispectral Image
Processing.” Proceedings of the American Society of Photogrammetry and Remote Sensing.
Seo, S. and C.G. O’Hara, 2004, “Quantifying Linear Feature Extraction Performance,” Proceedings of the
American Society of Photogrammetry and Remote Sensing, Denver, May 2004.
Srestha, B, O’Hara, C.G., Younan, N.H., 2004, “JPEG2000: Image Quality Metrics,” Proceedings of the
American Society of Photogrammetry and Remote Sensing.
Vijayaraj, V., O’Hara, C.G., and Younan, N.H., 2004, “Quality Metrics for Multispectral Image
Processing.” Proceedings of the American Society of Photogrammetry and Remote Sensing.
Vijayaraj, V., O’Hara, C.G., and Younan, N.H., 2004, “Comparison of Pansharpening Algorithms.”
Proceedings of the IEEE Geosciences and Remote Sensing Symposium.
Xudong Z., Younan, N.H., and O’Hara, C.G., 2004, “Wavelet-Based Hyperspectral Soil Texture
Classification Using Hidden Markov Model.” Proceedings of the American Society of
Photogrammetry and Remote Sensing.
O’Hara, C. G. and King, R.L., 2003, “A Computational Mapping Engine Portal for Accessing
GeoLibraries.” Proceedings of the IEEE Geosciences and Remote Sensing Symposium.
O’Hara, C.G., King, R.L., Cartwright, J.H., and King, J.S., 2003, “Multitemporal Land Use and Land
Cover Classification of Urbanized Areas within Sensitive Coastal Environments.” IEEE Transaction
in Geosciences and Remote Sensing.
O’Hara, C.G., King, R.L., Cartwright, J.H., and King, J.S., 2002, “Remote Sensing and Geospatial
Technologies for Developing Options to Relocate CSX Railroad from Mississippi Gulf Coast
Townships.” Proceedings of 2002 ASME International Mechanical Engineering Congress and
Exposition (CD), New Orleans, Louisiana.
King, R.L., and O’Hara, C.G., 2002, “A Synthesis of Remote Sensing Applications for Environmental
Assessment [in Transportation].” Proceedings of the 15th William T. Pecora Memorial Remote
Sensing Symposium/Landsatellite Information IV Conference (CD), Denver Colorado.
ROBERT E. RYAN
Building 1105
Stennis Space Center, MS 39529
228-688-1868 rryan@ssc.nasa.gov
SUMMARY OF QUALIFICATIONS
An established background encompassing engineering, research and development, strategic planning,
and project and personnel management, complemented by expertise in remote sensing, optics,
spectroscopy, laser technology, modeling, and physics. Earned 9 U.S. patents and published over 35
articles and final reports.
PROFESSIONAL EXPERIENCE
Science Systems and Applications, Inc.
2004 – Present
Applied Sciences Directorate, Stennis Space Center, MS 39529
Section Manager/Senior Staff Scientist
 Develop, manage, track, and report on all technical and operational aspects of the Systems
Engineering activities assigned to NASA’s Applied Sciences Directorate.
 Technical lead for NASA Return-to-Flight and Exploration Systems activities.
University of New Orleans
2003 – Present
Adjunct Faculty Member, Electrical Engineering Dept.
 Advise graduate students on research projects.
Lockheed Martin Space Operations 1998 – 2004
Remote Sensing Directorate, Stennis Space Center, MS 39529
Manager, Systems Engineering Group
 Developed, managed, tracked, and reported on all technical and operational aspects of the
Systems Engineering activities assigned to NASA’s Earth Science Applications Directorate.
 Received Lockheed Martin Space Operations 2001 Top Flight Award for Leadership.
 Received Lockheed Martin Space Operations Manager of the Quarter Award in 2001.
 Served as American Society of Photogrammetry and Remote Sensing Digital Imagery Guideline
Chairman.
 Led the development of a NASA Stennis commercial remote sensing calibration and validation
laboratory.
 Led the development of a systems engineering approach to National Applications.
Northrop Grumman 1983 – 1998
Corporate Research Center, Bethpage, NY 11714
Program Manager, Sensor Group (final position)
1994 – 1998
 Coordinated all phases in the research and development of optical sensor technology involving
spectral imaging sensors for remote sensing applications.
 Codeveloped winning $6.5 million DARPA Self Decontaminating Material Biological Defense
contract.
 Served as Lead Optical Engineer on an $8 million optical reconnaissance system project.
 Served as principal investigator on the development of a LWIR Fabry-Perot Hyperspectral
Imager.
 Developed Fourier Transform spectrometer systems for chemical weapon detection and
identification.
 Performed on-site evaluations of acquisition targets, producing comprehensive reports on
companies' viability based on their technical and research resources.
State University of New York at Stony Brook, Stony Brook, NY
Visiting Faculty Member, Physics
Hofstra University, Uniondale, NY
Adjunct Professor of Physics
1993 – 1998
1988 – 1991
EDUCATION
State University of New York at Stony Brook, Stony Brook, NY
Polytechnic Institute of New York, New York, NY
Hofstra University, Uniondale, NY
Ph.D. Physics
1993
M.S. Electrophysics 1985
B.S.
Physics
1982
Recent Publications/Presentations/Posters (by date)
Pagnutti, Mary, Kara Holekamp, Robert E. Ryan, Thomas Stanley, Vicki Zanoni, Ronald D. Vaughan, Jr.,
Kurtis Thome, Stephen Schiller, and David Aaron, 2005. NASA IKONOS multispectral radiometric
calibration and 5-year temporal stability assessment (presentation). CALCON Technical Conference
on Characterization and Radiometric Calibration for Remote Sensing, Aug 22–25.
Ryan, Robert E., Mary Pagnutti, Slawomir Blonski, Kenton W. Ross, and Thomas Stanley, 2005. High
spatial resolution commercial imaging product characterization (presentation). CALCON Technical
Conference on Characterization and Radiometric Calibration for Remote Sensing, Aug. 22–25.
McKellip, Rodney, Robert E. Ryan, Don Prados, and Slawomir Blonski, 2005. Crop surveillance
demonstration using a near-daily MODIS vegetation index time series. In 2005 International
Workshop on the Analysis of Multi-Temporal Remote Sensing Images. New York City: IEEE.
Pagnutti, Mary, Kara Holekamp, Robert E. Ryan, Ronald Vaughan, Jeff Russell, Don Prados, and
Thomas Stanley, 2005. Atmospheric correction of high-spatial-resolution commercial satellite
imagery products using MODIS atmospheric products. In 2005 International Workshop on the
Analysis of Multi-Temporal Remote Sensing Images. New York City: IEEE.
Berglund, Judith, Robert E. Ryan, Mary Pagnutti, Kenton Ross, Vicki Zanoni, Thomas Stanley, 2005.
Quality assessment and characterization of multiple-return and waveform LIDAR data for carbon and
water cycle research (presentation). International LIDAR Mapping 2005 Conference, April 25–26.
Ryan, Robert E., 2004. NASA JACIE plan for characterizing commercial data products in support of
carbon/water cycle research (presentation). Proceedings of the 2004 High Spatial Resolution
Commercial Imagery Workshop, November 8–10, Reston, VA, USA (sponsored by
NASA/NIMA/USGS Joint Agency Commercial Imagery Evaluation Team), CD-ROM.
Pagnutti, Mary, David Carver, Kara Holekamp, Kelly Knowlton, Dean Noel, Robert Ryan, Vicki Zanoni,
Kurtis Thome, Stephen Schiller, and David Aaron, 2003. NASA IKONOS multispectral radiometric
calibration and 3-year temporal stability assessment (presentation). ISPRS Commission I/Working
Group 2 International Workshop on Radiometric and Geometric Calibration, Dec. 2-5.
Ryan, Robert, 2003. Parameters describing Earth observing remote sensing systems (presentation). ISPRS
Commission I/Working Group 2 International Workshop on Radiometric and Geometric Calibration,
Dec. 2-5.
Zanoni, Vicki, Mary Pagnutti, Robert Ryan, Greg Snyder, William Lehman, and Spencer Roylance, 2003.
The Joint Agency Commercial Imagery Evaluation Team and Product Characterization Approach.
Proceedings of the ISPRS Commission I/Working Group 2 International Workshop on Radiometric
and Geometric Calibration, Dec. 2-5.
Goward, Samuel N., John R.G. Townshend, Vicki Zanoni, Fritz Policelli, Tom Stanley, Robert Ryan,
Kara Holekamp, Lauren Underwood, Mary Pagnutti, and Rose Fletcher, 2003. Acquisition of Earth
science remote sensing observations from commercial sources: Lessons learned from the Space
Imaging IKONOS example. In IKONOS Fine Spatial Resolution Land Observation, eds. S.N.
Goward and V. Zanoni, special issue, Remote Sensing of Environment, 88(1-2):209–219.
SHAW, DAVID R.
Education: B.S. in Agriculture, Cameron University-1981; M.S. in Agronomy-Weed Science, Oklahoma
State University-1983; Ph.D. in Crop Science-Weed Science, Oklahoma State University-1985
Experience: Mississippi State University-Assistant Professor (1985-1989); Associate Professor (19891991); Professor of Weed Science (1991-1999); Giles Distinguished Professor of Weed Science (1999Present); Director, Remote Sensing Technologies Center (1999-2002); Director, GeoResources Institute
(2002-Present)
Committee and Leadership Positions:
University Council on Water Resources
Board of Directors, 2003-Present
National Institutes of Water Resources
Member, 2003-Present
Weed Science Society of America
Member, Board of Directors, 1997-2002; Newsletter Editor, 1993-Present; Resolutions
Committee, 1989-1993 (Chair, 1990-93); Membership Committee, 1989-1994 (Chair, 1991-94);
Program Committee, 1991-1992; Awards Committee, 1995-Present; Reviewer, Weed Science;
Reviewer, Weed Technology
Southern Weed Science Society
President, 2005; Director, Academia, 1991-93; Secretary/Treasurer, 1993-1996; Awards
Committee, 1991-1994; Weed Contest Committee, 1985-Present (Chair, 1995-Present);
Graduate Contest Committee, 1987-90 (Chair, 1988); Program Committee, 1988, 1992, 1998;
Newsletter Committee, 1988-Present (Editor, 1989-1992)
University
Advisor and Coach, MSU Graduate Student Weed Contest Team, 1986-Present (placed in top 3
for past 10 years); Chair, Agricultural Pest Management Steering Committee; Co-host and
organizer, Mississippi Weed Training Workshop, 1990-1996; Member, Faculty Senate, 1994Present; Charter & By-Laws Committee (Chair, 1997-98); Development Committee,
Environmental Sciences Curriculum
Membership: Fifteen State, Regional, National and International Scientific, Professional, and Honor
Societies
Graduate Student Responsibilities: Major Advisor for 41 M.S. and 24 Ph.D.
Honors and Awards: Outstanding Young Weed Scientist, Southern Weed Science Society, 1994;
Outstanding MAFES Scientist, 1994; Alpha Zeta Outstanding Advisor Award, 1994; Outstanding Young
Weed Scientist Award, Weed Science Society of America, 1996; Research Award, Mississippi State
University Alumni Association, 1997; Grantsmanship Award, Mississippi Agricultural & Forestry
Experiment Station, 1997; Outstanding Educator Award, WSSA, 1998; Graduate Level Teaching Award,
MSU Alumni Association, 1998; William L. Giles Distinguished Professor of Weed Science, 1999;
Outstanding Alumnus Award, Cameron University, 1999; Weed Scientist of the Year, Southern Weed
Science Society, 2000; Ralph E. Powe Research Award, Mississippi State University, 2000; Research
Award, Southern Weed Science Society, 2002; Fellow, Weed Science Society of America, 2002;
Research Award, Weed Science Society of America, 2003
Refereed Publications: 142
Experiment Station/Extension Bulletins: 32
Abstracts from Presentations: 308
Total Grant Funding to Date: $38,700,662
Selected Publications:
Baughman, T. A., D. R. Shaw, E. P. Webster, and M. Boyette. 2001. Effect of cotton (Gossypium
hirsutum) tillage systems on off-site movement of fluometuron, norflurazon, and sediment in runoff. Weed
Technology 15:184-189.
Gao, W., and D. R. Shaw (editors). 2003. Ecosystems’ dynamics, agricultural remote sensing and
modeling, and site-specific agriculture. SPIE – the International Society for Optical Engineering
Conference, 7 August, 2003, San Diego, CA.
Koger, C. H., D. R. Shaw, C. E. Watson, and K. N. Reddy. 2003. Detecting late-season weed
infestations in soybean (Glycine max). Weed Technology 17:696-704.
Koger, C. H., L. M. Bruce, D. R. Shaw, and K. N. Reddy. 2003. Wavelet analysis of hyperspectral
reflectance data for detecting pitted morningglory (Ipomoea lacunosa) in soybean (Glycine max). Remote
Sensing of Environment 86:108-119.
Leon, C. T. Leon, D. R. Shaw, L. M. Bruce, and C. Watson. 2003. Effect of purple (Cyperus
rotundus) and yellow nutsedge (C. esculentus) on the growth and reflectance characteristics of cotton
and soybean. Weed Science 51:557-564.
Leon, C. T., D. R. Shaw, M. S. Cox, C. Watson, M. J. Abshire, B. Ward, and M. C. Wardlaw, III.
2003. Utility of remote sensing in predicting crop and soil characteristics. Precision Agriculture 4:359384.
Medlin, C. R., and D. R. Shaw. 2000. Economic comparison of broadcast and site-specific herbicide
applications in nontransgenic and glyphosate-tolerant soybean (Glycine max). Weed Science 48:653670.
Medlin, C. R., D. R. Shaw, M. S. Cox, P. D. Gerard, M. J. Abshire, and M. C. Wardlaw. 2001. Using
soil parameters to predict weed infestations in soybean (Glycine max). Weed Science 49:367-374.
Medlin, C. R., D. R. Shaw, P. D. Gerard, and F. E. LaMastus. 2000. Using remote sensing to detect
weed infestations in soybean (Glycine max). Weed Science 48:393-398.
Rankins, A., Jr., D. R. Shaw, and M. Boyette. 2001. Perennial grass filter strips for reducing
herbicide losses in runoff. Weed Science 49:647-651.
Saputro, S., D. B. Smith, and D. R. Shaw. 1991. Expert system for agricultural aerial spray drift.
Transactions, American Society of Agricutural Engineering 34:764-772.
Schraer, S. M., D. R. Shaw, M. Boyette, R. H. Coupe, and E. M. Thurman. 2000. Comparison of
enzyme-linked immunosorbent assay and gas chromatography procedures for the detection of cyanazine
and metolachlor in surface water samples. Journal of Agriculture & Food Chemistry 48:5881-5886.
Shaw, D.R., and C.L. Hill. 2000. Precision agriculture and remote sensing. Space 2000, American
Civil Engineering Society 32:455-460.
Tingle, C. H., and D. R. Shaw. 1998. Metolachlor and metribuzin losses in runoff as affected by
width of vegetative filter strips. Weed Science 46:475-479.
Webster, E. P., and D. R. Shaw. 1996. Impact of vegetative grass filter strips on herbicide loss in
runoff from soybean (Glycine max). Weed Science 44:662-671.
DAVID L. TOLL
Hydrological Sciences Branch, Code 614.3
NASA Goddard Space Flight Center
Greenbelt, MD 20771
CURRENT RESPONSIBILITIES
David Toll is involved in the research, management and application of hydrology, microclimatology, and
environmental science. Specific areas of interest include application and improvement of the modeling
and remote sensing of land surface water and energy fluxes and the remote sensing of land surface
biophysical parameters. Currently leads the Water Management activities at NASA/GSFC with the goal
to use NASA Earth science data to assist user groups, primarily other federal agencies, with water
resources applications.
EDUCATION:
1976 B.S. Natural Resources, Colorado State University, Ft. Collins, CO
1978 M.S. Earth Resources (Major - Remote Sensing), CSU, Ft. Collins, CO
1991 M.S. Dep’t Geography (Major - Microclimatology), Univ. Maryland, College Park, MD
PROFESSIONAL POSITIONS:
1978-1988
Earth Resources Branch, NASA/GSFC
1988-Present Hydrological Sciences Branch, NASA/GSFC
RECENT MAJOR RESEARCH GROUP PROJECTS:
1990-2004
MODIS Technical Team
1998-Present Land Information Systems
2002-Present NASA/GSFC Water Management Lead Project Scientist
2004-Present PI NOAA/GAPP Surface Water Project
ACTIVITIES
American Geophysical Union
Affiliate Member IEEE Geoscience and Remote Sensing
RECENT AWARDS
1999 NASA/GSFC Laboratory for Hydrospheric Processes Peer Award Outstanding Technical
2000 NASA/GSFC Special Act Award of Superior Performance
2001 NASA/GSFC MODIS Support Team Group Achievement Award
2002 NASA/GSFC Performance Award for Exceptional Service of Official Duties
2003 NASA/GSFC Performance Award for Exceptional Service of Official Duties
2004 NASA/GSFC Performance Award for Exceptional Service of Official Duties
2005 NASA/GSFC Performance Award for Exceptional Service of Official Duties
SELECTED JOURNAL PUBLICATIONS & BOOKS:
Jensen, J., and D. L. Toll "Detecting Residential Land Use Development at the Urban Fringe of Denver,
Colorado," Photogramm. Eng. and Remote Sensing, 48, (4), pp. 629-643, 1981.
Jensen, J., D. Toll and others, "Urban/Suburban Land Use Classification," in Manual of Remote Sensing,
2nd edition, R. Colwell, D. Simonett and J. Estes, eds., American Society of Photogrammetry, Falls
Church, VA, 1983.
Toll, D.L., "An Evaluation of Simulated Thematic Mapper and Landsat MSS Data for Discriminating
Urban Area Land Cover," Photogrammetric Engineering and Remote Sensing, 50, (12), pp. 17131724, 1984.
Williams, D., J. Irons, B. Markham, R. Nelson, D. Toll, R. Latty and M. Stauffer, "A Statistical
Evaluation of the Advantages of Landsat TM Data in Comparison to Multispectral Scanner Data,"
IEEE Transactions on Geoscience and Remote Sensing, GE-22, (3), pp. 294-302, 1984.
Irons, J., B. Markham, R. Nelson, D. Toll, D. Williams, R. Latty and M. Stauffer, "The Effects of Spatial
Resolution on the Classification of TM Data," International Journal of Remote Sensing, 6, pp. 13851403, 1984.
Toll, D., "Effect of Landsat Thematic Mapper Sensor Parameters on Land Cover Classification," Remote
Sensing of Environment, 17, pp. 129-140, 1985.
Toll, D., "Analysis of Digital Landsat MSS and Seasat SAR Data for Use in Discriminating Land Cover
of the Urban Fringe of Denver, Colorado," International Journal of Remote Sensing, 6 (7), pp. 12091229, 1985.
Toll, D., "Landsat-4 Thematic Mapper Scene Characteristics of a Suburban and Rural Area,"
Photogrammetric Engineering and Remote Sensing, 51 (9), pp. 147l-1482, 1985.
Asrar, G., R.E. Murphy and D.L. Toll, "International Satellite Land Surface Climatology Project: The
Retrospective Analysis Program (IRAP)", Agricultural and Forest Meteorology, 52, pp. 1-4, 1990.
Vukovich, F. M., D.L. Toll, and R. Wayland “Surface heat flux as a function of ground cover for climate
models”, Mon. Wea. Rev., 125(4), 572-586, 1996.
Toll, D. L., D. Shirey and D. Kimes, Land surface albedo algorithm development for NOAA AVHRR
Satellite Radiance Data, Int. J. Remote Sensing. 18(18), 3761-3796, 1997.
Rodell, M., P.R. Houser, U. Jambor, J. Gottschalck, C.-J. Meng, K. Arsenault, B. Cosgrove J.
Radakovich, M. Bosilovich, J.K. Entin, J. Walker, D. Toll, and K. Mitchell, . “The Global Land Data
Assimilation System (GLDAS)”, Bulletin to American Meteorological Society, 85(3), 381-394, 2004.
D. Toll and P. Houser. Assimilation of Land Data for the Study of Land Water and Energy Budgets,
2004, in Spatial Modeling of the Terrestrial Environment, Ch. 12, 245-263, R. Kelly, N. Drake, and
S. Barr eds., John Wiley and Sons.
de Goncalves, G.G., W. Shuttleworth, D. Toll, Y. Xue, and C. S. Chan, 2005, Impact of differential
initial soil moisture fields on ETA Model Weather, J. Geophys. Res., submitted, in review.
RECENT CONFERENCE AND TECHNICAL PAPERS
D. Toll, P. Houser, B. Cosgrove, and K. Arsenault. Land data assimilation system water and energy
fluxes, SPIE 2003, Barcelona Spain, Technical Paper, pp1-8, 2003.
D. Toll, K. Arsenault, P. Houser, J. Entin, A. Pinheiro and J. Triggs, NASA Remote Sensing Tools for
Water Management, IGARSS 2004, Technical Presentation, Anchorage, AK, 2004.
D. Toll, P. Houser, K. Arsenault, J. Triggs, J. Entin and A. Pinheiro, NASA water resources data for
natural resources applications, 10th Biennial Remote Sensing Application Conference, Salt Lake City,
UT, Poster Presentation, 2004.
D. Toll, P. Houser, K. Arsenault, J. Entin, J. Triggs, and A. Pinheiro, NASA data for water resources
decision support tools, European Geophysical Society, Technical Presentation, Nice France, 2004.
A. Pinheiro, K. Arsenault, P. Houser, D. Toll and S. Kumar, Improved evapotranspiration estimates to aid
water management practices in the Rio Grande River Basin, IGARSS 2004, Anchorage, Alaska,
Technical Paper, 4pp.
D. Toll, J. Triggs, J. Nigro, K. Arsenault, E. Engman and A. Pinheiro, 2005. Improving Water
Management Decision Support Systems Using NASA Data Products, NASA-USDA 2nd Applications
Workshop, New Orleans, Poster Presentation.
D. Toll, K. Arsenault, A. Pinheiro, P. Houser, C. Peters-Lidard, and S. Kumar, 2005. Using the NASA
Land Information System to Integrate Earth Science Data for Water Resources Applications,
European Geophysical Union, General Assembly 2005 (April), Vienna, Austria.
Igor Zhuk
Research Associate
Center for Advanced Vehicular Systems
Mississippi State University
Box 5405, Mississippi State, MS 39762-5405
Phone: 662-325-5584 Fax: 662-325-5433
igorzhuk@erc.msstate.edu
Education
Ivanovo State University, Physics Department, Ivanovo, Russia, 1977-1982.
Employment
Research Associate, Center for Advanced Vehicular Systems, Engineering Research Center, Mississippi
State University, 2004-present
Programmer, SB, Krasnodar, Russia, 2003-2004
Programmer, Ingenio BV, Haarlem, Holland, 2001-2002
Programmer, KrasnodarElectro, Krasnodar, Russia, 2000-2001
Programmer, Bee-Line, Krasnodar, Russia, 1999-2000
Programmer, Medical Computer Center, Krasnodar, Russia, 1998-1999
Programmer, InfoSysTech, Krasnodar, Russia, 1990-1992
System Programmer, Regional Computer Center, Krasnodar, Russia, 1987-1990
System Programmer, Oil Research Institute, Krasnodar, Russia, 1984-1987
Synergistic Activities – Awards
N/A
Synergistic Activities – Professional Affiliations and Activities
N/A
Selected Publications
N/A
Appendix A: Capabilities of the Stennis Team
Overview
The Stennis Team, comprised of the Institute for Technology Development (ITD) and
Science Systems and Applications Inc (SSAI), possess significant technical expertise and has
developed tools that will be extremely useful for the RPC. Their capabilities are described in
this appendix.
Atmospheric Correction Tool (ACT)
Remotely sensed ground reflectance and temperature are the basis for many inter-sensor
interoperability, change detection techniques, and higher level product generation. Most MODIS
and proposed VIIRS products are based on reflectance or temperature. Satellite intercomparisons and accurate vegetation indices, such as the Normalized Difference Vegetation
Index (NDVI), which is used to describe or to imply a wide variety of biophysical parameters
and is defined in terms of near-infrared and red-band reflectance, require the generation of
accurate reflectance maps. This generation relies upon the removal of solar illumination, satellite
geometry, and atmospheric effects, which is generally referred to as atmospheric correction.
Evaporation rates and other derived thermal properties rely upon accounting for both
transmission and emission effects to estimate surface temperature. Atmospheric correction of
remotely sensed imagery to ground reflectance and temperature, however, has been widely
applied only to a few systems. A set of tools has been established that rely on satellite-derived
atmospheric components, dark pixel algorithms, or empirical methods. Some of this work is
described in Pagnutti et al., 2005. The Atmospheric Correction Tool (ACT) is based on a
MODTRAN radiative transfer engine used directly for a thermal or a modified spherical albedo
approach for the reflective region—very analogous to the GSFC 6S radiative transfer approach.
Satellite-derived atmospheric constituents are provided by MODIS and TOMS products. This
tool is typically used to prepare datasets for use in the Application Research Toolbox and the
Remote Sensing Time Series Product Tool Development Tool. The current set of tools has been
validated with ground truth data.
Application Research Toolbox Description
The ART is an integrated set of algorithms and models developed in MATLAB® that allows
users to perform a suite of simulations and statistical trade studies on remote sensing systems.
Specifically, the ART provides the capability to generate simulated multispectral image products
at various scales from high spatial hyperspectral and or multispectral image products. The ART
utilizes acquired (“real”) or synthetic datasets, along with sensor specifications, to create
simulated datasets. The datasets can either be radiance or reflectance data. For existing,
multispectral sensor systems, the simulated data products are used for comparison, verification,
and validation of the simulated system’s actual products. For proposed sensor systems, the
simulated products can be used to conduct various trade studies and statistical analyses to ensure
that the sensor system will meet the scientific, academic, and commercial communities’
requirements. The ART simulations have been validated through comparisons with Landsat,
Hyperion, the Advance Land Imager (ALI), and high spatial resolution airborne imaging
systems. Visual and quantitative assessments show excellent agreement.
The process of creating a simulated multispectral image from a hyperspectral image, Figure
1, begins with the application of spectral band synthesis. Spectrally degraded, band-to-band
misregistration artifacts can be added to simulate sensor artifacts introduced during data
collection. Next, the spatial degradation or spatial synthesis algorithm is applied to convert the
hyperspectral image to the ground sample distance (GSD) and point spread function (PSF) of the
targeted sensor. Noise is added to the simulated image by applying a physics based noise model
that accounts for detector and quantum noise as well as systematic artifacts. The resultant image
is then quantized using an appropriated digitization model to the desired radiometric precision.
Spatial simulation is the emulation of a multispectral wide band sensor with a low resolution
GSD using data from a high resolution GSD sensor. Spatial simulation processing converts the
input image’s GSD to that of the targeted sensor as well as an estimate for the PSF. The ART
provides two methods of spatial simulation: PSF synthesis and high speed spatial degradation
based on aggregation and resampling.
Figure 1 shows an example of a comparison of Color Infrared (CIR) Landsat and Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS) input for an ART simulation. Although the
images are very similar, differences are primarily due to differences in time of acquisition. The
AVIRIS dataset was 3.2 m GSD and degraded to 28.8 m GSD using PSF synthesis.
Figure 1. Simulated Landsat 7 scene vs. actual Landsat 7 scene of Shelton, NE.
The images in Figure 2 illustrate a simulated pushbroom system with full PSF synthesis
derived from a Landsat-like instrument NDVI product of Manhattan with a 10% detector nonuniformity noise before and after non-uniformity correction. The striping on the left is a
manifestation of the detector non-uniformity, while the image on the right illustrates an
optimized correction algorithm. Similar methods will be applied to VIRRS and other system
emulations.
(A) NDVI 10% Non-Uniformity Uncorrected, 30 m GSD
(B) NDVI 10% Non-Uniformity Corrected, 30 m GSD
Figure 2. Effects of uncorrected and corrected non-uniformity on simulated 30 m NDVI
products.
Time Series Product Tool Description
Tools can be used to create time series products for data simulations from future earth-Sun
System Sensors such as VIIRS. A Time Series Product Tool (TSPT) has been created to display
various Moderate Resolution Imaging Spectroradiometer (MODIS) products, time series, and
image videos. The TSPT can display the spatial ranges either as rows and columns or as latitudes
and longitudes.
Once a scientific dataset and desired band is selected from a MODIS HDF file, spatial and
temporal ranges of interest can be entered for image display, time series plots, image videos, and
quality assurance (QA) reports. QA reports created using the HDF metadata include statistics on
bad pixels, on pixels adversely affected by clouds, and on pixels flagged as less than ideal.
The selected dataset can be displayed directly. For example, the daily MOD09 Surface
Reflectance dataset is typically displayed as a Normalized Difference Vegetation Index (NDVI)
calculated from the near infrared and the red surface reflectances. If the directory also contains
16-day composite MOD13 NDVI HDF files, the MOD13 NDVI can be overlaid onto the
MOD09 NDVI plots.
Geographic information system (GIS) shape file information, such as roads, state boundaries,
and crop field boundaries, can be included in the image displays. If the dataset selected as a
sinusoid projection, it can be reproject the dataset to a geographic projection. Using the TSPT
can also create subsets using the selected spatial ranges of interest.
Noise reduction techniques are available for the elimination of undesirable pixels, such as
those flagged as bad or as less than ideal in the metadata and those acquired at large sensor
zenith angles. Unique data fusion of multiple satellite systems such as Aqua and Terra can be
used to reduce cloudy pixels. Also, time series plots can be created for a selected latitude and
longitude and a date range. These time series can be interpolated to replace eliminated pixels and
can filter them using either a median or a Savitzky-Golay filter. Resulting time series have
shown to be significantly better than standard MODIS global composited products and could be
the basis of new VIIRS products.
Image videos can be created for selected date ranges. The output JPEG images can then be
combined to run as a video using separate software. The temporal noise reduction techniques
used in time series plots can also be used in creating the image videos.
Although the TSPT was developed for vegetation indices, this tool would also be useful for
rapid prototyping of other products. By expanding the functionality of the TSPT to other sensor
data, early analysis of time series products could be enhanced.
References
Blonski, Slawomir, Changyong Cao, Jerry Gasser, Robert Ryan, Vicki Zanoni, and Tom Stanley,
1999. Satellite hyperspectral imaging simulation. Proceedings of ISSSR ’99: Systems and
Sensors for the New Millennium, October 31–November 4, Las Vegas, NV, CD-ROM.
Blonski, Slawomir, Gerald Gasser, Jeffrey Russell, Robert Ryan, Greg Terrie, and Vicki Zanoni,
2002. Synthesis of multispectral bands from hyperspectral data: Validations based on images
acquired by AVIRIS, Hyperion, ALI, and ETM+. 2002 AVIRIS Earth Science and Applications
Workshop, March 5–8, Pasadena, CA (NASA Jet Propulsion Laboratory, California Institute of
Technology), http://popo.jpl.nasa.gov/docs/workshops/02_docs/2002_Blonski_web.pdf
Cao, Changyong, Slawomir Blonski, Robert Ryan, Jerry Gasser, and Vicki Zanoni, 1999.
Synthetic scene generation of the Stennis V&V target range for the calibration of remote sensing
systems. Proceedings of ISSSR ’99: Systems and Sensors for the New Millennium, October 31–
November 4, Las Vegas, NV, CD-ROM.
McKellip, Rodney, Robert E. Ryan, Don Prados, and Slawomir Blonski, 2005. Crop surveillance
demonstration using a near-daily MODIS vegetation index time series. In 2005 International
Workshop on the Analysis of Multi-Temporal Remote Sensing Images. New York City: IEEE.
Pagnutti, Mary, Kara Holekamp, Robert E. Ryan, Ronald Vaughan, Jeff Russell, Don Prados,
and Thomas Stanley, 2005. Atmospheric correction of high-spatial-resolution commercial
satellite imagery products using MODIS atmospheric products. In 2005 International Workshop
on the Analysis of Multi-Temporal Remote Sensing Images. New York City: IEEE.
Warner, Amanda, Slawomir Blonski, Bruce Davis, Gerald Gasser, Robert Ryan, and Vicki
Zanoni, 2001. An approach to application validation of multispectral sensors using AVIRIS.
2001 AVIRIS Earth Science and Applications Workshop, February 27–March 2, Pasadena, CA
(NASA Jet Propulsion Laboratory, California Institute of Technology),
http://popo.jpl.nasa.gov/docs/workshops/01_docs/2001Warner_web.pdf
Appendix B: Systems Engineering Documentation Standards
Task
Develop a
Capabilities
Document
Deliverable
Capabilities Document
Develop a
Preliminary Design
Concept of Operations
Document
Develop a
Preliminary Design
System Requirements
Specification
Develop
Implementation Plan
Implementation Plan
IEEE Standard
IEEE Std 1362-1998 IEEE Guide for
Information Technology —System
Definition — Concept of Operations
(ConOps) Document
IEEE Std 1362-1998 IEEE Guide for
Information Technology —System
Definition — Concept of Operations
(ConOps) Document
IEEE Std 1233-1998 IEEE Guide for
Developing System Requirements
Specifications
(not applicable)
Develop
Implementation Plan
Software Requirements
Specification
(for new software only)
IEEE Std 830-1998 IEEE
Recommended Practice for Software
Requirements Specifications
Develop System
Design Document
System Design Document
Implementation of
Rapid Prototyping
Capability Node
Final Software Design
Description
(for new software only)
IEEE Std 1471-2000 IEEE
Recommended Practice for Architectural
Description of Software-Intensive
Systems
IEEE Std 1016-1998 IEEE
Recommended Practice for Software
Design Descriptions
Implementation of
Rapid Prototyping
Capability Node
Testing Rapid
Prototyping
Capability Node
Documentation
Increment Acceptance
Test Plans, Test Cases,
and Test Procedures
Evaluation Reports
Status reports and
presentations
(All of above documents
are also included in
Documentation task)
IEEE Std 829-1998 IEEE Standard for
Software Test Documentation
(not applicable)
(not applicable)